shipAI structures inbound shipment requests, flags contradictions, proposes class or NMFC candidates, and routes exceptions with evidence while your existing pricing rules stay in control.
shipAI prepares the quote package. Final pricing still stays with your contracts, tariffs, surcharges, and approval policies.
Instead of throwing a user into a generic AI promise, shipAI narrows the job: structure the request, expose the risk, preserve the evidence, and hand the final draft to your existing systems.
Read the inbound request, normalize units, and extract origin, destination, dimensions, packaging, and service constraints into a clean draft payload.
Catch density mismatches, incomplete accessorials, and unsupported service details, then generate missing-information prompts instead of letting bad data move downstream.
Rank candidates, surface reason codes, and push low-confidence cases into a reviewer queue so the team sees why a recommendation exists before acting on it.
Ship structured output, reviewer decisions, and event-level logs to the systems already running pricing, approvals, and customer communication.
The page no longer needs a generic feature grid. The useful story is narrower: field quality, exception visibility, and review discipline.
Convert email chains, PDFs, and form submissions into consistent quote-ready fields with unit normalization, schema checks, and missing-info prompts.
Surface the fields that are expensive to miss: classification candidates, liftgate risk, limited access, appointment requirements, and other exception triggers.
Keep reviewer edits, confidence scores, reason codes, and policy overrides attached to the quote package so teams can coach, audit, and tune operations over time.
Start with one quoting lane, one reviewer group, and a small set of success metrics. The pilot is designed to prove throughput and risk handling before broader rollout.
Pick one RFQ channel, one service type, and one region or customer segment with enough volume to generate real operational feedback.
Capture reviewer actions, tune extraction rules, tighten prompts, and validate how exception routing behaves under real quoting pressure.
Review median response time, clarification rate, structured RFQ ratio, and re-rate trend before deciding what to scale next.
Integration should read as an operational flow, not a list of vendor nouns.
Ingest inbox traffic, PDFs, shared folders, and web forms with controlled field mapping and validation.
Normalize the request, rank class or NMFC candidates, and route the uncertain cases with evidence for analyst review.
Push structured quote drafts, decision logs, and reviewer outcomes into the systems already running pricing and customer communication.
The point is not generic enterprise language. The point is keeping sensitive shipment and pricing-adjacent decisions visible, controlled, and reviewable.
Share the current intake channel, quoting volume, and where your team gets blocked. We will respond with a pilot scope, sample requirements, and success metrics tailored to your operation.
Best for teams evaluating LTL, cross-border, or mixed RFQ workflows with reviewer approval requirements.