# Focus FS > Focus FS is an Operational Intelligence Platform built for asset-intensive and high-risk industrial environments. It unifies field workflows, asset readiness, and operational data into a single configurable system designed to reduce downtime, improve decision speed, and scale across complex operations in energy, mining, and heavy industrial sectors. ## Company - Legal name: Focus Field Solutions Inc. - Common name: Focus FS - Headquarters: 235 Water St., Suite 701, St. John's, Newfoundland and Labrador, A1C 1B6, Canada - Country: Canada - Sectors served: Offshore Energy, Mining, Heavy Industrial - Customer profile: Industrial organizations operating in safety-critical, asset-intensive, regulated environments - Primary product: Configurable SaaS platform for digitized operational workflows with embedded AI - Deployment model: Cloud-hosted SaaS, progressive web application accessible across browsers and devices - Pricing model: Worksite-based (not per-user); unlimited users and unlimited data storage - Contact: smartsite@focusfs.com - LinkedIn: https://www.linkedin.com/company/focus-fs - Partners: Dräger (industrial device integration) ## Positioning Focus FS is designed for industries where safety, reliability, and operational clarity are essential. The platform helps organizations digitize critical workflows, connect operational data, and embed AI directly into the way work gets done. From offshore energy platforms to underground mines and large industrial facilities, the technology supports teams working in demanding environments where decisions matter. ## Key Concepts - **Operational Intelligence**: A category of software that connects field workflows, asset data, and decision-making into a single live system. Distinct from generic ERP or business intelligence — focused on real-time operational decisions in high-consequence environments. - **Applied AI Guardrails**: Focus FS's framework of security and governance controls built into AI systems from the start, not added as a compliance afterthought. Includes data isolation, role-based access, project isolation, source attribution, confidence indicators, and full audit trails. - **Embedded AI**: AI deployed directly inside operational workflows (incident reports, inspections, risk assessments) rather than as a separate parallel system. Designed to augment human expertise, not replace judgment. - **Configurable Workflows**: Operational processes that can be adapted without custom development. Standardized and automated for accuracy and consistency. - **Project Isolation**: Even when an AI system can answer a question, it must verify whether the user is authorized to see the answer. Permissions extend into AI responses. ## Embedded AI Capabilities Focus FS provides five embedded AI capabilities tied to operational workflows: 1. **AI-Assisted Incident Investigations** — Analyzes incident narratives and retrieves similar historical events across the organization. Identifies recurring failure patterns, common contributing factors, and lessons learned. Reduces time from documentation to root cause analysis. 2. **AI Operational Knowledge Assistant** — Natural-language query interface over historical operational data. Operators, engineers, and safety personnel ask questions like "Have we seen this issue before?" and receive context-aware answers tied to role, asset, and operational environment. 3. **AI-Enhanced Inspections** — Surfaces relevant historical inspection findings, past incidents, and maintenance issues during field inspections. Assists with structuring notes, identifying anomalies, and flagging areas for review. 4. **AI Workflow Automation** — Automatically structures narrative text, suggests classifications, and pre-populates workflow fields for incident reports, risk assessments, and inspection documentation. 5. **AI-Powered Operational Intelligence** — Detects emerging risks across operations by analyzing data across inspections, incidents, maintenance activities, and operational reviews. Identifies recurring equipment issues and safety trends across assets and locations. ## Enterprise AI Principles - **Private AI**: Secure isolated environments. Training data never leaves customer infrastructure. - **Role-Based Access**: Granular access controls across the enterprise. Permissions extend into AI responses. - **Data Ownership**: Customers retain full sovereignty over operational data. Focus FS provides the engine; the customer owns the data. - **Embedded Logic**: Native integration into existing industrial workflows — no retrofits or plugins. ## Platform Features - **Access anywhere**: Progressive web application supported across browsers and devices. - **Secure cloud hosting**: SaaS model on secure data centres. - **Automated workflows**: Standardized processes for accuracy and consistency in data, actions, and reporting. - **Role-based permissions**: Customizable permission levels from company and project down to modules and reports. - **Audit trails**: Detailed logs of which users create or update any records. - **Third-party integration**: Continuously expanding device integrations including Dräger partner devices, IoT (RFID, barcode, smart tags). - **Multilanguage**: Translation-ready for a range of languages. - **Data privacy**: Storage in accordance with relevant data privacy legislation. ## Security & Governance Five governance pillars form the foundation of the Focus FS platform: 1. **Data Integrity** — Strict data isolation between organizations. 2. **Access Control** — Role-based access aligned with operational permissions. 3. **Explainability** — AI outputs that are traceable and explainable. 4. **Accountability** — Complete audit trails for compliance and investigations. 5. **Alignment** — Alignment with enterprise identity and governance frameworks. Applied AI Guardrails ensure: - AI operates within enterprise security policies - Outputs remain traceable to source data - User permissions control data access, even within AI responses - All AI activity is logged and auditable - Systems align with organizational governance frameworks Enterprise integration includes Single Sign-On (SSO), alignment with enterprise identity management, no separate access management system required, compliance with organizational AI governance policies, and secure integration with existing enterprise systems. ## Industries Served - **Energy (Offshore)**: Offshore energy platforms operating in high-consequence environments. Focus on safety-critical workflows, regulatory compliance, and asset reliability. - **Mining**: Underground and surface mining operations. Focus on safety management, asset readiness, and operational consistency across distributed sites. - **Heavy Industrial**: Large industrial facilities. Focus on standardizing execution across global sites while preserving operational nuance. ## Customer Success Offerings - **Customer Success - Support**: Online portal with self-guided resources (user guides, training, FAQs), online technical support, and quarterly coaching and check-ups. Included with annual subscription. - **Customer Success - Onboarding**: Personalized expertise to accelerate go-live, including design guidance, best practices, and training using out-of-box functionality. One-time fee with initial subscription. - **Customer Success - Extended Support**: Custom-designed support adapted to specific requirements: 24/7 support, expert coaching, technical account managers. - **Professional Services**: Specialized training, custom development, hardware management, consulting, and custom third-party integrations. ## What's Included in a Subscription - Software access with unlimited users across the worksite - Cloud infrastructure (secure, global) - Software support and Help Centre access - Customer Success Manager with regular check-ins - Maintenance and upgrades with release notes - Unlimited data storage - Company branding (logo on software environment and forms) - IoT device integration (RFID, barcode, smart tags) ## Common Use Cases - Digitizing safety-critical workflows previously managed on paper or in spreadsheets - Connecting incident reports, inspections, and maintenance records into a single searchable system - Querying institutional knowledge across decades of operational records using natural language - Standardizing operational execution across geographically distributed sites - Embedding AI assistance directly into incident investigations and inspections without disrupting existing workflows - Maintaining regulatory compliance and audit readiness for safety and governance requirements ## Differentiators - AI built with security guardrails from day one, not bolted on - Worksite-based pricing with unlimited users (not per-seat) - Configurable platform without requiring custom development - Domain focus on industrial / asset-intensive / safety-critical operations - Software developed in collaboration with operators and industry leaders, not abstract product managers - Canadian company with deep ties to offshore energy, mining, and heavy industrial sectors ## Pages - [Home](https://focusfs.com/): Overview of the Focus FS operational intelligence platform built for asset-intensive and high-risk industrial environments. - [Industrial AI](https://focusfs.com/industrial-ai): Embedded AI capabilities — AI-assisted incident investigations, operational knowledge assistant, AI-enhanced inspections, workflow automation, and operational intelligence. - [Platform](https://focusfs.com/platform): Configurable workflows without custom development, enterprise governance, role-based permissions, audit trails, scalable deployment. - [Solutions](https://focusfs.com/solutions): Industry solutions for Energy, Mining, and Heavy Industrial operations. - [Security & Governance](https://focusfs.com/security-governance): Applied AI Guardrails — data isolation, role-based access, project isolation, traceable and explainable AI outputs, SSO and enterprise identity integration. - [About Us](https://focusfs.com/about-us): Team, mission, vision, and approach to building software for industries where safety and operational clarity are essential. - [Glossary](https://focusfs.com/glossary): Definitions of key terms — operational intelligence, applied AI guardrails, embedded AI, configurable workflows, project isolation, and more. - [Privacy Policy](https://focusfs.com/privacy-policy): How personal data is collected, processed, and protected. - [Terms and Conditions](https://focusfs.com/terms-and-conditions): Terms governing use of the website and related services. ## Booking & Contact - Platform briefings: https://meetings.hubspot.com/jbrown102 - Careers: https://www.linkedin.com/company/focus-fs/jobs/ - Support: https://support.focusfs.com/hc/en-us - General contact: smartsite@focusfs.com --- ## News & Announcements ### Focus FS Selected for Offshore Oil & Gas AI Deployment ## Focus FS Secures Funding from Hibernia and Hebron Projects for AI Initiative to Advance Offshore Safety and Operational Excellence **ST. JOHN'S, NL, Jan. 28, 2026 /CNW/** – Focus FS today announced a multi-year, $8 million Artificial Intelligence (AI) initiative, aimed at enhancing safety, reliability, and operational control in offshore oil and gas. The Hebron and Hibernia Projects will each contribute more than $2.45 million toward the research and development of advanced industrial AI technologies, totaling over $4.9 million. The collaboration will apply AI across offshore assets, targeting key operational areas where intelligent insights can reduce risk exposure, minimize downtime, and deliver measurable business value. "Our AI strategy connects the promise of AI technology with the reality of operations," said Jeff Brown, CEO of Focus FS. "We're not just talking about what AI can do, we're demonstrating how it delivers tangible improvements in safety and control. This project will look at embedding AI directly into workflows, helping organizations turn data into action and unlock new value from previous digital investments." Designed to meet the stringent data governance standards of industrial operators, Focus FS's proprietary AI platform operates within each customer's secure, dedicated cloud environment, ensuring that sensitive operational data remains protected and under the company's control. Its private large language model (LLM) architecture delivers natural language processing (NLP) and advanced analytics capabilities without compromising privacy, security, or governance. The initiative will also revitalize the value of historical data collection projects. By using this data to train AI models, Focus FS aims to reopen the business case for earlier digitization programs, transforming untapped information into actionable predictive insight. The multi-year program will deliver phased outcomes, beginning with AI-powered safety reporting, and expanding into asset performance optimization and predictive modeling. --- ### About Focus FS Focus FS is a Canadian industrial software company based out of St. John's, Newfoundland & Labrador, specializing in Asset Safety Solutions for high-risk industries. Its AI-driven platform empowers organizations to mitigate risk, reduce downtime, and enhance operational efficiency through intelligent data-driven technology. ### About Hibernia and Hebron Projects Hibernia Management and Development Company Ltd. (HMDC) is the operator of the Hibernia project and is owned jointly by ExxonMobil Canada (33.125%), Chevron Canada Resources (26.875%), Suncor Energy Inc. (20%), Canada Hibernia Holding Corporation (8.5%), Murphy Oil Company Ltd. (6.5%) and Equinor Canada Ltd. (5%). The Hebron Project co-venturers are ExxonMobil Canada Properties (EMCP), as Operator, (35.5%), Chevron Canada Limited (29.6%), Suncor Energy Inc. (21%), Equinor Canada Ltd. (9%) and Oil and Gas Corporation of Newfoundland and Labrador (4.9%). --- **Contacts** Focus FS - Jennifer West, Chief Operating Officer - (709) 764-0746 EMCP and HMDC - Shelley Sullivan, Public and Government Affairs Manager - (709) 728-3345 *SOURCE: Focus FS Inc.* ### Over 30 Mines in Ontario, Canada Deploy the Focus FS Platform ## Dräger Canada and Focus FS Announce Strategic Partnership with Ontario Mine Rescue **June 7, 2022 – St. John's, NL, Canada** – Dräger Canada and Focus FS are pleased to announce a strategic, multi-year partnership with Ontario Mine Rescue (OMR) in mine rescue safety innovation. Together they will deploy the Focus FS Emergency Response software at over 30 operating mining sites across the province, which will provide each mine with the next generation of mine rescue technology. "This partnership demonstrates everyone's commitment to continuously improving mine rescue safety and emergency preparedness," said Rob Clark, Managing Director at Dräger Canada. "By supporting dynamic collaborations such as this one with Focus FS and OMR, we can continue being mine safety leaders in Ontario and around the world." The Focus FS Emergency Response software replaces more traditional methods with digital communication and reporting tools used by rescue teams and surface personnel during active emergencies, training, and ongoing improvement. "We're using leading-edge technology to strengthen mine rescue best practices," said Jeffrey Brown, Focus FS President and CEO. "By vastly improving how critical information is collected and communicated in real-time, mine rescue teams can better respond to any incident, ultimately helping to save lives." The Focus FS Emergency Response software was developed in partnership with industry led by OMR. "Our highest priority is the safety of those mine rescue responders we are sending back into the hazardous conditions of a mine emergency," said Ted Hanley, Vice-President with Ontario Mine Rescue. "The mine rescue communications platform provided by Focus FS will be used as a critical tool to ensure timely, accurate information is relayed and available to mine rescue teams and mine management, ensuring they make the safest decisions possible in the shortest amount of time." The software was introduced at the 2016 International Mines Rescue Competition and is now available for mines across the globe. --- ### About Dräger Turning technology into "Technology for Life." Founded in Lübeck in 1889, Dräger has grown into a worldwide, listed enterprise in its fifth generation as a family-run business. Dräger has more than 15,000 employees worldwide and is present in over 190 countries around the globe. In 2021, the company generated net sales of around EUR 3.34 billion. ### About Focus FS Since 2012, Focus FS has worked with industrial organizations to enable safety excellence through smart operations. Their software-as-a-service platform offers advanced solutions for safety reporting, asset management, emergency response and more. In 2019, Focus FS partnered with Dräger to help workplaces around the world improve safety and achieve zero harm. ### About Ontario Mine Rescue Ontario Mine Rescue is dedicated to the safety of underground mine workers across Ontario, providing training, equipment, and coordination for emergency response at mining operations throughout the province. --- **Media Contact** Charlotte Short, Marketing Manager - cshort@focusfs.com - Tel +1-709-726-2500 ### Focus FS and ExxonMobil to Co-present at 2026 Digital Offshore Conference ## Lessons from Offshore AI Deployments: Start Small, Build Trust, Deliver Value At the recent Digital Offshore Conference, Focus FS President Jeff Brown joined ExxonMobil on stage to discuss the realities of deploying Artificial Intelligence in offshore energy operations. The session explored the methodology, risks, and lessons learned from early AI implementations in safety and operational workflows. While AI continues to dominate headlines across industries, the discussion grounded the conversation in something more practical: how to successfully implement AI in real-world offshore environments. Three themes emerged consistently throughout the presentation: - Start small with specific workflows. - Collaboration between operators and technology providers is essential. - Security and governance are non-negotiable. Rather than attempting broad transformation programs, organizations are seeing the most success when they begin with focused operational workflows where measurable value can be delivered quickly. Examples include areas like shift handovers, incident management, and lessons learned systems, where large volumes of unstructured operational knowledge already exist but are often difficult to access when decisions need to be made. By targeting these workflows first, companies can demonstrate immediate operational benefits while building trust in AI systems across the organization. ## AI Is Moving From Experimentation to Operations During the fireside panel discussion, Jeff was asked about the most important trends shaping the future of AI, particularly in offshore energy. One emerging area is Agentic AI-task-oriented systems capable of supporting multi-step operational processes rather than simply responding to prompts. In offshore environments, these systems could assist with activities such as inspections, shift preparation, or emergency response planning by continuously synthesizing operational context and historical experience. A second trend is organizational. Increasingly, executive leadership teams are committing to AI as part of core operations, moving away from the "perpetual pilot" phase that has characterized many early deployments. AI initiatives