Industry · Logistics
The exceptions are the job. Automate those.
Freight runs smoothly until it doesn't, and the exception queue is where the margin goes. Agents that read the documents and chase the parties are worth more than another dashboard.
- AI Agents
- Generative AI & RAG
- 30-day AI Sprint
Why now
Logistics already has data; what it lacks is anyone with time to read it. Every delayed shipment generates a chain of emails, PDFs and phone calls across carriers, customs brokers and customers. That coordination — not route optimisation — is where AI removes the most hours today.
The problem
Where logistics teams lose time today.
- 01
The paper trail is unstructured
Bills of lading, packing lists, invoices and customs forms arrive as PDFs and scans from hundreds of counterparties, each with their own layout.
- 02
Exception handling is email archaeology
Answering 'where is it and why' means reading a thread, three attachments and a carrier portal that times out.
- 03
Tariff and compliance rules are scattered
HS codes, restrictions and documentation requirements live across sources that disagree, and the cost of getting it wrong is a held container.
Constraints
What makes this harder than a generic chatbot.
- 01
Extraction accuracy is measured per counterparty
One carrier's invoice layout is a solved problem; another's scanned fax is not. We report accuracy per source so automation is enabled where it's earned.
- 02
Customs and tariff answers get citations or nothing
Classification suggestions carry the source rule and go to a licensed broker for confirmation. An unsourced HS code is a fine waiting to be issued.
- 03
Integrations fail, so the design assumes it
Carrier APIs and EDI feeds go down mid-shipment. Retries, dead-letter queues and a visible degraded state beat a silent gap in tracking.
Integrations
Systems we wire into.
- Carrier and 3PL APIs
- EDI (X12 / EDIFACT)
- SAP & Oracle SCM
- Shiprocket & Delhivery
- Customs broker portals
- Snowflake
- Twilio & WhatsApp Business
Services
What we build for logistics.
- AI agents for logistics operationsAgents that detect a shipment exception, gather the facts from carrier and document sources, notify the right party, and escalate with the file already assembled.Read
- RAG for supply chain documentsRetrieval across standard operating procedures, tariff schedules, carrier contracts and customs requirements — with the source rule attached to every answer.Read
- 30-day AI sprint for logisticsPick the exception that costs the most operator hours. We ship working software against it in 30 days with an eval score, or you don't pay.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in logistics: the questions we get.
We already have a TMS. Where does AI fit?
Between the TMS and everything that never reaches it — the email threads, the attachments, the phone calls. The TMS knows the shipment; it doesn't know that the consignee replied at 11pm asking to change the delivery window.
Can it read documents from counterparties we don't control?
That's the main use case. Layout-aware extraction handles varied formats, and anything below the confidence threshold routes to a human rather than entering the system as a confident wrong number.
Does this need a data warehouse first?
No. We'd rather start on one exception type with the systems you already have than wait a quarter for a warehouse. The warehouse conversation is easier once something is running.
Contact
Talk to us about AI in logistics.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read