AI Agents

Context

Built for logistics 3PLs within the Smart Quoting stack and used by Pricing / Analytics teams, this agent closes a gap most organizations live with: there is no real closed loop on price performance. Quotes go out, some turn into shipments, some don’t, margins drift up or down—but the learning stays scattered in spreadsheets and people’s heads instead of flowing back into pricing guidance.

What it does

The agent learns from outcomes to improve pricing guidance over time. It looks at how quotes and shipments actually performed by lane, customer, product, and segment—whether deals were won or lost and what margin was realized—and compares those outcomes against the guidance and guardrails that were used when the quote went out. From that feedback, it adjusts the pricing signals that feed quoting: which ranges look too aggressive, which look too soft, and where your current guidance consistently underperforms or leaves money on the table. The result is that, over time, pricing guidance reflects how the market and your own book actually behave, not just how they were expected to behave when rules were first set.

Core AI functions

The core capability is feedback learning. The agent ingests historic quote and outcome data, detects patterns in where guidance and reality diverge, and uses that feedback to refine the signals and parameters that drive pricing decisions. Instead of one-off analyses and static rule updates, it creates a continual learning loop where each batch of outcomes nudges guidance in a better direction.

Problem solved

Today, most pricing environments push out guidance but never systematically pull outcomes back in. Teams talk about “how that lane feels” or “what usually works”, yet there is no consistent mechanism to compare expected versus realized margins and win rates and update guidance accordingly. That means mistakes repeat: some segments are habitually underpriced, others overpriced, and no one has a clean, data-driven way to show it. The agent turns that missing loop into a standing capability.

Business impact

The primary impact is improving margins over time. By continuously learning from actual price performance, the agent helps steer guidance away from structurally weak spots and towards a healthier balance between competitiveness and yield. That doesn’t require a big bang change—small, evidence-based adjustments accumulate across thousands of quotes, tightening discipline and supporting a more profitable mix of business without slowing sales down.

Integration and adjacent use cases

The agent typically needs access to quote and shipment outcomes and realized margin data from your pricing, TMS, and analytics environment, and a way to feed updated pricing guidance or parameters back into the tools used for day-to-day quoting.

Common combinations in this stack:

• Market Data Agent to provide the consolidated view of lane rates and carrier pricing that contextualizes how your performance sits versus the market, and

• Quote Optimization Agent to apply the improved guidance when computing margin-aware, competitive rates on each new quote.