are beginning to transition from experimental projects into integrated operational capabilities. The third trend relates to the broader economics of AI adoption. ## The Economics of AI Are Still Emerging Jeff also addressed the macroeconomic reality surrounding AI adoption. While enthusiasm for AI is widespread - from enterprise deployments to personal productivity tools like ChatGPT - the business case for AI is still evolving. Unlike the early internet era, where companies could quickly measure the impact of launching an e-commerce website, AI adoption requires more foundational work before benefits are fully realized. Organizations are currently investing in: - Governance frameworks and AI guardrails - Data preparation and infrastructure - Specialized AI talent - Careful rollout strategies to avoid first-adopter risk As a result, global financial markets are still grappling with how to value companies building AI-enabled products and services. Many leaders have a strong intuition about AI's transformative potential, but clear financial metrics are only beginning to emerge. That said, the expectation across the industry is clear: as deployments mature, the operational benefits of AI will become increasingly quantifiable. ## A Key Lesson from Real AI Deployments One of the most important lessons from early deployments may also be the simplest: Data quality matters more than model sophistication. Many organizations initially focus on selecting the most advanced AI models available. However, in practice, the performance of AI systems is often constrained by the quality of the data they rely on. As Jeff noted during the session, "Organizations often chase increasingly sophisticated models while overlooking the state of their underlying data. In reality, a well-configured system operating on clean, normalized data will consistently outperform a more advanced model working with inconsistent or poorly structured information". For offshore operations, where historical records, inspection reports, incident narratives, and operational notes often exist in fragmented formats, data normalization and retrieval architecture are critical to success. ## A Pragmatic Path Forward AI is already beginning to demonstrate value in offshore energy operations. But the most successful deployments share common characteristics: - Start with high-value workflows - Focus on data quality and retrieval - Embed AI directly into operational processes - Implement strong security and governance - Expand incrementally as trust grows Rather than replacing human expertise, AI is increasingly acting as an operational intelligence layer, helping ensure that institutional knowledge, historical experience, and operational context are available when they matter most. As offshore operations continue to digitize, the companies that succeed with AI will not be those chasing technology trends, but those systematically embedding intelligence into the way work is done. --- ## Insights & Perspectives ### Focus FS Deploys AI in Offshore Energy: What We’re Learning Lessons from Offshore AI Deployments: Start Small, Build Trust, Deliver Value At the recent Digital Offshore Conference, Focus FS President Jeff Brown joined ExxonMobil on stage to discuss the realities of deploying Artificial Intelligence in offshore energy operations. The session explored the methodology, risks, and lessons learned from early AI implementations in safety and operational workflows. While AI continues to dominate headlines across industries, the discussion grounded the conversation in something more practical: how to successfully implement AI in real-world offshore environments. Three themes emerged consistently throughout the presentation: - Start small with specific workflows. - Collaboration between operators and technology providers is essential. - Security and governance are non-negotiable. Rather than attempting broad transformation programs, organizations are seeing the most success when they begin with focused operational workflows where measurable value can be delivered quickly. Examples include areas like shift handovers, incident management, and lessons learned systems, where large volumes of unstructured operational knowledge already exist but are often difficult to access when decisions need to be made. By targeting these workflows first, companies can demonstrate immediate operational benefits while building trust in AI systems across the organization. ## AI Is Moving From Experimentation to Operations During the fireside panel discussion, Jeff was asked about the most important trends shaping the future of AI, particularly in offshore energy. One emerging area is Agentic AI task-oriented systems capable of supporting multi-step operational processes rather than simply responding to prompts. In offshore environments, these systems could assist with activities such as inspections, shift preparation, or emergency response planning by continuously synthesizing operational context and historical experience. A second trend is organizational. Increasingly, executive leadership teams are committing to AI as part of core operations, moving away from the "perpetual pilot" phase that has characterized many early deployments. AI initiatives are beginning to transition from experimental projects into integrated operational capabilities. The third trend relates to the broader economics of AI adoption. ## The Economics of AI Are Still Emerging Jeff also addressed the macroeconomic reality surrounding AI adoption. While enthusiasm for AI is widespread - from enterprise deployments to personal productivity tools like ChatGPT - the business case for AI is still evolving. Unlike the early internet era, where companies could quickly measure the impact of launching an e-commerce website, AI adoption requires more foundational work before benefits are fully realized. Organizations are currently investing in: - Governance frameworks and AI guardrails - Data preparation and infrastructure - Specialized AI talent - Careful rollout strategies to avoid first-adopter risk As a result, global financial markets are still grappling with how to value companies building AI-enabled products and services. Many leaders have a strong intuition about AI's transformative potential, but clear financial metrics are only beginning to emerge. That said, the expectation across the industry is clear: as deployments mature, the operational benefits of AI will become increasingly quantifiable. ## A Key Lesson from Real AI Deployments One of the most important lessons from early deployments may also be the simplest: **Data quality matters more than model sophistication.** Many organizations initially focus on selecting the most advanced AI models available. However, in practice, the performance of AI systems is often constrained by the quality of the data they rely on. As Jeff noted during the session, *"Organizations often chase increasingly sophisticated models while overlooking the state of their underlying data. In reality, a well-configured system operating on clean, normalized data will consistently outperform a more advanced model working with inconsistent or poorly structured information"*. For offshore operations, where historical records, inspection reports, incident narratives, and operational notes often exist in fragmented formats, data normalization and retrieval architecture are critical to success. ## A Pragmatic Path Forward AI is already beginning to demonstrate value in offshore energy operations. But the most successful deployments share common characteristics: - Start with high-value workflows - Focus on data quality and retrieval - Embed AI directly into operational processes - Implement strong security and governance - Expand incrementally as trust grows Rather than replacing human expertise, AI is increasingly acting as an operational intelligence layer, helping ensure that institutional knowledge, historical experience, and operational context are available when they matter most. As offshore operations continue to digitize, the companies that succeed with AI will not be those chasing technology trends, but those systematically embedding intelligence into the way work is done. ### From Hype to Results: A Practical AI Deployment Methodology for Offshore Energy From Hype to Results: A Practical AI Deployment Methodology for Offshore Energy Artificial intelligence holds enormous promise for offshore energy operations, but successful deployments rarely begin with technology. They begin with approach and methodology. Through our collaboration with the offshore energy industry, Focus FS has developed a structured approach to deploying AI in offshore environments that prioritizes operational reality over experimentation. The process begins with workflow identification. Instead of asking, "What can AI do here?", we ask a more practical question: "How does work actually get done, and where does friction exist?" This workflow-first mindset ensures AI is applied where it can immediately improve operational performance. It's also what separates deployments that succeed from those that stall. Next comes Data Reality Assessment, a step many organizations skip. Before configuring any model, we audit the underlying data. Inconsistent terminology, legacy document formats, missing context - these issues must be addressed early. Without this step, even the most advanced AI system will struggle to produce trustworthy results. Once the data landscape is understood, the focus shifts to Digitize and Normalize. This stage may not be glamorous, but it's essential. Standardizing inputs - reports, notes, inspections, operational records - creates the infrastructure that enables reliable AI outputs. The next principle is critical: **Embed AI, Don't Bolt It On.