AI prototypes hardened for production.
You built a working prototype. We make it production-ready — evals, guardrails, observability, cost controls — and ship a system your customers can actually trust.
What's included
Evaluation harness
End-to-end eval suite with ground-truth datasets, automated regression tests, and pass/fail thresholds so you know exactly when the model breaks.
Guardrails & safety layer
Input/output validation, content filtering, prompt injection defence, and structured output contracts that prevent your LLM from going off-script.
Observability & tracing
Full request tracing with LangSmith, Langfuse, or custom dashboards. Every prompt, token count, latency, and model call logged and searchable.
Cost controls
Per-user and per-org spend limits, token budgets, caching layers, and model routing to keep inference costs predictable at any scale.
Structured output & retries
JSON schema enforcement, automatic retry with backoff, fallback model chains, and graceful degradation so user requests never silently fail.
Model version pinning
Lock model versions, track provider deprecations, and maintain a canary pipeline so a provider update never breaks production unexpectedly.
How it works
Prototype audit
We review your codebase: prompts, model calls, error handling, and deployment setup. Written risk report delivered within 5 working days.
Eval harness design
Co-design an evaluation suite with your team. Define success metrics, assemble ground-truth datasets, and wire up automated CI runs.
Guardrails & observability
Implement input/output validation, cost controls, distributed tracing, and alerting. Every LLM call becomes observable from day one.
Harden & deploy
Performance-test under realistic load, tune latency and cost, then ship to production with a runbook and incident escalation path.
FAQ
Ready to ship? Let's talk.
Free discovery call. No commitment. Written proposal within 48 hours.