AI Integration
Connecting language models to the systems you already run, with typed contracts, evaluation harnesses and scope controls from day one.
AI integration is a systems problem, not a prompt problem
The hard part of AI integration is almost never the model. It is everything around it: getting clean data out of a system that was never designed to give it up, making the output structured enough for the next service to consume, and knowing what to do on the day the provider has an outage.
We treat every model call as an integration point with a contract. Inputs are validated, outputs are parsed against a schema, failures have a defined fallback, and every call is traced with its scope attached. That is ordinary distributed-systems discipline applied to a component that happens to be probabilistic. AI data integration is usually the harder half: the model is ready long before the records are clean, permissioned and reachable.
Most engagements start with one high-value flow, prove it against your own data, then widen. If you are integrating AI into an existing product, our Claude API integration guide covers the same patterns we use in production.
Teams that start here often pair it with Claude API integration, OpenAI integration and API integration.
A model that is right 95% of the time is a great feature and a terrible dependency. The integration layer is what turns one into the other.
Where AI integrations usually go wrong
Four failure modes we see in almost every rescue project.
The model returns prose where the next service expects a record, so the integration is held together by regular expressions that break on the first unusual response.
A single vendor and a single model, so a rate limit or a deprecation notice becomes a production incident with no route around it.
Every call goes to the largest available model because nobody measured which steps actually need it, and the bill grows faster than the usage.
No evaluation set, so nobody can tell whether a prompt change improved things or quietly broke a case that mattered.
What an AI integration engagement covers
The plumbing, the guardrails and the tooling to keep it healthy after we leave.
Clean, typed access to the records the model needs, including the joins and permissions work that existing systems usually make painful.
Every response is constrained to a JSON schema and validated before it moves on, so downstream services never parse prose.
Cheap models handle high-volume, low-stakes calls and a frontier model handles the hard ones, with automatic failover between providers.
A held-out set of real cases with expected outcomes, so prompt and model changes are measured rather than eyeballed.
Token spend broken down per feature and per customer, surfaced on a dashboard before it turns into a surprise invoice.
PII handling, prompt-injection defences and data-residency decisions documented for whoever signs off on the integration.
From first integration point to production through controlled rollout
One flow proven end to end before we widen the surface area.
We map the flow, the data it touches and the systems it must not break, then agree what a correct output looks like in writing.
Schemas, error semantics and fallbacks are defined before a single prompt is written, so the integration has a shape to build against.
Weekly increments against your real data in a sandbox, with the evaluation set growing every week from cases the model gets wrong.
We measure accuracy, latency and scope per call against the held-out set and tune the routing until all three are acceptable.
Live behind a feature flag on a slice of traffic, widened as the traces and the scope dashboard hold steady.
The tools behind a reliable AI integration
Provider-agnostic by design, so a commercial setup change or a deprecation is a config edit rather than a rewrite.
Multiple providers behind one interface, with routing by task complexity rather than habit.
Typed schemas on both sides of every call, validated before anything is persisted.
Idempotent writes into the systems of record, so a retry can never double-post.
Traces, evaluations and scope in one place, per feature rather than per account.
What this looks like in production
Adding AI to a product without destabilising it
A B2B SaaS platform wanted AI summaries and classification inside an existing product with thousands of paying seats. The first internal attempt returned free text, had no evaluation set and had already caused one incident when a malformed response was written straight into a customer record.
We put a contract around every call: constrained JSON output, schema validation before persistence, and an idempotency key on every write. Classification moved to a smaller, cheaper model, with the frontier model reserved for the summarisation step where quality was visible to users.
The evaluation harness became the thing the team valued most. Prompt changes stopped being a gamble and started being a pull request with a measurable diff.
What an AI integration services partner should own
We build the layer around the model with the same care as any other production dependency, because that layer is what decides whether an AI feature is an asset or an incident.
Schemas, fallbacks and error semantics are agreed before prompts are written, so the integration has a defined shape rather than an emergent one.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