** AI that sits outside operational workflows rarely gets used. AI embedded directly into processes such as shift handovers, incident reviews, or inspections becomes part of daily work. Finally, deployments follow a Human-in-the-Loop approach with iteration in production. Systems are introduced through controlled pilots, demonstrating value in one workflow before expanding across adjacent processes. This phased rollout improves adoption, reduces operational risk, and allows governance and security controls to mature alongside the technology. For our customers, this disciplined methodology protects their investment. Structured deployments deliver measurable value early and create the momentum needed to scale AI across their operations. ### Digital Dragerman: Mine Safety, Evolved Connect every aspect of your operation. Focus FS unifies incident tracking, asset management, and personnel coordination in a single platform, streamlining processes and fostering collaboration for seamless operations. ### AI You Can Trust: Building Security and Guardrails from Day One AI You Can Trust: Building Security and Guardrails from Day One Artificial Intelligence in offshore energy cannot succeed without trust. That trust begins with Applied AI Guardrails, which should be viewed not as a compliance checklist but as a commitment to responsible deployment. The first principle is simple: customer data must remain completely isolated. In a multi-client cloud environment, organizations need assurance that their operational data is never used to train another customer's model. Data separation is foundational full stop. Next comes role-based access and project isolation. Traditional software permissions determine whether a user can access a feature. AI systems add another dimension: even if a model knows the answer, it must also know whether the user is authorized to see it. Equally important is eliminating cross-user memory and uncontrolled learning. AI systems that learn from shared user interactions can inadvertently leak information between sessions if not carefully designed. Guardrails must prevent this entirely. Trust is also strengthened through source attribution and confidence indicators, allowing users to see where AI-generated insights originate and how reliable they are. Every output must also be logged in a full audit trail, providing traceability for incident investigations and regulatory review. Finally, AI systems must align with enterprise governance and Single Sign-On frameworks, integrating seamlessly with existing identity management. In offshore operations, security is not optional it is the foundation that enables AI adoption. ### What Real AI Deployments Teach Us What Real AI Deployments Teach Us As AI moves from experimentation to operational deployment, organizations are discovering that success depends less on technology and more on deployment discipline. One of the most important lessons is that **traceability builds trust**. Operators in high-consequence environments will not act on recommendations they cannot verify. Every AI output must link back to its source data. Another critical lesson is **data quality over model sophistication**. Many organizations initially focus on adopting the most advanced models available. In practice, a well-configured system operating on clean, normalized data consistently outperforms a sophisticated model working with inconsistent inputs. Deployment strategy also matters. **Incremental adoption beats big-bang transformation.** Large-scale “transform everything” initiatives often stall under their own complexity. Starting with one workflow, proving value, and expanding gradually leads to stronger adoption and measurable results. A surprising lesson for many teams is that **UI and UX design determine success**. Even highly accurate AI systems fail if the interface disrupts how people work. Finally, the most successful deployments treat AI as support for human expertise, not automation of judgment. Operators want tools that make them better at their jobs not systems that attempt to replace them. **Design for augmentation, and adoption follows.** ### Understanding the Real Risks of AI Deployment Understanding the Real Risks of AI Deployment Every serious AI deployment includes a clear understanding of risk. Addressing these risks is not a warning sign, it is evidence of organizational maturity. One of the most counterintuitive risks is **over-trust**. As AI outputs become more sophisticated, users may accept recommendations without verification. Mitigation requires structural controls such as confidence scoring, source citations, and mandatory human review checkpoints. At the same time, organizations must manage **under-trust**. Different teams often have varying levels of familiarity with AI. Transparency is the solution: when users can trace outputs directly to underlying data, skepticism becomes informed validation. Another long-term challenge is **data drift**. Over time, processes change, new equipment is introduced, and terminology evolves, particularly in industries known for their acronyms. Models trained on outdated data can gradually produce subtle but incorrect results. Governance practices such as scheduled model revalidation and clear data ownership help address this risk. Deployment strategy can also introduce risk. **Workflow disruption** occurs when AI is layered on top of existing processes, creating parallel systems and duplicated work. Embedding AI directly within workflows eliminates this issue. Finally, **security gaps** are non-negotiable. Offshore operations handle sensitive operational, personal, and proprietary data. Strong access controls and governance must be built into every deployment. Managing these risks is what separates durable AI systems from short-lived experiments. ### Designing AI for high-risk environments _Perspective_ ### How one utility cut outage response by 40% _Customer story_ ### The new operating picture for incident command _Guide_ --- ## Operational Use Cases ### Mine Rescue ## Mine Rescue ### Faster Coordination During Underground Emergencies **Real-time visibility between underground rescue teams and incident command.** Underground mine emergencies require precise coordination between rescue teams operating below surface and incident command managing the response above ground. However, traditional rescue operations often rely on radio communication, handwritten logs, and limited visibility into the evolving conditions underground. Critical information such as gas concentrations, oxygen levels, and team location can be difficult to track and communicate in real time. Focus FS provides a digital platform for coordinating mine rescue operations with integrated underground mapping and real-time data capture. Rescue teams can transmit gas detection readings, oxygen levels, and situational updates directly from underground into a centralized incident command dashboard. Digital mine maps allow command teams to track team location, hazard zones, and progress through the mine in real time. This improves situational awareness, enables faster decision-making, and strengthens coordination between rescue teams and command personnel helping organizations respond more effectively when every minute matters. ### Incident Reporting ## Incident Reporting ### Accelerate Incident Investigation Workflows with AI Suggested Lessons Learned **Capture accurate incident data faster while improving investigation quality.** Incident reporting is critical to improving safety and operational performance. Yet many organizations struggle with inconsistent reporting formats, incomplete documentation, and delayed investigations. Valuable operational insights are often buried within narrative reports, making it difficult to identify patterns and prevent future incidents. Focus FS digitizes incident reporting workflows and embeds AI to assist with documentation, categorization, and historical comparison. The platform automatically structures incident narratives, suggests relevant classifications, and retrieves similar historical incidents to support investigation teams. Investigators gain faster access to prior lessons learned, root cause patterns, and recommended corrective actions. This reduces investigation time while improving the quality and consistency of incident analysis. By transforming incident reporting from a compliance exercise into an operational intelligence process, organizations strengthen their safety culture, reduce repeat incidents, and improve the speed at which corrective actions are implemented. ### Shift Handover ## Shift Handover ### Improve Operational Continuity Between Shifts **Ensure critical operational context is never lost during shift transitions.** Shift handovers are one of the most critical moments in offshore and industrial operations. Important operational details including equipment conditions, ongoing maintenance, safety risks, and operational deviations must be communicated clearly between teams. However, traditional handovers often rely on manual notes, emails, or verbal briefings. Important context can be missed or inconsistently recorded, increasing operational risk. Focus FS digitizes the shift handover process and embeds operational intelligence directly into the workflow. Operators can document operational conditions, highlight risks, and track unresolved issues in a structured environment. AI assists by summarizing shift activity, identifying anomalies, and surfacing relevant historical events tied to the asset or operation. This creates a clear, auditable operational record across shifts. Teams begin each shift with complete situational awareness, reducing operational errors and improving coordination between teams. ### Embedded AI ## Embedded AI ### Bring AI Directly Into Operational Workflows **Deliver intelligence at the moment decisions are made.