Agentic Quotation Engine
An AI-driven quoting tool that drafts quotations from RFQs, learns each customer's price acceptance behavior over time, and surfaces margin opportunities the sales team would have missed.
The Challenge
A maritime logistics provider was losing deals to faster competitors and leaving margin on the table on the ones they won. Quote preparation meant a senior account manager manually pulling tariffs, fuel surcharges, port fees, and historical pricing from multiple systems — then making a gut-feel call on the markup. Customers waited days. Pricing was inconsistent. Nobody was learning from won or lost quotes.
- Quote turnaround averaging 2-3 days for routine RFQs
- Pricing decisions made on intuition, not data — significant variance between account managers
- No feedback loop from won/lost quotes back into pricing strategy
- Customers with high willingness-to-pay being underpriced by default
Our Approach
We built an agentic quotation engine that handles the entire quote lifecycle — from RFQ ingestion to final pricing recommendation — and continuously learns each customer's price elasticity from accepted and rejected quotes, so pricing gets sharper with every cycle.
RFQ Ingestion & Cost Modeling
Built ingestion pipelines that pull RFQs from email, portals, and forms, then automatically calculate the true cost basis — tariffs, surcharges, port fees, currency conversions — across every routing option in seconds.
AI-Drafted Quotation
Developed an agent that drafts a complete quotation document — line items, conditions, terms, validity — in the company's voice and format, ready for the account manager to review rather than write from scratch.
Customer-Specific Price Intelligence
Built a learning layer that tracks every quote outcome per customer — won, lost, renegotiated, accepted at price X — and models each customer's acceptance threshold over time. The system surfaces a recommended price with a confidence band, letting the team push margin where the customer will pay it.
Human Refinement Loop
Account managers stay in control — adjusting the AI's recommendation, overriding, or accepting. Every adjustment feeds back into the pricing model, so the system mirrors and amplifies the team's judgment instead of replacing it.
The Results
"The quote tool turned pricing from gut-feel into something we can actually measure and improve. We're winning more deals at higher margins — that's not a tradeoff we thought we could make."
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