Multi-agent orchestration
Specialist agents for query parsing, product configuration, pricing, and quote generation, coordinated through structured handoffs.
Case Study · B2B PROCUREMENT
Racepoint: Multi-agent LLM procurement
Natural-language B2B procurement.
The story
What Racepoint needed, and what we built.
B2B procurement is full of multi-step friction, parse the quote request, configure the right product, calculate dynamic pricing, generate a quote. Founder Melyssa Plunkett-Gomez saw the opportunity to collapse this into a natural-language AI workflow. KUMO is building Racepoint as a multi-agent LLM SaaS.
What we delivered
Six areas of production work.
Natural-language procurement UX
Procurement teams interact in plain language; agents handle the multi-step backend without exposing the orchestration.
Dynamic pricing engine
Inventory- and contract-aware pricing that adapts in real time to availability, terms, and customer-specific deals.
Automated quote generation
Quote document creation with the configuration, terms, and pricing context resolved, ready for review and send.
Per-agent eval suites
Evaluation harnesses per agent so regressions are caught in CI before they reach a customer.
Production AI architecture
Observability, fallback paths, and human-in-the-loop on edge cases, designed for production, not a demo loop.
Highlights
Project highlights.
More work.
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