** Many AI initiatives fail because intelligence is delivered outside of operational workflows through dashboards or standalone tools that operators rarely use during real work. Focus FS takes a different approach by embedding AI directly into operational processes such as inspections, incident reviews, shift handovers, and risk assessments. Instead of asking users to query AI separately, the system surfaces relevant insights automatically as work is being performed. AI retrieves relevant historical experience, identifies patterns across operational data, and assists with documentation and decision support. Operators maintain full control while gaining immediate access to contextual knowledge. This approach reduces cognitive load, improves consistency in operational decisions, and ensures that valuable organizational experience is accessible when it matters most. Embedded AI becomes part of daily operations rather than an external analytical tool. ### Process Safety ## Process Safety ### Strengthen Process Safety Oversight **Improve visibility into critical safety controls and operational risk.** Process safety management requires constant monitoring of operational conditions, procedures, and control systems. However, many organizations struggle with fragmented safety documentation and limited visibility into how process safety controls are functioning in real operations. Focus FS provides a centralized platform for managing process safety workflows, including inspections, risk assessments, operational reviews, and corrective actions. Operators and safety teams gain clear visibility into safety-critical conditions across assets and operations. Embedded AI can analyze historical safety data, surface relevant lessons learned, and highlight deviations that may indicate emerging risk conditions. By connecting operational workflows with safety intelligence, organizations improve their ability to detect early warning signals, reinforce safety protocols, and reduce the likelihood of high-consequence incidents. ### Car Seal Management ## Car Seal Management ### Digitize and Control Critical Isolation Systems **Maintain integrity of critical safety barriers.** Car seal systems are widely used to ensure critical valves remain in their intended position. However, traditional car seal tracking methods often rely on manual logs and physical tags, making it difficult to maintain accurate records of changes or verify system integrity. Focus FS digitizes car seal management, creating a traceable and auditable record of all seal installations, removals, and inspections. Operators can easily verify valve positions, document changes, and track approvals. Integrated workflows ensure that any modification to safety-critical equipment is properly documented and reviewed. AI-assisted monitoring can also identify patterns or irregularities in seal management activity. By digitizing car seal processes, organizations strengthen barrier integrity, reduce compliance risk, and improve operational oversight of safety-critical systems. ### Preventive Maintenance ## Preventive Maintenance ### Improve Maintenance Planning and Asset Reliability **Use operational data to prevent failures before they occur.** Preventive maintenance programs are designed to reduce equipment failure and operational downtime. Yet maintenance planning is often disconnected from operational insights and historical experience. Focus FS integrates maintenance workflows with operational intelligence, allowing teams to analyze past equipment issues, inspection findings, and operational conditions when planning maintenance activities. AI can identify patterns in equipment performance and highlight recurring issues across assets or locations. Maintenance teams gain improved visibility into asset history and can prioritize activities based on risk and operational impact. This leads to more effective maintenance scheduling, reduced unexpected equipment failures, and improved asset reliability. Organizations benefit from lower downtime, better resource allocation, and improved operational continuity. ### Lessons Learned ## Lessons Learned ### Turn Operational Experience Into Actionable Knowledge **Ensure valuable lessons are never lost.** Industrial organizations generate vast amounts of operational knowledge through incidents, audits, inspections, and operational reviews. Unfortunately, this information is often stored in fragmented reports that are difficult to retrieve when needed. Focus FS transforms lessons learned into accessible operational intelligence. Historical records are indexed, structured, and made searchable across the organization. AI helps identify relevant past experiences based on operational context such as asset type, location, or activity. When operators are planning work or reviewing incidents, the system can surface relevant historical lessons automatically. This ensures that valuable operational experience is consistently applied across teams and locations. By improving access to institutional knowledge, organizations reduce repeat incidents, improve decision quality, and strengthen organizational learning. ### Operational Risk Assessments ## Operational Risk Assessments ### Improve Risk Visibility Before Work Begins **Strengthen planning for high-risk operational activities.** Operational risk assessments are essential for identifying hazards and implementing appropriate controls before work begins. However, many risk assessments rely heavily on manual processes and limited historical context. Focus FS digitizes risk assessment workflows and connects them with operational data and historical experience. Teams can quickly identify similar past activities, relevant hazards, and recommended control measures. AI assists by surfacing patterns across historical incidents, near misses, and operational records tied to the activity being planned. This results in more informed risk assessments and better-prepared operational teams. Organizations gain improved hazard recognition, stronger risk mitigation planning, and greater consistency in how safety protocols are applied across operations. ### Operational Knowledge Management ## Operational Knowledge Management ### Unlock Decades of Operational Knowledge **Make institutional knowledge accessible in seconds.** Many industrial organizations possess decades of valuable operational records including incident reports, inspection findings, engineering notes, and operational briefings. Yet accessing this information when needed is often difficult. Focus FS creates a centralized operational knowledge system that connects historical documentation with real-time workflows. AI-powered search and retrieval allows operators and engineers to ask natural language questions and receive context-aware insights drawn from the organization's historical data. Instead of searching across multiple systems, teams gain instant access to relevant operational knowledge tied to their current task or asset. This dramatically improves the ability to learn from past experience, reduces time spent searching for information, and supports more consistent operational decisions across teams. --- ## Embedded AI Capabilities ### AI-Assisted Incident Investigations ## AI-Assisted Incident Investigations ### Accelerate Investigations with AI-Powered Analysis **Identify patterns, root causes, and lessons learned faster.** Incident investigations often require reviewing large volumes of reports, witness statements, inspection logs, and historical records. This process can be time-consuming and may miss valuable patterns hidden across multiple incidents or locations. Focus FS uses AI to assist investigators by analyzing incident narratives and retrieving similar historical events across the organization. The system can identify recurring failure patterns, highlight common contributing factors, and surface relevant lessons learned. Investigators gain immediate access to prior cases involving similar equipment, conditions, or operational activities. This allows teams to move more quickly from documentation to root cause analysis. AI does not replace investigator expertise it enhances it by reducing the time spent searching for information and ensuring that institutional knowledge is consistently applied. Organizations benefit from faster investigations, stronger corrective actions, and improved prevention of repeat incidents. ### AI Operational Knowledge Assistant ## AI Operational Knowledge Assistant ### Ask Your Operational Data a Question **Instant access to decades of operational experience.** Industrial organizations possess decades of valuable operational knowledge stored across reports, inspections, incident records, and operational notes. However, this information is often difficult to locate when decisions need to be made. Focus FS enables teams to query operational data using natural language. Operators, engineers, and safety personnel can ask questions such as: - "Have we seen this issue before?" - "What incidents involved this asset?" - "What lessons apply to this operation?" AI analyzes historical records and returns context-aware answers tied to the user's role, asset, and operational environment. Instead of searching across multiple systems, teams gain immediate access to relevant experience from across the organization. This dramatically reduces the time required to locate operational insights while ensuring critical knowledge is consistently applied across assets and locations. ### AI-Enhanced Inspections ## AI-Enhanced Inspections ### Smarter Inspections with Real-Time Intelligence **Identify risks faster during field inspections.