Win-Rate Booster for Complex Freight RFPs

Large enterprise and mid-market logistics sellers respond to complex RFPs with long sales cycles, inconsistent pricing, and manually assembled proposals. That creates lost deals, margin leaks from ad-hoc discounts, and high overhead from spreadsheet-based modeling and legal/ops rework.

Build a decision-support system that combines predictive ML for win probability and price elasticity with grounded generation for proposal content, integrated into the sales workflow and controlled by human approvals.

• Train a tabular model (e.g., gradient boosting + feature engineering) on CRM, TMS/visibility, CPQ, historical bids, route characteristics, capacity/cost signals, and customer behaviour to predict win probability and price sensitivity.
• Produce actionable outputs: win score, recommended price band and concession levers, margin impact, and counterfactual «what-if» scenarios for different pricing or service levels.
• Integrate into CRM/CPQ UI so sellers can simulate offers, see tradeoffs, and auto-generate proposal text and negotiation scripts via a grounded LLM that references contract templates and compliance rules.
• Keep humans in the loop: sales manager approvals for below-threshold margins, ops validation for feasibility, and a feedback loop to capture outcomes for retraining.
• Governance: data lineage, explainability for each recommendation, monitoring of model calibration and business KPIs, and scheduled retraining (e.g., monthly/quarterly) plus guardrails for regulatory or contract constraints.

Illustrative impact: targeted RFPs typically see a 5-15% lift in win rate and a 0.5-3 percentage-point improvement in negotiated margins (or ~1-4% relative margin uplift), while proposal preparation time falls 30-60%. Time-to-value is often 3-6 months for a focused pipeline segment, with continuing gains as models and playbooks are refined.