AI Agents for Logistics Services
A tracking dashboard shows what happened. An agent decides what to do about it.
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
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?
Plain 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. A first agent covering one workflow launches as a validated v1 at a fixed cost, then grows into a multi-agent system.
What to remember
- Agents earn their keep on workflows where the current "automation" is actually a person doing interpretive work daily: reading invoices, classifying exceptions, pulling HS codes.
- Data normalization sits at the integration layer, translating REST JSON, EDI, and PDF invoices into one schema before the agent's reasoning logic runs.
- Customs document agents don't submit to authorities without human review on new trade lanes; established lanes can move to exception-only review.
- A first single-workflow agent covers one workflow; the full multi-agent system across booking, customs, and exceptions grows over time.
Proof
- shipping production software across logistics, fintech, and hospitality
- Since 2015
- RaftLabs delivery record
- scope and cost agreed in writing before any development starts
- Fixed price
- Every RaftLabs engagement
AI agents that act, not just report
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.
| Dispatch task | Rule-based automation | AI agent | Human dispatcher |
|---|---|---|---|
| Reading a carrier invoice PDF | Breaks the moment the layout changes | Extracts fields with a confidence score, matches against the rate confirmation, flags the discrepancy | Reads and cross-checks by hand, one invoice at a time |
| Classifying a delivery exception | Fires the same canned message for every event type | Sorts weather, address, damage, and customs holds against a taxonomy and applies the right playbook | Interprets each notification, then decides what to do |
| Booking against a routing guide | Follows one fixed rule, no trade-offs | Compares live rates and transit times, applies your routing logic, escalates spot-market calls | Compares carrier options manually for every order |
| A new trade lane or edge case | No path; drops to manual | Routes to a person with the case pre-documented and its reasoning attached | Handles it, but the same judgment is not captured for next time |
The agent sits between the two columns you have today. It takes the interpretive work a rule engine cannot handle and a person should not repeat all day, and it hands the genuine judgment calls back to your team with the case already assembled.
Capabilities
What we build
Carrier selection and booking agent
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.
Customs document automation agent
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.
Delivery exception handling agent
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.
Shipment status update agent
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.
Invoice reconciliation agent
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.
Demand forecasting agent
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
From scope to live agent
- Phase 101
Workflow and TMS scoping
We map the target workflow, your TMS and carrier API coverage, and escalation logic. You leave scoping with a written scope document and a fixed-price quote.
- Phase 202
Routing and playbook design
Routing guide rules, exception taxonomy, and human-in-the-loop checkpoints defined and confirmed with your operations team. The escalation design decides which exceptions resolve automatically and which route to a dispatcher with the case pre-documented.
- Phase 303
Build and integrate
Agent workflow built against LangGraph, tested against real TMS and carrier sandbox data every sprint. Integration scope is confirmed against what your TMS actually exposes before anything goes live.
- Phase 404
Launch and monitoring
Production deployment with audit logging and escalation queues staffed from day one. Monitoring covers exception volumes, escalation rates, and decision audit trails so drift is visible.
Where to start
Start with the smallest workflow that earns its keep, prove it against real shipments, then grow. Invoice reconciliation is often the first agent to prove itself: an agent that catches weight, accessorial, and duplicate-billing disputes before payment.
Scope and fixed price are agreed in writing before any development starts.
- Number of carrier integrations
- One REST-API carrier is an entry-tier build. FedEx, UPS, DHL, and a mix of LTL carriers on EDI 204/214/210 each add a mapping and a test surface.
- TMS API complexity
- An API-first TMS (Oracle Transportation Management, MercuryGate) is cleaner than write-back over a legacy or EDI-only configuration. What your TMS exposes varies enough to change scope materially, so we confirm it in discovery.
- Customs destination coverage
- One established trade lane with a validated template is light. Multi-country coverage with HS-code classification and per-authority filing formats adds review workflow and compliance scope.
- Exception and escalation design
- The taxonomy, the resolution playbooks, and the human-in-the-loop checkpoints for the cases that should not go straight through are real scope, not an afterthought.
Proof
What logistics operators evaluate
- 01
The team that scopes it builds it
The engineers who assess your carrier network also build the solution. No bait-and-switch after the contract is signed.
- 02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts.
- 03
A carrier-integration platform already in production
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. For multi-carrier logistics specifically, we rebuilt UrShipper on its fifth delivery attempt, with integrations across FedEx, DHL, UPS, Aramex, and Shippo.
- 04
Normalisation handles your full carrier mix
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.
- 05
Customs stays human-reviewed on new lanes
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.
