Embedding AI in an existing product
Adds intelligent features to your live platform without destabilising it.
Services / AI Integration & Platform Engineering
Production foundations
AI Integration & Platform Engineering
The engineering under the intelligence: APIs, data pipelines, evaluation, security, cost control and monitoring.
Typical stack
AWSModel routingObservabilityMCPCI/CD
The approach
Built around
Built around
the outcome.
Agents only create value when they are properly embedded. We build the integration layer, model routing, caching, prompt and eval infrastructure, observability and cost controls that keep an AI system dependable as usage grows.
- Predictable latency and spend as volume increases
- Regression-tested prompts and tools, not untracked changes
- Clear data boundaries and access control by design
Scope, model choice and hosting are decided with you against your data-sensitivity and cost constraints. We will recommend the simplest architecture that meets the quality bar.
Use cases
Where this
Where this
gets applied.
Each of these has a measurable baseline we can benchmark an agent against before you commit to a production build.
Model routing and cost optimisation
Sends each task to the cheapest model that passes your quality bar.
Evaluation and monitoring
Continuous scoring, drift alerts and dashboards for live agent quality.
Legacy system bridges
Gives agents safe, governed access to systems with no modern API.
Related services
All services
Often built together.
Custom AI Agents
Goal-driven agents that plan, call your tools and complete real multi-step work with human oversight.
ExploreVirtual Sales & Support Agents
Always-on digital agents that answer, qualify, book and resolve across WhatsApp, web, voice and email.
ExploreRAG & Knowledge Systems
Retrieval systems that answer strictly from your approved documents, with citations and no invention.
Explore