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Logistics & Supply Chain · Freight document automation

Your ops team should not be re-keying what is already on the paper

Bills of lading, commercial invoices, customs declarations, and packing lists arrive in a mix of scanned PDFs, faxes, and carrier portals. Banao deploys document AI that reads each one, pulls the structured fields into your TMS, and routes exceptions — not the full stack — to your ops staff.

The model handles format variation across carriers, ports, and origin countries. A 3-way match flags discrepancies before they become a customs hold or a disputed invoice.

What a Banao freight-document deployment covers

Document automation is more than OCR. The hard work is handling every carrier layout, matching against live TMS data, and routing only what needs human eyes — without burying the queue.

Extraction across carrier and shipper formats

The model is trained on your actual document corpus — not a generic template. Layout variation across carriers, ports, and origin countries is part of the training data, not an edge case to handle later.

3-way match: PO, invoice, proof of delivery

Extracted line-item data is matched automatically against open POs and delivery records in your TMS or ERP. Discrepancies surface as exceptions, not as a pile of mismatched paper found at month-end.

Exception queue, not a blanket review

Documents that pass extraction and match go straight through. Only documents with a discrepancy, a low-confidence field, or a missing value reach a human reviewer — so ops staff work on the ten that need them, not the hundred that don't.

TMS and ERP integration

Structured data writes directly into your TMS, ERP, or customs filing system as a clean record. We integrate with legacy EDI feeds, SAP, and proprietary freight management platforms — flat-file export is available as a fallback for systems with no API surface.

Multi-format ingestion

Email attachments, carrier portal PDFs, scanned paper, and fax-to-email feeds enter a single pipeline. Document type is classified on intake; the correct extraction model applies without manual routing.

Audit trail and compliance export

Every extracted field records its source location and confidence score alongside the final value. The audit trail is structured for customs audit response and internal finance review — not just a raw log.

Where this is running

Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live.

Indian Oil

Freight and dispatch documentation across a national distribution network

  • ··%manual keying eliminated
  • ··%clearance cycle time reduction
  • ··%invoice matching accuracy

Indian Oil generates a document load across thousands of depot and carrier relationships that grew faster than the operations headcount to process it. Banao deployed document AI across the inbound freight and depot dispatch flow — extracting delivery orders, challans, and supplier invoices and writing structured records into the ERP without manual re-entry.

We run our own operations on the AI we build

Banao operates ~300 engineers across multiple offices and processes its own supplier, vendor, and payroll documentation through AI-assisted workflows. Before a document model reaches a client's ops team, it has had to handle Banao's own inconsistent invoice formats, late PDFs, and manual exception queues.

That daily exposure is what makes our document AI handle the real document set — not a cleaned-up demo version of it.

  • InterviewGodScreens every Banao engineering hire before a human interview.
  • VikaasRuns Banao's own demand-gen pipeline, including document-heavy sourcing flows.

When freight document AI is not the right starting point

Document AI requires a document problem worth solving. We will tell you when you don't have one:

  • Low volume: below a few hundred documents a day, a well-designed spreadsheet workflow costs less than a model. We will say so.
  • Highly standardised formats: if every carrier already sends a clean EDI 210, you may have a structured feed that doesn't need AI extraction at all.
  • No validation layer: a document model without a downstream 3-way match or exception queue is not compliant for customs filing. We won't deploy extraction without a validation step.

How we engage — fixed-price first

We don't quote a document automation build off a sample pack. We look at your real document corpus and current exception rate first.

  1. AI Discovery Sprint2 weeks · fixed price

    We review a sample of your actual document set — format variety, scan quality, field coverage — and return a baseline extraction accuracy estimate, an exception-rate projection, and ROI maths on keying-time saved. Yours to keep either way. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Training on your real document corpus, integration with your TMS or ERP, and the exception queue and audit trail. Data pipeline engineering is part of the deliverable, not a prerequisite.

  3. Production & continuous improvement

    Deployment with ops-team change management, a reviewer dashboard, and a feedback loop. Documents that ops staff correct feed back into the model so extraction accuracy keeps improving with your actual document flow.

Frequently asked questions

Yes — format variation is the main thing the Discovery Sprint assesses. The model is trained on your actual document sample, not a generic template, so carrier-specific layout, field placement, and terminology are part of the training data, not edge cases addressed later.

Low-confidence extractions and failed matches go into an exception queue for human review. The reviewer confirms or corrects the field; that correction feeds back into the model. You are never downstream of an unreviewed extraction on a document where accuracy matters — the queue is the control mechanism.

Yes. Scanned paper, fax-to-email, and digital PDFs enter the same pipeline. Lower-quality scans route to the exception queue at a higher rate, which the Discovery Sprint will quantify against your actual scan quality distribution.

Integration is part of the build deliverable. We support direct API integration with most TMS and ERP platforms, EDI feed injection, and flat-file export for systems with no integration surface. Legacy customs filing platforms — SAP GTS and similar — are in scope.

A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout with ops-team change management. Extraction accuracy on the primary document types reaches a working threshold during the build phase; the model keeps improving after go-live as reviewer feedback accumulates.

Bring your worst carrier format

Send us a sample of the document types your ops team dreads most. In 45 minutes we will tell you what extraction accuracy we can reach and what the keying-time saving looks like.

Book a 45-min scoping call