AI that increases throughput without increasing risk
We've developed an AI-augmented delivery model designed specifically for organisations where governance, auditability, and long-term maintainability are non-negotiable.
The problem with uncontrolled AI adoption
For regulated enterprises, reckless AI adoption creates more risk than opportunity.
Compliance Exposure
Code generated without traceability or audit trails creates regulatory risk.
Quality Degradation
AI outputs that pass superficial review but harbour subtle defects.
Governance Gaps
No clear accountability when AI-generated code causes incidents.
Vendor Lock-in
Dependency on opaque AI services with unclear data handling.
Technical Debt
Rapid generation of code that no human fully understands.
Unsustainable Velocity
"Move fast and break things" doesn't work in regulated environments.
Our approach: Disciplined AI augmentation
AI should be treated as a new class of tooling—powerful, but operating within strict human-defined boundaries.
Formal Specification Before Generation
Every feature begins with explicit documentation: requirements, constraints, acceptance criteria, edge cases. This structured detail is captured before any AI agent is engaged.
AI does not decide what to build. Humans do.
AI as Augmented Team Members
AI agents operate as junior developers, senior reviewers, architects, QA engineers, and documentation specialists—but every output passes through human review.
Accountability remains with named individuals.
Boring Technology Choices
We deliberately avoid complexity. Monolith-first architecture, major cloud providers, simple auditable pipelines. No microservices unless there's compelling justification.
AI-generated code lands in systems humans can understand.
Complete Traceability
Every line of code traces back to a ticket. Every ticket traces back to a requirement. Every AI interaction is logged. Full audit trail for regulators.
The accountability that responsible engineering demands.
Business outcomes
Increased Capacity
A team of 3-4 senior engineers can sustain delivery velocity previously requiring 8-12 people.
Reduced Risk
Formal specifications and human review catch defects earlier. Fewer production incidents.
Lower Total Cost
Fewer people, but more senior. Less rework. Faster time-to-value.
Maintained Compliance
Full traceability and clear human accountability satisfy regulatory requirements.
Sustainable Velocity
Maintains pace over years, not sprints. Technical debt controlled. Teams don't burn out.
Enterprise Ready
Delivered for Deutsche Bank, NHS, National Grid, and other regulated organisations.
Learn more about our approach
Delivery Methodology
Specification standards, ticketing workflows, AI agent usage by phase, and human review checkpoints.
Read methodologyTechnology Choices
Why Python, Django, and pragmatic monoliths maximise AI development effectiveness.
Read technology guideGovernance & Risk Control
Data handling principles, access controls, logging and traceability, and risk assessment.
Read governance frameworkDue Diligence
IP ownership, vendor control, security, knowledge transfer, and timeline claims for procurement teams.
Read due diligence guideReady to build with confidence?
Let's discuss how our AI-augmented delivery model can help your organisation ship faster without compromising on governance.
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