Ankior Technologies / SAFETYTECH
SafetyOps
An EHS platform for incidents, investigations, CAPA, risk, tasks, evidence, and auditable closure.
Closed-loop safety operations architecture
Where the project started
SafetyOps carries an incident from intake through triage, RCA, and CAPA to a verified closure, with an immutable timeline and evidence built to survive an audit.
SafetyOps is the source of truth for incidents and corrective actions. The architecture separates intake, triage, investigation, CAPA, evidence, notifications, and reporting, with critical histories kept append-only.
Business / Product
Problems and solutions
Each tile connects a real product tension with the decision that clarified the domain, UX, or operations.
Incidents without owners, evidence, and a CAPA process become reports that cannot be defended during an audit.
Design a flow from signal to decision, task, evidence, and closure.
The scope could easily become a feature list without one product, data, and ownership model.
SafetyOps is the source of truth for incidents and corrective actions. The architecture separates intake, triage, investigation, CAPA, evidence, notifications, and reporting, with critical histories kept append-only.
The project needed decisions that would stay readable after the first version: for users, the team, and further delivery.
Incident lifecycle with auditable state. CAPA and SLA as operating mechanisms.
Roles and competencies
The competencies this build required
Competencies are shown as ownership roles: CTO, Tech Lead, engineering, DevOps, cloud, security, and AI where they were part of the work.
Fractional CTO
Connecting the product thesis, domain risk, and priorities so technology supports business decisions instead of becoming a separate workstream.
Open competency- Product category and positioning
- Scope priorities and risk
- Decisions ready for founder or CTO review
- Product thesis translated into technical priorities.
- Risks framed in a language stakeholders can review.
Tech Lead
Shaping responsibility boundaries, domain modeling, and architecture decisions so the project can grow without drifting into an accidental monolith.
Open competency- Bounded contexts and ownership
- Readable technical decisions
- Roadmap without implementation chaos
- Domain boundaries and decisions that remain reviewable after MVP.
- Ownership model readable for later delivery stages.
Software Engineer
Turning the domain into screens, APIs, flows, and maintainable implementation with focus on clarity and post-MVP evolution.
Open competency- Backend and application contracts
- UX surfaces ready for iteration
- Code and maintenance model
- Implementation tied to the real product workflow.
- Contracts and code prepared for post-launch iteration.
DevOps Engineer
Designing delivery, observability, and operations so release, diagnostics, and recovery are part of the product.
Open competency- CI/CD and release readiness
- Observability and production signals
- Operations without manual rituals
- Release, diagnostics, and recovery designed as product capabilities.
- Production signals ready for maintenance without guessing.
Cloud Engineer
Choosing cloud, data, and integration foundations so scale, cost, and reliability are not added after the fact.
Open competency- Google Cloud and managed services
- Data, events, and storage
- Cost and operational control
- Cloud choices tied to cost, scale, and data responsibility.
- Integrations and storage treated as foundations, not add-ons.
Security Engineer
Critical evidence and decisions carry cryptographic integrity and a custody history, and closing a CAPA or incident requires an authorized role and segregation of duties.
Open competency- Evidence integrity: hashes, metadata, and custody-transfer history
- RBAC and segregation of duties on CAPA/incident closure
- Immutable incident timeline, resistant to silent edits
- Privacy, roles, and auditability built into the product model.
- Security visible in domain decisions, not only around the UI.
AI/ML Engineer
Treating AI as a product capability: with context, constraints, versioned signals, and a clear boundary between facts and interpretation.
Open competency- LLM as a controlled capability
- Signal and projection quality
- Responsible product constraints
- AI constrained by context, signals, and product responsibility.
- Boundary between facts and interpretation preserved in architecture.
Stack by competency
Tech stack
The stack is grouped by competency so it shows both technology and responsibility: software, leadership, DevOps, cloud, security, and AI.
- .NET / ASP.NET Core
- .NET MAUI mobile client
- Angular web client
- PostgreSQL
- Event-driven integration
- Access-control governance
- Evidence & history domain model
- Offline-first domain model
- OpenTelemetry
- Cloud Logging
- Kafka operations
- Object storage
- Kafka
- RBAC
- Audit log
- Evidence integrity & custody
Product preview
Gallery
Selected screens and materials showing the product surface, UX decisions, and work outcome.