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Unstructured Data Extraction APIs

How do we automatically classify and split mixed document packets (like BOL + rate confirmation + POD in one PDF) before extraction?

8 min read

You don’t start by parsing the fields. You start by untangling the packet.

When you get a 20-page PDF that mixes a Bill of Lading, rate confirmation, and POD in one file, the first production problem isn’t “how do I extract line items?” It’s “what’s on which page, what type is it, and where does each document begin and end?” If you get that wrong, every downstream extraction, validation, and posting step is corrupt.

Quick Answer: Mixed packets are handled by a two-stage workflow: first, a Split + Identify pass that segments and classifies each document inside the packet (BOL, rate confirmation, POD, etc.), then a Route step that sends each classified document into the correct extraction + validation workflow. In Bem, this is implemented as composable functions (Split, Identify, Route) with per-doc confidence, schema validation, and exception routing when confidence drops below your threshold.

Why This Matters

Per-page OCR or “single-document” APIs assume your input is clean and homogeneous. Operations teams know that’s rarely true. Carriers bundle BOLs, rate confirmations, emails, and signed PODs into a single PDF; freight forwarders glue invoices, packing lists, and certificates into one long packet; TMS exports mash multiple loads into one download.

If your system can’t automatically classify and split mixed packets before extraction:

  • Accuracy tanks: the wrong schema gets applied to the wrong page.
  • Glue code explodes: you reinvent splitting, matching, and routing in every integration.
  • Operators drown in edge cases: low-confidence packets bounce back to manual entry.

Automatic classification and splitting is what turns “demo extraction” into a real, debuggable pipeline.

Key Benefits:

  • Deterministic routing: Each page or span is labeled as BOL, rate confirmation, POD, etc., then routed to the correct workflow instead of a generic parser.
  • Schema-safe extraction: Once split, every document is validated against its own schema, catching mismatches before they hit your TMS/ERP.
  • Operational visibility: Per-document confidence and audit logs show exactly how a packet was split and classified, so you can debug and improve instead of guessing.

Core Concepts & Key Points

ConceptDefinitionWhy it's important
SplitA function that segments a mixed packet (multi-page PDF, email thread, etc.) into candidate documents based on layout, structure, and learned patterns.You can’t safely extract or validate until you know where one document ends and the next begins.
Identify (Classify)A semantic classification step that labels each segment as a specific document type (e.g., “Bill of Lading,” “Rate Confirmation,” “POD”) and sometimes language or variant.Drives routing and schema selection; if type is wrong, every downstream field is suspect.
RouteWorkflow logic that sends each identified document to the correct extraction + validation pipeline (e.g., BOL workflow vs rate confirmation workflow).Turns a messy packet into clean, typed JSON outputs that downstream systems can trust.

How It Works (Step-by-Step)

At Bem, mixed packet handling is a first-class workflow pattern, not something you bolt on at the end. Conceptually, the flow looks like:

  1. Ingest packet → Split into logical documents
  2. Identify each segment’s type → Attach confidence
  3. Route to type-specific extraction + validation workflows

Under the hood, these are just functions in a versioned workflow.

1. Ingest & Split: Turn “one PDF” into candidate documents

You start with a single input: a PDF that might contain a BOL, rate confirmation, and POD back-to-back.

Typical entry point:

  • Upload via REST API (single file)
  • Forward as an email with attachment(s)
  • Drop via an S3 bucket or storage integration

First workflow step: a Split function.

What Split actually does:

  • Looks at layout and structure:
    • Page headers / footers changing across pages
    • Big shifts in density (e.g., pages with signature blocks vs tables vs plain text)
    • Repeating patterns like “Bill of Lading” titles, carrier logos, or templates
  • Uses models trained on logistics packets:
    • Shipping packets, rate sheets, PODs, etc.
    • Learned page transition patterns (e.g., BOL → Customs Declaration → Payment Receipt)
  • Produces segments:
    • Example:
      • Segment 1: pages 1–3 → candidate BOL
      • Segment 2: pages 4–5 → candidate rate confirmation
      • Segment 3: pages 6–6 → candidate POD

Output of Split:

{
  "segments": [
    { "id": "seg_1", "pages": [1, 2, 3] },
    { "id": "seg_2", "pages": [4, 5] },
    { "id": "seg_3", "pages": [6] }
  ],
  "packet_id": "pkt_123"
}

This is not yet about field extraction; it’s about cutting the packet into coherent chunks that could be documents.

2. Identify: Classify each segment (BOL vs rate confirmation vs POD)

Next step: an Identify function runs semantic classification over each segment.

This step:

  • Uses both content and layout:
    • Content cues: “Bill of Lading,” “Carrier Rate Confirmation,” “Proof of Delivery,” pro number, SCACs, consignee/shippers, rate tables, signatures.
    • Structural cues: presence of rate tables vs shipment details vs signature stamps.
  • Assigns a document type + confidence:
    • BillOfLading (0.995)
    • RateConfirmation (0.992)
    • ProofOfDelivery (0.981)
  • Optionally adds variant metadata:
    • E.g., Carrier=XYZ, Language=en, Region=US, which you can use for more granular routing.

