AI integration services in London
Calling the model is the easy bit. AI integration services are the rest: retries, timeouts, fallbacks, latency, cost, prompt versions, and getting the model onto your real data — so the answer still shows up on a Tuesday afternoon. If the feature itself is not designed yet, that is AI software development, not this page.
This is for teams with a working app and a model that already answers. You need AI integration that still works during an outage, when a provider raises prices, and when the invoice lands in a new layout.
The 95% past the API call- Fallback, routing, caching by default
- Connected to your real data, not test data
- Prompt versioning, eval gates, monitoring
When the demo is fine and production is not
The model is rarely the blocker
Wiring identity, data access, rate limits, and failure modes into your existing product is. That is ordinary software engineering with sharper edges.
Private data paths
We keep prompts, logs, and documents inside agreed boundaries, and make it obvious what leaves your VPC and what never should.
You keep ownership
Clear interfaces, docs, and monitoring so your team can run it. No mystery black box living only in our laptops.
You do not need us for…
Dropping a vendor SDK into a side page does not need us. Call when the feature sits in a core user journey.
How we wire a model into production
Discovery and Integration Audit (Weeks 1-2, fixed-fee from £2,000)
We map the integration surface: your existing product architecture, the data the AI needs, the latency and cost budget, the regulatory frame, the SLA the feature has to hit. Discovery includes a paid PoC on your real data when integration risk is high. We test the obvious pre-built option (raw OpenAI API, Vision API, Textract) on your data first.
You receive: integration architecture (model routing, fallback chain, RAG layer if needed), prompt management plan, fixed-scope build quote and a projected monthly run rate.
Build the Production Plumbing (Weeks 3-8+)
Build runs as a small senior team led by Michal Vavra, embedded with your product team. We build the prompt registry, eval pipeline, model routing layer, retrieval layer, fallback chain, monitoring stack and the integration into your existing product all together.
Each release ships behind feature flags with eval gates in CI, rollback on regression, and shadow runs against real production traffic before any user sees output.
Integrate, Evaluate, Harden
We wire the AI into your real product surface. Auth propagated, rate limits respected, latency budget validated end-to-end, cost telemetry live, output validation against typed contracts. Then we run a hardening pass: how does this fail when the model is bad, when the API is slow, when the data is stale, when a user's prompt is hostile.
The integration ships when it survives the hardening pass, not when the demo works.
Launch, Monitor, Iterate (optional retainer)
At launch we wire in cost-per-user telemetry, output quality monitoring, drift detection on the metric that matters, exception-queue tracking and a feedback loop from human review back to eval. AI features age. We size the retainer to keep yours from quietly degrading at month six.
Monthly retainer for monitoring, prompt tuning, model updates, on-call. Take it in-house whenever you're ready.
What AI integration services cost
Frequently Asked Questions
Direct answers to the questions CTOs and Heads of Product ask us on every scoping call.
If your use case is a single API call in a request handler, wire it up yourselves and skip us. We're the right call when the integration needs retry logic, multi-provider fallback, prompt versioning, model routing, prompt caching, cost monitoring, latency budgets, RAG over your real data and an eval pipeline that catches regressions before users do. The 95% past the API call. The reason your demo isn't a product yet.
Usually yes. Practical patterns: API wrappers where there are APIs, middleware layers where contracts are messy, reverse-proxy interception where the system has no API, data federation across systems holding the same entity four different ways. We've done this on systems that 'shouldn't have an API' and made it work. We'll tell you in discovery if the integration cost outweighs the AI value, which sometimes it does.
Multi-provider fallback designed in from day one. Primary on OpenAI, secondary on Anthropic, tertiary on a self-hosted open model where the use case allows. Health-checked with circuit breakers, automatic failover, alerts when fallback engages, cost telemetry sliced by provider. When Claude has its bad afternoon, your users keep working.
Three layers. Model routing (cheap or fine-tuned small model for high-volume routine work, frontier only when needed). Prompt caching (repeated context cached for up to 90% reduction). Cost telemetry per user, per feature, per provider, with hard caps and alerts. Most teams leave 60-90% of cost savings on the table by skipping the first two. We design them in.
We ship a versioned prompt registry, eval pipeline gating prompt changes in CI, A/B testing infrastructure for prompt variants and rollback on regression. Prompts go through the same release process as code. The PM can iterate; the production prompt that's actually running is always known and rollback-able.
Discovery: fixed-fee from £2,000. Small focused integrations (single feature, single provider, modest data layer): from £10,000. Mid-market production builds with RAG, prompt management and multi-provider fallback typically land £25,000-£100,000. Run rate is flat monthly plus inference cost. We quote both upfront so the run-rate isn't a surprise at month six.
Yes. Standard contract is full IP ownership: source code, prompts, prompt registry, infrastructure-as-code, eval datasets, monitoring dashboards, runbooks. No rented layer. We document so it can be handed to your team or another vendor at any point. The only reason you stay is because the work is good.
Related
Related AI work
If the model is not chosen and the UX is not designed, start with AI software development. This page is the plumbing once you know what to ship.