Global Trust Index99.8%|
EU Regulatory SyncActive|
Network Latency12ms|
Uptime (90d)99.997%|
Threat PostureNominal|
DORA ReadinessCompliant|
Edge Nodes47 / 47|
Global Trust Index99.8%|
EU Regulatory SyncActive|
Network Latency12ms|
Uptime (90d)99.997%|
Threat PostureNominal|
DORA ReadinessCompliant|
Edge Nodes47 / 47|

Secure engineering & AI

Secure software and AI delivery, prototype to production.

We help teams design and ship software — including AI-enabled products — with the architecture, data governance and operational controls regulated environments require.

When this matters

Secure delivery becomes a board issue when an AI prototype is heading to production, customers start asking about data use, or security architecture concerns are slowing release.

What we cover

Scope of work

Secure architecture

  • Reference and target-state architecture
  • Threat modelling against real workflows
  • Identity, data and trust boundaries
  • Security-by-design review

AI product delivery

  • Prototype to production pathway
  • Model, prompt and evaluation strategy
  • Cost, latency and reliability trade-offs
  • Human-in-the-loop where it matters

RAG & internal knowledge systems

  • Retrieval-augmented generation design
  • Source authority and freshness
  • Access control on knowledge sources
  • Hallucination and citation handling

Secure SDLC & DevSecOps

  • Branching, review and release model
  • SAST, SCA, secrets and IaC scanning
  • Pipeline hardening and provenance
  • Vulnerability triage and SLAs

Data protection by design

  • Data minimisation and classification
  • Encryption at rest and in transit
  • Tenancy and access boundaries
  • Regulator-aware data flows

Code quality & maintainability

  • Targeted refactoring roadmap
  • Test strategy and coverage focus
  • Architectural seams and module boundaries
  • Technical debt that actually matters

What good looks like

Engineering keeps shipping, and security, evidence and AI governance are part of how delivery works — not added on later.

  • Architecture diagrams that match what is deployed
  • A secure SDLC the team will keep using
  • An AI approach grounded in evaluation, not slogans
  • A maintainable codebase with a credible improvement path

Common red flags

Patterns we see most often.

  • AI prototypes moving to production without data governance
  • Secrets, keys or PII handled inconsistently
  • No clear secure SDLC or change evidence
  • Threat modelling done once, never revisited
  • Pipelines without security gates
  • Architecture decisions not written down

Next step

Talk to Bergson about this work

Most engagements start with a short call to understand the deadline, the team and the constraints.