$ ai --to-production
AI Engineering & LLM Integration
Put language models into production safely — server-side credential custody, retrieval over your own content, tool-layer authorisation and cost ceilings.
The risk in an AI feature is no longer an inappropriate sentence. Once an agent holds tools it can spend money and change state, so authorisation has to move from the output filter to the action itself.
How we build them
- Credentials held server-side only — the browser talks to your service, never to the model provider
- Retrieval combining keyword and semantic search, because pure vector search misses exact identifiers
- Tool calls authorised in code against the end user's identity, never on the model's say-so
- Rate limits per IP, per session and globally — the global ceiling is what stops a prompt-injection loop becoming a five-figure invoice
- Evaluation in CI, so a prompt change is testable rather than shipped on instinct
System prompts are guidance, not a security boundary. We design so that a fully compromised prompt still cannot trigger an action the authorisation layer would refuse.
Ready to talk ai engineering & llm integration?
Bring your current setup — we'll bring a migration path.