How to automate document data entry into your TMS

Put a schema-enforced extraction layer in front of the TMS: every rate confirmation, BOL, or carrier invoice becomes typed JSON matching your TMS ingestion fields, delivered by webhook, with per-field confidence scores routing uncertain values to review. Your TMS stays the system of record, and bem feeds it records it can trust.

Why is TMS data entry still manual?

Because the documents arrive outside the TMS. EDI covers the flows both trading partners set up in advance: load tenders and invoices moving as 204s and 210s, status updates as 214s. Everything else comes in sideways. Rate confirmations land as email attachments. Proofs of delivery are photos a driver took at the dock. Carrier invoices arrive as PDFs from carriers too small to run EDI. Lumper receipts, weight tickets, and customs paperwork come off portals one at a time.

The TMS only speaks structured input. Its API and import formats want a load number, typed stops, a numeric weight, coded charges. A PDF is none of those things. So a coordinator opens the document on one screen and the TMS on the other, and data entry becomes a tax every shipment pays.

Generic OCR and most intelligent document processing tools stop short here: they produce text or loosely structured output, and text is not what a TMS API accepts. The gap between extracting a document and a correct load in the TMS is an integration problem, and it is the one this piece is about.

What does a TMS ingestion contract look like?

Treat your TMS's create and update payloads as a contract, because that is how the TMS treats them. The contract is typed. A weight is a number with a unit, not the string "40,000 lbs approx". A stop is an object with an address, a stop type, and an appointment window. Free text either gets rejected at the API or, worse, gets accepted and silently pollutes reporting.

TMS field group	Typical typed fields	What the document actually gives you
Load identity	load number, BOL number, PRO number, PO references	mixed labels, plus the carrier's own reference numbers
Stops	address, city, state, stop type, appointment window	a free-text address block and a scribbled arrival time
Freight	weight with unit, piece count, pallet count, equipment type	"40,000 lbs" in a table cell, handwriting on the POD
Charges	line-haul amount, fuel surcharge, accessorial code and amount	carrier-specific line items that map to your charge codes

Write this contract down first. Once the target shape is explicit, extraction stops being an accuracy contest and becomes a typing problem: whatever reads the document has to guarantee this shape every time, not just find the right words in the text.

How does schema aligned extraction produce TMS-ready data?

Buyers call this schema aligned extraction. We build it as enforcement: aligned output usually matches your fields, enforced output always does. Start from the TMS payload and work backward. In bem, you define a JSON Schema that mirrors your ingestion contract, and the Extract function pulls structured JSON out of documents, images, and media against that schema, validated on every run.

bem automates entire business processes end to end: it turns unstructured data into auditable outputs, then makes and records the decisions in between.

Every field carries its own confidence score, and this is where the integration holds or quietly rots. Run the math your engineers will run: 98 percent per-field accuracy compounds to roughly 82 percent document-level accuracy on a ten-field document, and a rate confirmation has more fields than that. A blended accuracy number is the wrong question. The right question is what happens to the fields the system is not sure about. In bem, low-confidence fields route to human review automatically, so a smudged weight on a POD becomes a review task instead of a wrong load record.

Determinism is the other half. bem functions are versioned and idempotent: same input, same output until you publish a new version, and every version gets automatic regression testing and drift detection, with rollback per environment. The integration behaves the same in month six as in the evaluation.

Fleetio is a fleet operations platform. Fleetio cut invoice processing time 65 percent, with 100 percent accuracy on invoice totals. The second number matters more here: totals are the fields a system of record pays against.

How do you deliver extracted data into the TMS by webhook?

The schema you extract against is never quite the payload your TMS wants, and we built bem's Payload Shaping function for exactly that gap. Payload Shaping transforms data structures using JMESPath expressions: rename fields to the TMS's names, nest stops the way its API expects, reshape charge line items into its billing structure. The extraction schema stays document-shaped, the outbound payload TMS-shaped, and when the TMS API changes you change an expression, not your extraction.

Delivery is a subscription. You bind a webhook to the workflow's output events, verify the signature on each delivery, and receive TMS-ready JSON the moment a document finishes processing. On your side sits one thin service with a single job: an authenticated create or update call into the TMS. That is the whole integration. All of it sits behind the V3 API, with SDKs in TypeScript, Python, Go, and C#.

In this pattern, bem delivers to a thin service you own rather than writing into the TMS directly. The TMS stays the system of record for loads, and bem sits in front of it, turning documents into evidence-backed records. Every payload that reaches your service traces back to the exact input file, function version, and per-field confidence that produced it, which is the audit trail you need when someone asks why a load shows a 44,000 pound weight.

How do you match documents to existing loads instead of creating duplicates?

This is the hard part. Almost every logistics document refers to a load your TMS already knows about. A rate confirmation confirms a load you tendered. A POD closes a stop you dispatched. A carrier invoice bills a load you already delivered. Blindly creating records fills the TMS with duplicates; blindly updating overwrites good data with a misread. Update versus create is the real integration problem.

Matching is hard because documents are bad at references. The carrier's invoice leads with their PRO number, not your load number. The BOL number was re-keyed at the shipper with a transposed digit. One invoice covers three loads.

Extraction narrows the problem without eliminating it. bem contributes typed candidate references with per-field confidence, so you know whether the load number came out clean or barely legible. If you keep open-load references in a bem collection, the Enrich function can augment the extraction with context from that collection, giving the matcher candidates to score. The match policy itself is deterministic rules: an exact load-number match updates, a BOL-plus-carrier-plus-date match becomes a candidate a person confirms, no match holds in a queue rather than auto-creating a load. You can run those rules in your integration service against the TMS's own search, or write them as a Decide policy that bem applies to every record and logs with its reasoning. Either way the decision rule stays explicit and yours.

The right policy depends on your TMS's data quality and your tolerance for duplicates. What schema enforcement changes is that every input to that decision is typed, confidence-scored, and traceable, so the policy becomes a short, auditable rule instead of tribal knowledge in a coordinator's head.

Does this work with McLeod, MercuryGate, or a homegrown TMS?

Yes, and by design it does not care which. The integration point is your TMS's own API or import format, so the pattern is identical for McLeod, MercuryGate, or something your team built a decade ago: a JSON Schema that mirrors the ingestion contract, Payload Shaping to match the payload format, a webhook into one thin service. Swapping the TMS changes expressions and an endpoint, not the architecture.

The same boundary answers the next buyer question: how do you automate document-heavy logistics workflows with AI agents end to end? Not by pointing an agent at an inbox of PDFs. An agent that re-reads documents makes a fresh guess every time. Agent-readable data means the reading already happened once, deterministically, with confidence scores and an audit trail attached, on bem's entity registry and event log, which the agent can query. An agent can then safely act: dispute a detention charge, chase a missing POD, flag an invoice that does not match the rate confirmation. The TMS stays the system of record for operations, and bem makes those operations readable to software across the rest of your supply chain and logistics workflows.

If your team is re-keying freight documents today, the fix is not better OCR or a bigger EDI project. It is a contract, a schema that enforces it, and a webhook that honors it.