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Guides on turning business documents into structured data: OCR and parsing, schema design, evaluation, security, and the accounts payable and logistics work around them.
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Freight Invoice Audit: How to Match Carrier Invoices Against the Bill of Lading and Rate Confirmation Before You Pay
Freight Invoice Audit: How to Match Carrier Invoices Against the Bill of Lading and Rate Confirmation Before You Pay, readCarrier invoices drift from the rate you agreed. Learn how to audit each one against the bill of lading and rate confirmation, and catch accessorial overbilling.
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Vendor Statement Reconciliation: How to Match a Supplier Statement Against Your AP Ledger Without Ticking Lines by Hand
Vendor Statement Reconciliation: How to Match a Supplier Statement Against Your AP Ledger Without Ticking Lines by Hand, readSupplier statements rarely match your AP ledger on the first pass. Reconcile them step by step: why balances drift and how to clear each difference.
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One PDF, Twelve Documents: Splitting and Classifying a Batch Before You Extract
One PDF, Twelve Documents: Splitting and Classifying a Batch Before You Extract, readReal uploads are not single documents — they are 40-page bundles holding invoices, receipts, contracts and a fax cover sheet. Here is how to find the boundaries, classify each piece, and keep the split auditable before extraction ever runs.
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Schema Design for Document Extraction: The Hour That Decides Your Accuracy
Schema Design for Document Extraction: The Hour That Decides Your Accuracy, readMost extraction projects are debugged at the model when the defect is in the schema. Here is how to design the JSON contract — field names, types, enums, nulls, nesting, provenance and versioning — so the model has one obvious right answer for every field.
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Prompt Injection Through Documents: How to Harden an LLM Extraction Pipeline
Prompt Injection Through Documents: How to Harden an LLM Extraction Pipeline, readAn invoice can carry instructions as easily as it carries a total. Here is how indirect prompt injection reaches an extraction pipeline, why instructing the model to ignore it does not work, and the architecture that keeps a hostile PDF from changing what your systems do.
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Document Extraction Accuracy: Build a Field-Level Eval Set Before You Trust Any Model
Document Extraction Accuracy: Build a Field-Level Eval Set Before You Trust Any Model, readA single “99% accurate” number tells you nothing about whether an extraction pipeline is safe to automate. Here is how to build a field-level eval set, pick the right metrics, and turn accuracy into a routing decision instead of a marketing claim.
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How to Integrate Automatic Document Extraction into Your System via API (A Practical Guide for Technical Teams)
How to Integrate Automatic Document Extraction into Your System via API (A Practical Guide for Technical Teams), readA practical guide to integrating a document extraction API: authenticate, upload, poll or use a webhook, consume the JSON, and make retries safe.
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JWT, IAM, and Auth0: Authentication and Authorization Explained (Without the Confusion)
JWT, IAM, and Auth0: Authentication and Authorization Explained (Without the Confusion), readDemystify JWT, IAM, and Auth0 — learn authentication vs. authorization, roles, scopes, and secure access for apps, APIs, and microservices.
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Markdown vs. JSON vs. Raw Text: How to Optimize Token Usage and Model Performance with Structured Parsing
Markdown vs. JSON vs. Raw Text: How to Optimize Token Usage and Model Performance with Structured Parsing, readWrong format, wasted tokens: markdown vs JSON vs raw text for LLM prompts and outputs, compared on token count, parse reliability and accuracy.