Build · Digital Products
AI integration
Practical, governed use of large language models and machine learning inside existing services, with privacy and evaluation designed in.
The challenge
Most institutions now have more AI ideas than they can safely deliver. Pilots appear quickly, but few reach production because questions about data protection, accuracy, accountability and cost were not answered at the start.
Integrating a model is the easy part. The harder work is deciding which data it may see, measuring whether its answers are good enough, preventing prompt injection and data leakage, and giving staff a clear way to check and override what it produces.
We integrate AI into real services as a governed engineering capability: scoped use cases, evaluation sets agreed with the business, guardrails in code, and human oversight where the stakes require it.
Our method
How the work is done.
- 01
Use-case triage
Assess candidate use cases for value, feasibility, data sensitivity and risk, and select those suitable for production.
- 02
Data and privacy design
Define what data the model may access, where it is processed, retention rules and redaction, supported by a data protection impact assessment.
- 03
Evaluation first
Build an evaluation set and acceptance thresholds with subject-matter experts before choosing a model or prompt design.
- 04
Build with guardrails
Implement retrieval, prompts, tool use and output checks, with defences against prompt injection and sensitive data disclosure.
- 05
Operate and monitor
Track quality, cost and drift in production, with feedback loops and clear escalation to human reviewers.
Deliverables
What you receive.
- Use-case assessment and prioritised AI roadmap
- Data protection impact assessment input and data flow diagrams
- Evaluation datasets, acceptance thresholds and evaluation harness
- Production integration with retrieval, guardrails and audit logging
- Model and prompt documentation describing intended use and limitations
- Quality, cost and usage monitoring dashboards
- Human oversight and incident procedures
Engagement options
Ways to buy it.
- 013–5 weeks
AI readiness assessment
Use-case triage, data review and governance gap analysis with a recommended first use case.
- 022–4 months
Pilot to production
Delivery of one use case through evaluation, build and controlled rollout.
- 032–3 months
AI platform foundations
Shared gateway, evaluation tooling, logging and policy so multiple teams can build safely.
Standards
Frameworks we work to.
- ISO/IEC 42001
- NIST AI Risk Management Framework
- OWASP Top 10 for LLM Applications
- ISO/IEC 23894
- ISO 27001
Questions
What buyers ask us.
Will our data be used to train someone else's model?
Not if it is designed correctly. We select deployment options and contractual settings that exclude your data from provider training, and we document where data is processed.
Which model should we use?
The one that passes your evaluation set at an acceptable cost and within your data residency constraints. We test candidates against the same evaluation rather than choosing by reputation.
How do you stop the system giving wrong answers?
No system eliminates error entirely. We measure accuracy against agreed thresholds, ground answers in your own sources where appropriate, and keep a human in the loop for consequential decisions.
Can AI run within our own infrastructure?
Yes, where open-weight models meet the quality bar. We assess the trade-offs in capability, hosting and operations with you.
Related services
Often delivered together.
Build
API & systems integration
Connecting core systems, legacy databases and third-party services safely, observably and without fragile point-to-point links.
Secure
Governance, risk & compliance
Gap assessments, risk registers, policies and audit readiness against ISO 27001, SOC 2, PCI DSS and NIST CSF, built to be used rather than filed.
Train
Secure development
Practical training for developers on writing, reviewing and testing code that withstands real attacks and passes security review.
Discuss ai integration.
A senior engineer reviews every enquiry and replies within one business day.