** Field inspections are essential for identifying operational risks, equipment issues, and safety hazards. However, inspectors often rely on manual checklists and limited access to historical context while performing their work. Focus FS integrates AI directly into digital inspection workflows. As inspectors record observations, the system can surface relevant historical inspection findings, past incidents, or maintenance issues tied to the same equipment or location. AI can also assist with documenting inspection findings by structuring notes, identifying potential anomalies, and highlighting areas that require further review. This allows inspectors to perform more informed inspections while reducing administrative burden after the inspection is complete. Organizations benefit from improved hazard identification, more consistent inspection quality, and stronger operational oversight across assets. ### AI Workflow Automation ## AI Workflow Automation ### Reduce Administrative Workload Across Safety Workflows **Automate documentation while improving data quality.** Many operational workflows including incident reports, risk assessments, and inspection documentation require significant manual effort to complete. Teams often spend valuable time writing narratives, entering structured data, and organizing reports. Focus FS uses AI to assist with documentation by automatically structuring narrative text, suggesting classifications, and pre-populating workflow fields based on available data. For example, AI can summarize operational events, categorize incident types, and suggest corrective actions based on similar historical cases. This significantly reduces the time required to complete reports while improving the consistency and quality of recorded data. By automating repetitive administrative tasks, teams can focus more of their time on operational decision-making and risk mitigation. Organizations benefit from faster reporting, improved data accuracy, and stronger organizational learning. ### AI-Powered Operational Intelligence ## AI-Powered Operational Intelligence ### Turn Operational Data Into Actionable Insight **Detect emerging risks across operations.** Operational data is generated across inspections, incidents, maintenance activities, and operational reviews. However, identifying patterns across these datasets can be challenging when information is stored in separate systems. Focus FS uses AI to analyze operational data and detect patterns that may indicate emerging risks. By connecting data across workflows, the system can identify recurring equipment issues, operational anomalies, or safety trends across assets and locations. AI can highlight patterns that may otherwise remain hidden in large datasets, allowing organizations to act earlier and prevent potential incidents. These insights help safety and operational leaders move from reactive reporting toward proactive risk management. Organizations benefit from improved visibility into operational trends, stronger preventative actions, and better-informed decision-making across their operations. ### Operational AI Search Using Natural Language **Operational Knowledge, Instantly Accessible** Query operational records, inspections, and incident history using natural language. AI retrieves relevant insights from across your data while linking directly to source documents for verification and deeper review. All within a private, secure, data model. ### AI-Powered Incident Intelligence **Turn Investigations into Institutional Knowledge** AI analyzes investigation details and recommends lessons learned and corrective actions based on historical operational data, helping organizations identify patterns and prevent repeat events. ### Voice-to-Workflow Automation **Speak. The Form Fills Itself.** Convert voice input into structured operational records. AI automatically populates workflow forms, reducing reporting effort while improving the speed and consistency of field documentation. ### Enterprise AI Principles - **Private AI** — Secure isolated environments for your models. Your training data never leaves your infrastructure. - **Role-Based Access** — Granular access controls across the enterprise. Define exactly who interacts with which intelligence layers. - **Data Ownership** — Full sovereignty over your operational data. We provide the intelligence engine, you own the fuel. - **Embedded Logic** — Native integration into existing industrial workflows. No retrofits, no plugins, just enhancement. ### Operational Impact - **94% Prediction Accuracy** — Validating over 2.4 million operational data points daily with sub-millisecond latency. - **-31% Unplanned Downtime** — Proactive maintenance scheduling through predictive anomaly detection across legacy hardware. - **2.4x OEE Efficiency** — Overall Equipment Effectiveness uplift through autonomous process optimization loops. --- ## Platform ### Platform Overview Energy, Mining, and Heavy Industrial operations face similar challenges: harsh environments, remote locations, safety-critical workflows, and strict data governance. Focus FS provides a configurable operational platform designed for these realities. Our platform digitizes operational workflows and embeds AI directly into how work gets done, supporting inspections, incident management, shift handovers, emergency coordination, and operational reporting. **Key capabilities** - **Configurable workflows** — Aligned to operational procedures - **Embedded AI** — Surfaces relevant operational context and historical insights - **Secure data architecture** — Enterprise governance and role-based access - **Harsh & remote environments** — Designed for operational realities - **Standardized platform** — Scales across assets and locations ### Platform Features - **Access anywhere** — Focus FS is a progressive web application, meaning you can access it through all supported browsers on almost any device. - **Secure cloud hosting** — Our software-as-a-service model allows for a convenient, reliable and cost-effective hosting solution on secure data centres. - **Automated workflows** — Standardized, automated workflows and processes enable accuracy and consistency in data, actions and reporting. - **Role-based permissions** — Users only get access based on customizable permission levels, from the company and project level down to modules and reports. - **Audit trails** — Keep detailed logs of which users create or update any records in the system. - **Third-party integration** — We're continuously adding new devices that communicate with our software, including those from our partners at Dräger. - **Multilanguage** — Translation-ready for a range of languages. - **Data privacy** — We work to ensure your data is stored safely and securely in accordance with relevant data privacy legislation. ### Service Highlights - **Onboarding** — Our proven onboarding process follows best practices to help your organization hit the ground running. - **Support** — Our trained support staff and partners are always available to provide expert support whenever you need it. - **Training** — We ensure your success with guided training, and offer self-service tutorials through our Help Centre. - **Pricing** — We price our solutions by worksite, not by user, so you have the flexibility you need to build a better safety culture. ### Products - **Foresight Ops** — Incident & field operations - **Foresight Risk** — Predictive analytics - **Foresight Intel** — Sensors & intelligence - **Foresight Command** — Executive oversight ### Platform in Practice - Deployed across 30+ mining operations - Supporting multi-site offshore energy environments - Integrated within regulated emergency response programs - Configured across enterprise safety and operational teams ### Partners & Highlights **Partnered with Dräger** An international leader in the fields of medical and safety technology, Dräger has been our partner and investor since 2019. You'll get all the benefits of their global reach and 130 years of experience. **Focus FS Rolling Out at 30 Mines Across Ontario** We're excited to officially announce our partnership with Dräger Canada and Ontario Mine Rescue to bring Focus FS Emergency Response to 30 mines across Ontario. Click here to read the press release. **Improving Through Innovation** "We now have an extra layer of insight into operations which is very useful for reporting. When we tell clients about our commitment to safety, we have detailed data to back our claims. If there are any issues, we know about them more quickly than we did with paperwork." - Nate Thompson, Director of HSEC, DMC Mining Services ### Standardize how critical work gets done. Across mining, oil & gas, utilities, and emergency response, performance depends on coordination and critical insights. Focus FS standardizes operational workflows and surfaces historical experiences within one configurable, secure system. --- ## Solutions by Sector ### Energy — Operational Intelligence for Offshore and Energy Infrastructure Energy operations require disciplined coordination across safety, maintenance, and production. Focus FS digitizes operational workflows and embeds intelligence directly into daily operations, supporting teams working in complex and remote environments. **Capabilities** - **Digitized Workflows** — Inspections, shift handovers, incident reporting, and risk assessments automated and tracked. - **Embedded AI** — Retrieves historical lessons, incidents, and operational context instantly. - **Secure Data Management** — Enterprise identity systems with governance aligned to your security requirements. - **Configurable Workflows** — Aligned to operational procedures without requiring custom development. - **Standardized