Example Identify output:

{
  "documents": [
    {
      "segment_id": "seg_1",
      "type": "BillOfLading",
      "confidence": 0.995
    },
    {
      "segment_id": "seg_2",
      "type": "RateConfirmation",
      "confidence": 0.992
    },
    {
      "segment_id": "seg_3",
      "type": "ProofOfDelivery",
      "confidence": 0.981
    }
  ]
}

This is where most “per-page OCR” tools fall down. They’ll give you text per page, but they don’t tell you that pages 1–3 are a BOL and page 6 is a POD. You end up writing brittle regexes and heuristics. In Bem, Identify is a built-in primitive with evals and versioning.

3. Route: Send each document to the right workflow

Once each segment is labeled and scored, a Route step decides what happens next:

  • High-confidence documents (e.g., ≥ 0.98) go straight into their type-specific extraction workflow:
    • BillOfLading → BOL_Extraction_v12
    • RateConfirmation → RateConf_Extraction_v7
    • ProofOfDelivery → POD_Extraction_v3
  • Medium-confidence docs (e.g., 0.90–0.98) can:
    • Still route automatically but flag for post-extraction review
    • Or go to a human review surface for quick type confirmation
  • Low-confidence or unknown docs:
    • Route to an “Unclassified Documents” queue
    • Or a generic “catch-all” schema to avoid silent failure

Routing rule example (pseudo-config):

{
  "routes": [
    {
      "when": "doc.type == 'BillOfLading' && doc.confidence >= 0.98",
      "workflow": "BOL_Extraction_v12"
    },
    {
      "when": "doc.type == 'RateConfirmation' && doc.confidence >= 0.97",
      "workflow": "RateConf_Extraction_v7"
    },
    {
      "when": "doc.type == 'ProofOfDelivery' && doc.confidence >= 0.97",
      "workflow": "POD_Extraction_v3"
    },
    {
      "when": "doc.confidence < 0.97",
      "workflow": "DocumentType_Review_Surface"
    }
  ]
}

From here, each workflow is just a normal Bem pipeline: Extract → Enrich → Validate → Sync to your TMS, ERP, or billing system.

Common Mistakes to Avoid

  • Trying to extract before you split:
    Running a generic “shipping packet extractor” on the entire PDF and then trying to sort fields post-hoc leads to collisions and ghost fields (e.g., line items from the rate confirmation showing up on the BOL). Always Split + Identify before extraction.

  • Assuming one schema fits all document types:
    A BOL, rate confirmation, and POD share some fields (load ID, carrier, consignee), but they’re structurally different. Forcing a single schema on mixed content makes validation useless. Instead, keep schemas per type and let the Identify + Route chain decide which one applies.

Real-World Example

Take a 6-page logistics packet in a single PDF:

  • Pages 1–3: BOL (with line items, weights, origin/destination)
  • Pages 4–5: Rate confirmation (with lane, fuel surcharge, accessorials)
  • Page 6: Signed POD (with signatures, timestamps, handwritten notes)

In Bem, the workflow might be shipping-packet-extraction · v59, and the trace would show:

  1. Upload & Identify packet
    • Input: shipping_packet_0423.pdf (6 pages)
  2. Split (split-documents_13 · 6.4s runtime)
    • seg_1 (pages 1–3)
    • seg_2 (pages 4–5)
    • seg_3 (page 6)
  3. Identify document types
    • seg_1 → BillOfLading (99% confidence)
    • seg_2 → RateConfirmation (99% confidence)
    • seg_3 → ProofOfDelivery (98% confidence)
  4. Route to workflows
    • seg_1 → BOL_Extraction_v12
    • seg_2 → RateConf_Extraction_v7
    • seg_3 → POD_Extraction_v3
  5. Extract with schema validation
    • Each workflow outputs schema-enforced JSON with per-field confidence and hallucination checks.
  6. Enrich & Validate
    • Join with your Collections: carrier master, lane pricing, GL codes.
    • Validate totals (e.g., line items vs BOL totals vs rate confirmation).
  7. Sync
    • Post BOL data into TMS.
    • Trigger billing from rate confirmation.
    • Attach POD to the load record.

Operationally, you get:

  • One call to Bem with a messy packet.
  • Three clean, schema-valid outputs (BOL JSON, rate confirmation JSON, POD JSON).
  • Full traceability of how the PDF was split and classified.

Pro Tip: Treat Split + Identify as their own versioned “sub-workflow” with evals. Build a small golden set of mixed packets (BOL + rate confirmation + POD in various orders, carriers, and layouts), then track F1 for both segmentation and classification over time. When you ship a new model version, you’ll know immediately if your split logic regressed before it hits production.

Summary

Automatically classifying and splitting mixed document packets isn’t a nice-to-have; it’s the foundation of any serious logistics or AP automation pipeline. The pattern is simple but non-negotiable:

  1. Split the packet into logical segments using layout + learned patterns.
  2. Identify each segment’s document type with confidence scores.
  3. Route every document into a type-specific extraction and validation workflow, backed by schema enforcement and exception handling.

Bem bakes this into the infrastructure, not as a pile of regexes and page counters. You send one PDF. You get strictly typed JSON per document type—or explicit exceptions when something doesn’t meet your thresholds.

Next Step

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