Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.
Web or in-app surface (Next.js / React Native / Slack / Teams) with auth, role-based access, and audit logs.
Versioned prompts, golden test sets, regression evals on every PR — so model upgrades don't silently break behaviour.
Routing across OpenAI / Anthropic / Gemini / Bedrock / open-weight, with caching, retries, fallbacks, and cost guardrails.
Per-request traces, token spend, hallucination flagging, user feedback loop — wired into Datadog / Honeycomb / your stack.
Lock the user surface, the eval criteria, the latency / cost budget, and the failure-mode catalogue. No code yet.
Iterate on prompts, retrieval, and UI in parallel — daily evals, weekly demos, your team in the loop.
Load testing, red-teaming, SOC-2 / ISO checks, runbooks, and the on-call handoff to your ops team.
Optional retainer — model upgrades, drift monitoring, and quarterly cost-optimisation passes.
Best-in-class where it matters; boring and battle-tested everywhere else.
Cost depends on surface count, model selection, and integration depth. Scope-locked SOW, milestone-paid, 90-day post-launch warranty. Cloud spend is passthrough at cost.
It depends on the task — we'll route across models and pick per-call. Frontier models (GPT-5, Claude Opus) for high-stakes reasoning; smaller / open-weight for high-volume cheap stuff. The router is part of the deliverable.
Usually no. RAG + a strong base model beats fine-tuning for 90% of use-cases now. If genuinely needed, we'd engage our Fine-tuning service separately.
Yes — we deploy in your AWS / GCP / Azure account, or on-prem with vLLM / TGI. Used in BFSI and government scopes.
We can. If not, we'll work to your Figma. We don't ship undesigned admin-panel UIs.