Agentic selling

An offer used to go out as a bare number. Accepting it is a trust decision, and a number alone gives the customer nothing to trust — no comparables, no reasoning, no answer to "why is this fair?"

For every quote, the system researches comparable sales, uses an LLM judge to select the strongest comps, and runs a self-correcting generator that writes a pitch grounded in them — so each offer arrives with its argument attached, automatically.

1. 01Quote created (event-driven trigger)
2. 02Comparable research
3. 03LLM-judge comp selection
4. 04Pitch generation
5. 05Self-correction pass against the evidence
6. 06Pitch attached to the offer

• TriggerEvent-driven backbone (SQS + Lambda)
• ResearchComparable retrieval over sales data
• SelectionLLM-as-judge scoring
• GenerationSelf-correcting LLM pipeline
• RuntimePython on AWS

• Offer acceptance more than doubled in phase one: 11.5% → 26%
• Every quote ships with an evidence-backed pitch, no human in the loop