Enterprise Research Copilot Case Study
What it took to ship a research copilot for 40k analysts—stack choices, evaluation loops, and adoption tactics.
A global research organisation asked us to build a copilot for 40,000 analysts working across 18 markets. The goal: collapse research time without leaking privileged knowledge.
Architecture Decisions
We paired OCI for sovereign workloads with Azure OpenAI for specific language coverage. Data landed in a governed data mesh; embeddings and generations ran inside isolated tenants.
Evaluation & Safety
- Factual accuracy, citation coverage, and jurisdictional compliance scored automatically.
- Human reviewers provided sentiment and usefulness ratings that fed the data flywheel.
- High-risk topics triggered escalation to domain experts before responses were released.
Change Management
We trained champions in each region, shipped quick-reference guides, and instrumented in-product feedback buttons. Adoption jumped once analysts saw their own reports reflected in answers.
Outcomes
- Research time dropped from 52 minutes to 11 minutes per query.
- Inference spend decreased 63% after the first two flywheel iterations.
- Regulators received a full control dossier with architecture, evaluations, and access logs.
The copilot now processes 80k+ monthly queries and drives roadmap priorities through live metrics—not guesswork.