Platform** — Scales seamlessly across offshore and onshore assets and locations. **Measurable impact** - **Improved Continuity** — Shift-to-shift operational continuity and knowledge transfer. - **Faster Response** — Incident reporting and investigation cycles accelerated. - **Quick Access** — Reduced time locating operational records and documentation. - **Greater Visibility** — Enhanced visibility into operational and safety trends in real-time. - **Strong Compliance** — Auditable digital records ensure regulatory compliance. **Featured use cases:** Incident Reporting, Shift Handover, Car Seal Management, Process Safety, Lessons Learned ### Mining — Digitized Operations for Remote and Underground Environments Mining operations require reliable systems that support teams operating in remote locations and underground environments. Focus FS provides operational coordination tools that improve visibility, safety oversight, and response readiness. **Capabilities** - **Digital Inspection Workflows** — Safety observation and inspection protocols structured and tracked systematically. - **Mine Rescue Coordination** — Real-time operational visibility and emergency response coordination. - **Centralized Reporting** — Safety, incidents, and operational activities reported and tracked centrally. - **Embedded AI** — Surfaces relevant historical events and operational insights automatically. - **Configurable Workflows** — Site procedures aligned with standardized platform architecture. **Measurable impact** - **Faster Coordination** — Mine rescue and emergency response coordination accelerated with real-time visibility. - **Better Safety** — Hazard identification and safety reporting improved across operations. - **Reduced Workload** — Administrative burden for safety documentation significantly reduced. - **Greater Visibility** — Operational visibility enhanced across multiple mine sites simultaneously. - **Lessons Applied** — Consistent application of lessons learned across all operations. **Featured use cases:** Mine Rescue, Incident Reporting, Preventive Maintenance, Embedded AI, Operational Knowledge Management ### Heavy Industrial — Scalable Operational Systems for Complex Facilities Heavy industrial facilities generate large volumes of operational data across maintenance, safety, and production teams. Focus FS connects these workflows into a unified operational intelligence platform. **Capabilities** - **Digitized Workflows** — Inspections, maintenance reporting, and safety workflows structured and tracked systematically. - **Centralized Data** — Operational data unified across facilities, teams, and departments. - **Embedded AI** — Identifies operational patterns and retrieves historical insights automatically. - **Configurable Workflows** — Aligned with facility processes without requiring custom development. - **Standardized System** — Scales seamlessly across multiple sites and operational locations. **Measurable impact** - **Better Maintenance** — Improved maintenance planning and enhanced asset visibility. - **Quick Access** — Faster access to operational records and complete incident history. - **Consistent Processes** — Standardized inspection and safety processes across all facilities. - **Reduced Effort** — Reporting effort minimized through structured workflow automation. - **Enhanced Insight** — Operational intelligence and visibility for facility leadership. **Featured use cases:** Lessons Learned, Embedded AI, Operational Knowledge Management, Incident Reporting, Preventative Maintenance --- ## Careers ### Industrial Mid-Market Account Executive Business Development · Flexible New business across heavy industrial, mining, construction, and oil & gas. ## Role Summary Drive new business at the site and project level across mining, construction, and oil & gas operators and contractors. Focus on high-velocity deals with rapid deployment and clear ROI. **Location:** Flexible ## Key Responsibilities - Manage a high-volume pipeline of site-level opportunities - Sell directly to operations, HSE, and site leadership - Run efficient sales cycles (30–120 days) - Deliver compelling demos focused on operational impact - Close deals and support initial expansion across additional sites - Partner with BDRs for pipeline generation ## Qualifications - 2+ years technology sales experience - Experience in mid-market or transactional sales environments - Strong discovery, demo, and closing skills - Ability to operate with speed and discipline - Industry exposure (construction, mining, O&G) is a strong asset ## Why Join Focus FS? Work with a growing industrial technology company delivering AI-enabled operational platforms to some of the world's most complex industries. Be part of a high-impact team helping mining, oil & gas, and industrial operators modernize safety, operations, and field execution through scalable enterprise technology. Join a company investing heavily in operational AI, enterprise growth, and long-term customer partnerships across North America and global industrial markets. Collaborate closely with executive leadership, product, and solutions teams in a fast-moving environment where your contributions directly influence growth and customer success. Competitive compensation, comprehensive benefits, and the opportunity to help shape the next phase of Focus FS growth. ### Enterprise Account Executive Business Development · Flexible Strategic enterprise accounts in mining and oil & gas. ## Role Summary Own and grow a portfolio of strategic enterprise accounts in mining, oil & gas, and heavy industry. Lead complex, multi-stakeholder sales cycles and drive multi-site, long-term platform adoption. **Location:** Flexible ## Key Responsibilities - Build and execute account plans across 4–6 strategic accounts - Engage senior stakeholders (Operations, IT, HSE, Digital) - Lead full sales cycle from discovery through close - Orchestrate internal resources (Solutions, Product, Leadership) - Identify and expand opportunities across business units and sites - Manage long sales cycles, procurement, and enterprise negotiations ## Qualifications - 7–12+ years enterprise SaaS or industrial technology sales - Experience selling into mining, oil & gas, or heavy industry preferred - Proven track record closing $250K+ ACV deals - Strong executive communication and relationship management - Familiarity with operational systems, safety, or industrial AI is an asset ## Why Join Focus FS? Work with a growing industrial technology company delivering AI-enabled operational platforms to some of the world's most complex industries. Be part of a high-impact team helping mining, oil & gas, and industrial operators modernize safety, operations, and field execution through scalable enterprise technology. Join a company investing heavily in operational AI, enterprise growth, and long-term customer partnerships across North America and global industrial markets. Collaborate closely with executive leadership, product, and solutions teams in a fast-moving environment where your contributions directly influence growth and customer success. Competitive compensation, comprehensive benefits, and the opportunity to help shape the next phase of Focus FS growth. ### Solution Engineer Technical Sales · St. John's Technical lead translating customer challenges into solutions. ## Role Summary Act as the technical lead in the sales process, supporting both Enterprise and IMM segments. Translate customer operational challenges into technical solutions and ensure alignment with security, data, and infrastructure requirements. **Location:** St. John's ## Key Responsibilities - Lead technical discovery and solution design - Deliver product demos tailored to operational use cases - Support enterprise security and architecture reviews - Work closely with AEs to progress and close deals - Bridge customer requirements with product and engineering teams - Support customer onboarding, pilots and proof-of-concepts ## Qualifications - 3+ years in solutions engineering, technical sales, or similar - Experience with SaaS platforms, data systems, or AI solutions - Understanding of industrial environments or operational workflows preferred - Strong communication skills (technical to non-technical audiences) - Ability to manage multiple deals across segments ## Why Join Focus FS? Work with a growing industrial technology company delivering AI-enabled operational platforms to some of the world's most complex industries. Be part of a high-impact team helping mining, oil & gas, and industrial operators modernize safety, operations, and field execution through scalable enterprise technology. Join a company investing heavily in operational AI, enterprise growth, and long-term customer partnerships across North America and global industrial markets. Collaborate closely with executive leadership, product, and solutions teams in a fast-moving environment where your contributions directly influence growth and customer success. Competitive compensation, comprehensive benefits, and the opportunity to help shape the next phase of Focus FS growth. ### Product Designer (UX/UI) Design · St. John's, NL End-to-end UX/UI ownership from requirements to build-ready designs. ## Job Summary We are looking for a Product Designer (UX/UI) with strong end-to-end ownership who can take a feature or workflow from business requirements through to build-ready designs and production implementation. This is a highly collaborative, hands-on role. You will work closely with product leadership, developers, and AI engineers to translate ideas, requirements, and constraints into clear, practical, and buildable user experiences. You will be expected to make informed design decisions, balance trade-offs, and deliver