Most LLM features die in production, not in the demo. The difference is evaluation.
Retrieval, extraction and agents — engineered for accuracy under real usage, with the numbers in front of you from week one.
Pricing, upfront
Four situations that usually start the conversation.
The prototype nailed hand-picked questions, then failed on real documents.
Our fix: an evaluation set built from your own data.
Ticket volume is rising and hiring is frozen.
Our fix: an assistant scoped as a measurable project with a payback date.
Staff paste sensitive contracts into public chatbots.
Our fix: a private deployment with access controls.
Leadership wants “AI” shipped fast, with no baseline.
Our fix: narrow it to one use case with a success metric.
What you get, and how long each piece takes.
Estimate this build| Deliverable | Typical time |
|---|---|
| Use-case scoping with success metric and kill threshold | 1–2 weeks |
| Evaluation dataset and scoring harness from real data | 2–3 weeks |
| Working RAG or agent pipeline behind a versioned API | 4–8 weeks |
| Production hardening: guardrails, fallbacks, cost & latency monitoring | 2–3 weeks |
| Handover with eval dashboard and prompt-change review process | 1 week |
Correctness first. Complexity only when the numbers ask for it.
Define correctness
100–300 real examples with expected outputs. Scoring rules agreed before a single prompt.
Simplest passing pipeline
Retrieval plus a strong base model before fine-tuning. Add complexity only when evals justify it.
Flag, measure, widen
Release to a slice of users with accuracy, latency and cost dashboards. Widen on metrics, not the calendar.
The first two to three weeks show nothing demo-worthy. That’s the evaluation set being built — and it’s why the feature still works in month six.
Questions buyers ask us.
Retrieval first in most cases. Fine-tuning when evaluation shows retrieval has plateaued.
Fixed-price phase one at $8k–15k. A production RAG feature is quoted at $30k–80k after phase one.
Reported from week one of testing. Most features land at $0.01–0.10 per request.
Yes — region-pinned endpoints (US or EU) or self-hosted open-weight models, with any accuracy trade-off shown.
Grounded answers, citations, refusal on empty retrieval and human review of low-confidence answers. We target a measured error rate, not zero.
Your team, or us under a support agreement. The eval harness is always handed over.
Stack: Python · Node.js · React · Postgres/pgvector
Start here
Describe the AI feature you need
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