A corporate coaching provider matched clients to coaches entirely by hand — reviewing enquiries, interpreting needs, and searching profiles across language, seniority, location, and topic. The process was slow, inconsistent, and constrained by strict data privacy requirements.

We built a privacy-aware matching engine combining semantic search, RAG-based retrieval, keyword scoring, and proximity signals. The system surfaces ranked coach shortlists from unstructured, multilingual client inputs — with every match explainable and reviewable by a human operator.

Matching time dropped from 7 days to seconds. Match quality became measurable for the first time via rematch rate, acceptance rate, and override rate. The operations team shifted from manual searching to expert review.

Onboarding new clients onto a complex software platform required teams to manually read through PDFs, spreadsheets, emails, and discovery call notes — then interpret requirements, identify gaps, and decide what to configure first.

We built an AI system that ingests raw client documents across formats, extracts structured requirements, identifies missing information, and maps each requirement to the correct system action — generating a dependency-aware execution plan.

Manual effort to prepare a client setup plan dropped from days to hours. Missed steps and rework were eliminated, the client experience improved, and the system created a scalable foundation for automating repeatable onboarding workflows.