solutions that are both high-quality and implementation-ready, directly supporting engineering through flows, prototypes, and structured design documentation. ## What You Will Do - Translate business requirements into user flows, wireframes, and high-fidelity UI designs aligned with our Angular-based platform. - Create interactive Figma prototypes that clearly communicate states, edge cases, and real-world usage scenarios. - Design with awareness of offline-first workflows, data synchronization, and system reliability constraints. - Produce clear design documentation for internal review and sign-off, including flows, assumptions, constraints, and rationale. - Work directly with Full Stack Developers to ensure designs are buildable, precise, and implementation-ready. - Support UX for AI-powered features, including data presentation and human-in-the-loop interactions. - Maintain and extend existing design systems and component libraries for consistency across the platform. - Participate in design reviews, walkthroughs, and release readiness processes. ## What Will Help You Succeed - Demonstrated experience designing and shipping real-world SaaS or platform products. - Advanced proficiency in Figma, including components, design systems, and interactive prototyping. - Strong ability to take ownership of ambiguous product problems and turn them into clear design solutions. - Ability to balance user experience quality with technical and delivery constraints. - Experience producing structured design documentation that supports engineering implementation and stakeholder sign-off. - Strong communication skills and confidence working directly with developers and product leaders. - Familiarity with AI-assisted design tools and workflows (e.g., Claude, Figma AI features, Canva AI, or similar tools) is considered valuable. ## Nice to Have - Experience contributing to or owning at least one shipped SaaS or digital product end-to-end. - Strong understanding of responsive web design and component-based UI systems. - Experience working in cross-functional teams with developers and product managers. - Portfolio demonstrating end-to-end UX/UI ownership and shipped work. - Experience designing for Angular, React, or similar component-based frontend systems. - Exposure to AI-driven product experiences or LLM-based interfaces. - Experience with design systems at scale in production environments. - Understanding of accessibility (WCAG) and usability standards. - Experience designing for offline-first or synchronization-heavy applications. ## What We Value - Strong ownership mindset from discovery through to delivery. - Practical design thinking grounded in real engineering constraints. - Clear communication and ability to align stakeholders quickly. - Ability to work fast in an iterative, product-driven environment. Please include a portfolio or examples of shipped product work demonstrating end-to-end UX/UI ownership. ### Full Stack Developer Software · St. John's, NL Build and ship production SaaS applications end-to-end. ## Job Summary We are looking for a Full Stack Developer with strong hands-on experience building and shipping production SaaS applications. This role is suited to someone who can take ownership of features end-to-end and work comfortably across frontend, backend, and data layers. You will work closely with UX designers, product leadership, and AI engineers to deliver reliable, high-quality functionality in a mission-critical environment. You will be expected to make sound technical decisions, balance trade-offs, and contribute code that is clean, secure, and maintainable. ## What You Will Do - Build and maintain full stack web applications supporting our SaaS platform. - Develop responsive frontend features using Angular, TypeScript, HTML, and CSS/SCSS. - Design and implement backend services using Node.js and RESTful APIs. - Work with MariaDB and IndexedDB to support performant data access and offline-first workflows. - Collaborate with designers to implement build-ready UI and UX patterns. - Integrate authentication, authorization, and security best practices. - Contribute to CI/CD pipelines using Git and Azure DevOps. - Debug, optimize, and improve performance across the full application stack. ## What Will Help You Succeed - Demonstrated experience shipping production web applications. - Strong proficiency with Angular, TypeScript, and modern JavaScript. - Solid backend experience with Node.js and API-driven architectures. - Comfort working with relational databases and data synchronization patterns. - Ability to work independently and take ownership of complex features. - Strong communication skills and experience working in small cross-functional teams. - Familiarity with using AI-assisted development tools (e.g., Cursor, Claude, GitHub Copilot, ChatGPT) as part of daily coding workflows. ## Nice to Have - 1–3 years of experience in full stack or software development roles. - Hands-on experience shipping at least one production web application or SaaS product. - Strong understanding of frontend + backend integration patterns. - Working knowledge of Git and CI/CD workflows. - Basic understanding of application security, performance, and debugging. - Experience with NestJS, Express, or similar backend frameworks. - Exposure to Azure or other cloud platforms. - Familiarity with Docker or containerized development environments. - Experience with offline-first applications or data sync systems. - Exposure to AI-integrated features or LLM-based applications. ## What We Value - Ownership mindset and ability to deliver end-to-end features. - Practical engineering approach focused on shipping reliable software. - Comfort working in fast-moving, cross-functional teams. - Curiosity and willingness to leverage modern AI tools to improve productivity and quality. Please include links to relevant projects or repositories where possible. ### AI Engineer Software · St. John's, NL Design, build, and deploy production-grade AI systems. ## Job Summary We are seeking a motivated AI Engineer (1–3 years experience) to join the AI Innovation Team at Focus FS. You will contribute to designing, building, and deploying production-grade AI systems across generative AI, machine learning, and cloud-native architectures. This role is focused on hands-on implementation in real systems, not just experimentation. You will work closely with AI Engineers, Full Stack Developers, and Product teams to turn LLM and ML capabilities into reliable, scalable SaaS features. ## Key Responsibilities - Design, build, and deploy AI features using Generative AI, NLP, and Retrieval-Augmented Generation (RAG). - Implement and maintain end-to-end ML/LLM pipelines including data ingestion, training/evaluation workflows, and deployment. - Build production-ready services using Python (FastAPI preferred) and integrate AI models into backend systems via APIs. - Work with vector databases (e.g., Pinecone, Weaviate, FAISS, pgvector) for embedding-based retrieval systems. - Integrate and fine-tune LLMs via APIs (OpenAI, Azure OpenAI, Hugging Face models). - Develop and maintain CI/CD pipelines (GitHub Actions, Azure DevOps, or similar) with testing and version control best practices. - Deploy and operate AI services in cloud environments (Azure / AWS / GCP) using containers (Docker, basic Kubernetes exposure preferred). - Implement monitoring, logging, and evaluation pipelines for model performance and reliability. - Conduct A/B testing and offline evaluation to measure model quality and user impact. - Collaborate with cross-functional teams to translate prototypes into production-grade scalable systems. ## Nice to Have - 1–3 years of experience in software engineering, ML engineering, or AI-focused development roles. - Hands-on experience building or deploying at least one production or near-production AI/ML system. - Strong proficiency in Python and familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). - Experience working with LLM APIs (OpenAI, Azure OpenAI, Hugging Face Transformers). - Understanding of RAG architectures, embeddings, and vector search systems. - Familiarity with REST APIs, microservices architecture, and backend systems integration. - Basic experience with cloud platforms (Azure / AWS / GCP) and containerization (Docker). - Working knowledge of Git, CI/CD pipelines, and automated testing practices. - Experience with LangChain, LlamaIndex, CrewAI, or similar LLM orchestration frameworks. - Exposure to model evaluation techniques, prompt engineering, or fine-tuning workflows. - Understanding of MLOps practices (model versioning, deployment monitoring, rollback strategies). - Familiarity with event-driven architectures or streaming data pipelines. - Experience optimizing AI systems for latency, cost, and scalability trade-offs. ## What We Value - Curiosity and eagerness to keep up with rapidly evolving AI technologies. - Practical engineering mindset balancing experimentation with production reliability. - Strong collaboration and communication in cross-functional teams. - Ability to work through ambiguity and deliver incremental, production-ready value. ## What Will Help You Succeed - Experience deploying AI or machine learning solutions into production systems. - Strong proficiency in Python and modern ML frameworks. - Comfort working within an established AI codebase and collaborating with other AI Engineers. - Understanding of system performance scalability and operational considerations. - Ability to work through ambiguity and deliver practical AI solutions. - Strong collaboration and communication skills. - Please include examples of production AI work or projects where available. Please include links to relevant projects or repositories where possible.