AI Agents for Logistics
A tracking dashboard shows you what happened. An AI agent decides what to do about it. When a shipment misses a checkpoint, an agent can classify the exception, check carrier ETAs, draft the customer update, and route the case to a dispatcher only when the situation falls outside its resolution logic. We build logistics AI agents with defined scope, explicit escalation rules, and integration into the carrier APIs, TMS platforms, and customs systems your operations already depend on.
Carrier selection and booking agents that compare rates and confirm bookings
Customs document agents that generate paperwork from shipment data
Delivery exception agents that classify and resolve routine exceptions automatically
Invoice reconciliation agents that catch carrier billing errors before payment
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
Dispatchers spending hours comparing carrier rates and booking shipments manually for every order?
Delivery exception notifications consuming dispatcher time on routine updates that follow predictable resolution paths?
Short answer
RaftLabs builds autonomous AI agents for logistics workflows: carrier selection and booking, customs document generation, delivery exception handling, shipment status updates, invoice reconciliation, and demand forecasting. Unlike rule-based automation, these agents reason across shipment data, carrier APIs, and customs requirements to take multi-step actions within defined guardrails. Most logistics AI agent projects deliver in 10-14 weeks at a fixed cost.
Key takeaways
Trusted by
A tracking dashboard shows you what happened. An AI agent decides what to do about it. When a shipment misses a checkpoint, an agent can classify the exception, check carrier ETAs, draft the customer update, and route the case to a dispatcher only when the situation falls outside its resolution logic.
We build logistics AI agents with defined scope, explicit escalation rules, and integration into the carrier APIs, TMS platforms, and customs systems your operations already depend on. Integration is scoped during discovery because that's where most logistics projects carry the most hidden complexity.
Capabilities
Queries connected carrier APIs and rate engines using shipment parameters, applies your routing guide logic, and submits bookings via carrier API or EDI when a carrier meets all criteria. Confirmations write back to the TMS with PRO number and pickup window. Lane exceptions, spot-market decisions, and new carrier relationships route to your team; the audit trail captures every rate query and booking so lane performance accumulates automatically.
Generates commercial invoices, packing lists, and certificates of origin from structured shipment data. HS code classification uses a trained model plus knowledge base; low-confidence classifications route to a trade compliance reviewer with the rationale attached. New trade lanes require human review before submission; established lanes with validated templates can move to exception-only review.
Classifies exception events, weather delay, address correction, damage, customs hold, against a defined taxonomy and applies resolution logic automatically for classes with playbooks. A weather delay with a strong carrier recovery rate triggers a customer notification with no dispatcher action; a damage report triggers a claim workflow with the case pre-documented. Escalation happens when the exception falls outside the taxonomy or involves a flagged SLA.
Pulls tracking data from carrier APIs and aggregators, normalises it to a unified status taxonomy, and pushes structured updates to customer portals, ERP records, and internal channels. A status cache answers "where is order 98765?" in seconds without a live carrier API call. High-priority shipments get more frequent polling and immediate push notifications on any change.
Matches carrier invoices against rate confirmations, BOLs, and TMS records to catch billing discrepancies before payment, weight disputes, accessorial disputes, rate disputes, duplicate invoices. Discrepancies below a threshold generate a dispute notice automatically; above it, they escalate to a freight auditor with the matched record for review. Monthly reports show total billed, disputed, and recovered by carrier.
Analyses historical volume, seasonal patterns, and confirmed order history to generate lane-level and carrier-capacity forecasts. Capacity risk signals flag lanes where forecast volume exceeds contracted capacity or carrier performance has degraded, each with supporting data attached. Commercial decisions, requesting spot capacity, adjusting contracted volumes, stay with your carrier relations team.
How we work
We map the target workflow, your TMS and carrier API coverage, and escalation logic. You leave week 1 with a written scope document and a fixed-price quote.
Routing guide rules, exception taxonomy, and human-in-the-loop checkpoints defined and confirmed with your operations team.
Agent workflow built against LangGraph, tested against real TMS and carrier sandbox data every sprint.
Production deployment with audit logging and escalation queues staffed from day one.
Why us
The engineers who assess your carrier network also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
We scope the work, calculate the cost, and lock it in writing before any development starts.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building logistics and carrier-integration platforms.
REST JSON, EDI, and PDF invoices all translate into one schema before the agent reasons over them, so the agent applies the same logic regardless of how a carrier delivers data.
The agent doesn't submit to customs authorities without a review step for new trade lanes. Established lanes can move to exception-only once templates are validated.
Tell us the workflow you want to automate, your TMS platform, and your carrier mix. We'll scope what an agent can handle and give you a fixed cost.
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Read moreA TMS workflow automation executes a predefined rule that runs the same way every time. An AI agent reasons over variable inputs, reading a carrier invoice in PDF format, matching it against the rate confirmation in the TMS, identifying a discrepancy, and generating a dispute notice without a human mapping every field. Agents operate as stateful, multi-step processes that can interpret unstructured inputs, call external systems, and apply judgment logic that would require a rule for every combination in traditional automation. The workflows that benefit most are ones where the current automation is actually a person doing the same interpretive work every day.
We integrate with TMS platforms that expose REST APIs or support EDI transaction sets, including Transplace, MercuryGate, Oracle Transportation Management, Manhattan TMS, and BluJay. Carrier API integration covers FedEx Ship Manager API, UPS Developer Kit, DHL Express API, and USPS Web Tools. For LTL carriers, the NMFTA standard EDI transaction set (204/214/210) is the baseline, with direct REST API integration available for carriers that have published one. Integration scope is confirmed during discovery, as the variance in what different TMS configurations expose via API is large enough to change project scope materially.
A focused logistics AI agent covering one workflow, defined escalation logic, and standard integration typically runs $25,000 to $60,000 and delivers in 10-14 weeks. A multi-agent system covering carrier booking, customs document generation, and delivery exception handling with TMS write-back and carrier API integration typically runs $60,000 to $130,000. Cost is driven by the number of carrier integrations, TMS API complexity, and customs destination coverage.
Carrier data normalization is built into the agent architecture. The integration layer translates each carrier's native format, REST JSON response, EDI transaction, or PDF invoice, into a unified internal schema before the agent's reasoning logic runs. For carrier invoices specifically, the document intelligence layer handles PDF parsing with trained extraction models. EDI mapping is done per trading partner during integration setup, so adding a new carrier requires configuring the EDI map for that carrier, not modifying the agent's core logic.
Work with us
We scope AI Agents for Logistics in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
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