GenAI Pilot Build. @genai-pilot-build

GenAI in production needs engineering, not a handoff. The GenAI Pilot Build takes one LLM use case — document intelligence, a conversational interface, content generation, or workflow automation — and ships it as a production pilot: connected to your data, governed, and measured against defined evaluation criteria.

The build is model-agnostic. We select against your requirements from the current frontier — OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral — with retrieval on Pinecone, Weaviate, or pgvector, and compliance and security built in, not bolted on.

Use-Case & Data Audit

Requirements, data sources, and compliance constraints are audited up front, so the pilot is built on data you can actually use.

Model Selection

An objective model choice against quality, latency, cost, and data-residency requirements — with the reasoning documented, not asserted.

RAG & Integration Build

A retrieval pipeline over your knowledge base plus integration into the workflow where the use case lives — the pilot runs where the work happens.

Production Pilot with Evals

The pilot ships with an evaluation harness: accuracy and hallucination tracking, latency monitoring, and token cost visibility from day one.

1. Scope — Pick the use case, audit the data and compliance constraints, and fix the evaluation criteria the pilot must meet.
2. Build — Model selection, RAG and integration build, and prompt engineering in two-week sprints with metric tracking throughout.
3. Handover — The pilot goes live to a defined user group, with eval results, runbooks, and a scale/iterate/stop recommendation.

The GenAI Pilot Build is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Which models do you work with?

We are model-agnostic: OpenAI (GPT-4o, o-series), Anthropic Claude, Google Gemini, Meta Llama, and Mistral, plus open-weight deployments where data residency requires it. The pilot documents why its model was chosen.

How do you handle hallucinations?

With engineering, not hope: retrieval grounding, an evaluation harness that tracks hallucination and accuracy rates, and guardrails on what the system may answer. The eval results are part of the handover.

What happens after the pilot?

The failure mode for GenAI in production is rarely a bad build — it is abandonment. To keep the system monitored, evaluated, and current through model updates, add the Model Ops Retainer to your plan backlog.