Services/Scale
AI Integration & API Development
APIs and Model Context Protocol servers that connect models to your data, tools, and business logic.
- 4 to 10 weeks
- Fixed price, or time and materials
- Backend engineers with a platform lead
- Teams adding AI capabilities to existing applications
What we do
We build the integration layer that makes AI available across your organisation: production APIs, MCP servers, and the middleware that connects models to CRM, ERP, warehouses, and internal systems with authentication, rate limits, and documentation.
What the work includes
Production-grade APIs
Versioned, documented, rate-limited endpoints with the error handling and observability an internal platform team would expect.
Model Context Protocol
MCP servers that expose your data sources and tools to any compatible model or agent runtime, so context sharing and function calling are standardised across teams.
Enterprise system integration
Webhooks, event-driven sync, and middleware between models and your CRM, ERP, and data warehouse.
Security by default
OAuth 2.0, scoped keys, audit logs, least-privilege access, and data handling designed for enterprise security review.
In production
Data platform exposed through SQL and REST
A medallion data platform with 500+ connectors, exposed to agents and analysts through governed SQL and REST interfaces, delivered for an enterprise supply-chain pilot.
See our enterprise experienceHow an engagement runs
Scope, build, evaluate, operate.
The same four stages on every engagement, each with a defined output.
Scope
A short discovery with the people who own the problem. Output: a written scope, acceptance criteria, and a fixed estimate.
Build
A named lead and a team sized to the scope. Working software from the first weeks, demonstrated on a fixed cadence.
Evaluate
Every AI component is measured against real cases before rollout. The numbers decide when it goes live.
Operate
Deployment, monitoring, and a support window. Then a handover, or an ongoing team if you want one.
Typical stack
Tools we commonly use for this work. The final choice follows your requirements, region, and existing platform.
- Python
- FastAPI
- OpenAI
- Anthropic
- PostgreSQL
- Redis
- Docker
- OpenAPI
- GraphQL
Integration
AI as an interface your teams can call
Through REST or MCP, models reach your systems through governed, documented interfaces instead of one-off scripts.