Calling an LLM API is the easy 5% of the work. The other 95% is retry logic, timeout handling, fallback chains, latency budgets, cost monitoring, prompt versioning, error handling and connecting to your real data. We build the production infrastructure around AI calls so the feature you ship inside your existing product actually keeps working.
Pixelfield is for CTOs, Heads of Product and VPs of Engineering at companies with a working app, platform or workflow that needs AI inside it, not rebuilt from scratch. The seniors who scope the work also write the code. 50+ AI features in production, fastest deployment two months, and a reputation for telling you when the OpenAI API on its own is enough.
The 95% past the API callFallback, routing, caching by defaultConnected to your real data, not test dataPrompt versioning, eval gates, monitoring
Every AI integration starts with 'this is amazing in the API playground'. Then real traffic hits, the model has its bad afternoon, the prompt gets longer, the cost climbs, and a user sees a 60-second blank screen. We design for the unhappy path first: retries with backoff, multi-provider fallback, latency budgets, cost caps, graceful degradation. The demo's job ended at 'amazing'. Production's job is reliability.
OpenAI's docs show you the API call. They don't show you how to version prompts, route between models, cache repeated context, propagate identity through retrieval, evaluate outputs in CI, monitor cost per user, fall back when a provider goes down. The 95% past the API call is where production AI lives. We build it as part of the integration, not after the first incident.
The model's only as useful as the data it can see. Your Postgres updated five minutes ago, your data warehouse synced last hour, your CRM was edited this morning. If your AI is reasoning over yesterday's snapshot, the answers are confidently wrong. We build the data layer with freshness SLAs, hybrid retrieval, source lineage and access controls so the AI sees what your users see.
If your use case is a single API call wrapped in a loop, wire it up yourselves. If your team can run a model registry, eval pipeline, prompt versioning and on-call without breaking, fine. Hire us when the integration has to be production-grade against a real product, real traffic and a real SLA. We'll tell you on the scoping call if you don't need a build.
Related AI cluster services
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 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.
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.
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.
Direct answers to the questions CTOs and Heads of Product ask us on every <strong>scoping call</strong>.
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 £2K. Small focused integrations (single feature, single provider, modest data layer): from £10K. Mid-market production builds with RAG, prompt management and multi-provider fallback typically land £25K-£100K. 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.
