Automated Deal Desk Pricing in Logistics

AI-driven deal desk pricing ingests live TMS data, fuel indices, and carrier costs to generate real-time, margin-aware pricing recommendations for every inbound RFQ in under 2 minutes. The system models your actual freight cost structure - driver utilization, detention risk, FMCSA constraints, drayage complexity - and recommends floor and target pricing before your sales team finishes reading the quote. It learns your lane profitability, seasonal patterns, and customer-specific risk profiles, continuously improving accuracy. Unlike static rate cards, the AI adapts pricing to fuel volatility, capacity constraints, and competitive pressure, so you stop leaving margin on the table or pricing yourself out of winnable loads.

Yes. We isolate your data environment and integrate directly with your Oracle TMS, MercuryGate, or Blue Yonder instance via secure API. HAZMAT, C-TPAT, and customs data stays inside your environment and is surfaced to your compliance team for review - a price flag is never treated as a compliance clearance. Your pricing logic and margin assumptions remain proprietary - the AI learns your costs, but your data never leaves your infrastructure.

Plan for a working system inside the first 100 days: weeks 1-3 involve data mapping and TMS integration; weeks 4-6 focus on model training using your historical freight data and cost assumptions; weeks 7-9 include pilot testing with your deal desk team and refinement; weeks 10-14 cover full rollout and handoff. A rollout like this is scoped to show measurable results - faster quotes, improved pricing accuracy, and margin gains - within 60 days of go-live. By month 4, the system has learned enough seasonal and lane-specific patterns for recommendations to firm up on your core corridors, with a target of 30-40% more loads bid per rep.

By day 30, the system is connected to your core platforms and shadowing real workflows so your team can validate accuracy against existing decisions. By day 60, it's running in production for a defined slice of work with humans reviewing outputs and a measurable baseline against pre-deployment metrics. By day 90, you have production-grade adoption: your team is operating from the system's outputs, you have a documented accuracy and exception-rate baseline, and you've decided which next slice to expand into. A rollout like this is scoped to show meaningful operational impact between day 60 and day 90, with full ROI realization in months 6-12 as the model learns your specific patterns.

Firms under $10M in revenue, or teams where the volume is still low enough for one person to handle comfortably - at that scale the math rarely clears, and we will say so. This is built for Logistics firms of 50-500 people where the work is real enough that the default fix would be another process hire. If you are not sure which side of that line you are on, the free AI Opportunity Assessment will tell you.