AI Agents

Context

Built for logistics 3PLs within the Smart Quoting stack and used by Pricing and Sales teams, this agent focuses on the core decision in every spot quote: what rate to offer for a given shipment. Its role is to compute margin-aware, competitive prices for each shipment so sales doesn’t have to rely on gut feel and scattered spreadsheets.

What it does

The agent takes the key inputs already available in your quoting flow—shipment characteristics, lane and distance, service requirements, cost baselines, and current commercial guardrails—and produces a suggested sell rate for that shipment. For each quote it balances competitiveness (what is likely to win on this lane and profile) against your minimum and target margins, so the user sees a recommended rate plus a small range they can move within. The final decision stays with Pricing or Sales, but the starting point is no longer a blank cell.

Core AI functions

The core capability is optimization plus prediction. The agent uses predictive models to estimate win probability and margin outcomes at different price points for a given shipment, then searches across those options to find one that meets your margin requirements while remaining competitive. It standardises this logic so every quote is evaluated against the same objectives and constraints, rather than each rep improvising their own approach.

Problem solved

Manual pricing on spot quotes is slow and risky. Without a structured optimizer, some quotes are set too high and lost unnecessarily, while others go out too low and erode margin—especially under time pressure or on unfamiliar lanes. This agent replaces that ad hoc decision with a consistent, data-driven recommendation that explicitly trades off win chance and margin, reducing the likelihood of both lost bids and bad deals.

Business impact

The primary outcome is higher win rate with healthier margins. Sales can respond faster because they don’t have to assemble a pricing view from scratch, and management gains more confidence that quotes are aligned with commercial strategy instead of drifting lane by lane. Over time, this supports a more disciplined pricing posture across the portfolio while still letting frontline teams move quickly.

Integration and adjacent use cases

Integration focuses on plugging into your existing quoting and pricing environment: the agent reads shipment details, cost baselines, and market inputs from your TMS, pricing tools, and rate sources, and writes suggested rates and ranges back into the same quote screen—no core replacement required.

Common combinations in this stack:

• Market Data Agent to provide a unified, up-to-date view of lane and carrier pricing that the optimizer can use as a reference, and

• Margin Intelligence Agent to learn from win/loss and performance data over time and feed improved guardrails and response curves into the optimization logic. Outside the stack, common adjacent use cases include contract pricing and promotions, where the same optimization engine can be applied to longer-term agreements and targeted pricing campaigns.