Pitfalls we plan around
Most logistics agent projects fail in predictable places. We design against them from the start.
- Carrier data is messier than the API docs
- One carrier sends clean REST JSON, the next sends an EDI 214 with quirks, a third sends a PDF invoice. We normalise every source into one schema before the agent reasons over it, so adding a carrier means configuring a map, not rewriting logic.
- The agent making a customs call it should not
- Filing to an authority carries real liability. The agent generates and pre-checks documents, but new trade lanes require human review before submission. Only established lanes with validated templates move to exception-only review.
- An escalation queue that quietly overflows
- Set the confidence thresholds too tight and every edge case becomes a manual review, so nothing is saved. We tune the exception taxonomy and thresholds with your dispatchers so real exceptions surface and routine cases flow through.
- Carrier API rate limits and outages
- Live carrier calls fail and throttle. A status cache answers routine lookups without a live call, and the agent retries and degrades gracefully instead of stalling the workflow when a carrier endpoint goes down.
Where agentic logistics ops is heading
The tracking dashboards of the last decade told you what happened. The next layer decides what to do about it and does it, within guardrails you set. The practical path is not one autonomous system that runs the whole operation. It is a set of scoped agents, each owning one workflow, each with a clear escalation path, accumulating an audit trail as they go. We build for that path now: the agent absorbs the interpretive load, your team keeps the judgment calls, and every decision the agent makes is logged and reviewable.
Proof
Case studies
- 01
Scalable multi-carrier shipping software for a global logistics platform
- shipments between May 5 and June 2, 2025
- 1,005
- migrated customers using the portal by June 2025
- 214
- 02
Musgrave receipt scanning software case study for SuperValu and Centra
Musgrave's post-campaign report records 1,062 users and 1,610 receipt submissions. At the reporting snapshot, 1,158 were AI approved, 409 AI rejected, 4 manually approved, 10 manually rejected, and 29 remained processing.- users in the campaign report
- 1,062
- receipt submissions at the reporting snapshot
- 1,610
- project period recorded in the task
- About 2-3 months
- 03
RaftLabs grows organic traffic 30% after migrating its website to a headless CMS and launching 3,000 programmatic pages
RaftLabs migrated from Webflow to Sanity CMS to run a programmatic SEO strategy at scale. Zero downtime. 3,000+ pages launched. 12,000 monthly visitors moved without traffic loss. Organic traffic up 30% in the first year (our internal records).- pages launched, driving pSEO and traffic growth
- 3000+
- downtime: 12K monthly visitors moved without traffic loss
- 0
- organic traffic growth in year one
- 30%
Work with us
Have a logistics AI agent project?
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.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
A 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.
Start with one workflow. A focused agent covering one workflow, defined escalation logic, and standard integration launches as a validated v1, so you can put it in front of real dispatchers and measure it before you commit further. From there it grows: a multi-agent system covering carrier booking, customs document generation, and delivery exception handling with TMS write-back and carrier API integration. Cost is driven by the number of carrier integrations, TMS API complexity, and customs destination coverage.
The fear is real, and the answer is guardrails, not trust. Do not rely on the model as the only source of operational truth: authoritative systems, business rules, confidence thresholds, and human approval for high-impact actions. Start with the minimum authority needed: observation and recommendations first. High-cost, regulatory, safety, or customer-commitment decisions stay human unless governance is defined. You cannot delegate accountability. Design so that failure is visible, not silent.
No. The TMS remains the system of record; the agent works around it as a coordination layer. Think supercharge, not rip and replace: routine pre-negotiated purchasing can be automated, but the human has to sign it. Agents track, quote, and book freight on negotiated rates, but every exception routes to a human expert. The assisted journey, not automation: human judgment stays in control.
Not yet, and this is the gate most vendors skip. You cannot give AI access to data people themselves cannot reach without clicking through tabs. Systems integration must precede agents: fix the data foundation first, then automate the decisions on top of it. AI built on messy data just automates the mess faster.
Ask for the approve-button pattern: every consequential action the agent takes should be visible and reversible before it commits. Start with a bounded pilot on one lane or one workflow, and measure what changed: cost per shipment, exception resolution time, planner hours freed. Demand the failure story: what happens when the agent is wrong, who sees it, and how fast does a human take over? If the vendor cannot demo the guardrails, they built a demo, not an agent.
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.
What it costs
Every project starts at $10000.
The first phase deploys one agent: exception handling, carrier selection, or customer updates.
Starting investment
Starts at $10000
One logistics agent handling exceptions or bookings with approved actions and escalation. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Choose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
- 02
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
- 03
Build
Own the system when the logistics decision shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
- 04
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
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