AI Agents

Context

Built for logistics 3PLs within the Smart Quoting stack and used by Pricing and Sales Ops, this agent tackles the basic but painful question behind every quote: “What does the market actually look like on this lane right now?”. It sits at the front of the quoting flow, giving sales and pricing teams a single, reliable view of current lane rates, carrier pricing, and recent quoting history instead of a puzzle of scattered inputs and tabs.

What it does

The agent aggregates live lane rates, carrier pricing, and historical quotes into one coherent view that can be pulled up directly inside the quoting workspace. For a given lane or corridor, it pulls in current carrier rate cards where available, recent spot and contracted prices, and your own historical quotes and outcomes, then normalizes these into a consistent, comparable snapshot. Pricing and sales users see where the market is, how your recent pricing has behaved on similar moves, and what band looks realistic, instead of chasing numbers across spreadsheets, emails, and portals.

Core AI functions

The core function is data aggregation: the agent connects to multiple internal and external sources, cleans and normalizes lane and rate data, and stitches it together into a single market view for each quote. It focuses on making the inputs to pricing complete, timely, and consistent, rather than trying to “decide the price” itself.

Problem solved

When market and internal pricing data are scattered, quotes slow down. Sales and pricing teams jump between carrier portals, TMS exports, BI dashboards, and old email chains just to form a view of what “reasonable” looks like, and different people end up using different reference points for similar opportunities. That creates delays, inconsistent pricing, and a higher risk of either overpricing (and losing the bid) or underpricing (and eroding margins). By centralizing market and historical data into one place, this agent removes that scattered-input bottleneck.

Business impact

The immediate impact is faster, more consistent pricing decisions. Quotes can be prepared more quickly because the data-gathering step is largely automated, and different teams work from the same view of lane and carrier conditions, which reduces internal debate and random variation. Over time, this translates into a smoother quoting experience for customers and a more disciplined approach to how you position prices against the market.

Integration and adjacent use cases

Integration focuses on connecting to your existing rate sources and quoting tools: the agent reads lane rates, carrier price feeds, and historical quote data from TMS, pricing sheets, and external content providers, then surfaces a unified view directly in the quoting interface—no core replacement required.

Common combinations in this stack:

• Quote Optimization Agent to take the aggregated market and historical view and compute margin-aware, competitive rates for each opportunity; and

• Margin Intelligence Agent to learn from win/loss and performance data over time, feeding back smarter pricing guidance and guardrails.