AI Document Processing and Data Extraction

Documents that still get retyped by hand

Almost every operations team has a stack of documents that someone reads and types into a system. A freight forwarder keys bills of lading and packing lists into its transportation system. A general contractor’s office checks every subcontractor’s certificate of insurance against contract requirements and chases expired ones. A specialty clinic receives faxed referrals and enters patient, diagnosis and requested service into its practice management system. A distributor receives customer purchase orders as PDFs and retypes each line.

These documents vary by sender, arrive in poor scan quality and often bundle several forms in one file, which is why template based OCR tools struggled with them. Current models read layout and language together, so a single pipeline can handle dozens of formats for the same document type without a template per sender.

Tepia adds document processing to the system you already run. Extracted data lands in your existing database or application through its API, and the people who type documents today become reviewers of the few that need attention.

The extraction pipeline, stage by stage

Tepia builds every document workflow from the same stages, adjusted per document type:

1. Intake: monitored inboxes, upload screens in your web or mobile app, scanner folders, fax to email and SFTP.
2. Preparation: page rotation, deskewing, splitting combined PDFs and detecting duplicates already processed.
3. Reading: OCR and layout analysis through Azure AI Document Intelligence, Amazon Textract or Google Document AI, chosen to match your cloud.
4. Classification: deciding what each document is, so a packing list is not processed as a bill of lading.
5. Extraction: a language model fills a JSON schema for that document type, returning a value and source location for each field.
6. Validation: rules and lookups against your data, described below.
7. Review: anything below threshold or failing a rule goes to a person.
8. Export: clean records written to your system, with the original file linked.

Each stage logs its result, so when a field is wrong Tepia can see whether OCR misread it, classification picked the wrong type or the model chose the wrong value.

Document types and the checks that make them trustworthy

Validation is where Tepia spends the most design time, because it decides how many documents can skip human review. Examples from common workflows:

A document that passes every check can flow straight into your system. One that fails a check is routed with the reason attached, so the reviewer knows exactly what to look at.

Confidence, human review and audit

Tepia sets confidence thresholds per field, not per document. A misread reference number may be harmless if the validation lookup catches it, while a misread expiration date on an insurance certificate is not, so that field gets a stricter threshold.

The review screen Tepia builds shows the page image on one side and the extracted fields on the other, with each field highlighting its source region when selected. Reviewers can correct a value, reclassify the document or reject it, and every action is logged with who made it and when. Corrections are added to the evaluation set, so the pipeline is measured against the exact mistakes it made before.

For regulated documents such as medical referrals, Tepia applies HIPAA aware handling: encrypted storage, access by role, audit logs and model providers covered by a business associate agreement. Retention rules decide how long original files are kept. See HIPAA compliant software development for the wider controls.

How Tepia approaches AI document processing

Tepia’s six phase process is applied with the people who process documents today, under a US based project manager.

1. Discovery: collection of a few hundred real samples per document type, including the worst scans, plus interviews on how each is handled now. Deliverables are an Investigation Summary, an Interview Summary and User Stories such as a coordinator flagging a certificate that names the wrong additional insured.
2. Design: field schemas, validation rules and wireframes of the review queue and document detail screen.
3. Development and Testing: intake and extraction measured against labeled samples first, then validation and export. Alpha on historical documents, Beta on live intake in parallel with manual entry, with functional, user acceptance and non functional test plans covering accuracy per field and throughput.
4. Training: hands on sessions for reviewers using real exceptions.
5. Launch: manual entry retired one document type at a time, with backfill of historical documents where useful.
6. Support: new document types, sender formats and rule changes handled as they appear.

Typically Discovery takes 1 to 3 months and development and QA 2 to 6 months. Read about our process.

A document processing platform or a custom pipeline

Packaged document processing products work well for common document types with standard connectors to common systems. If yours fit that description, Tepia will recommend one.

Custom pipelines win when documents are specific to your industry, when validation has to check your own records, when results must appear inside your own app rather than a separate portal, or when several document types feed one workflow. A hybrid is common: Tepia uses a cloud OCR service for reading and builds classification, validation, review and export around it.

For supplier bills specifically, see AI accounts payable automation. For storing and finding processed files, see document management system development. Then send Tepia a sample set of your hardest documents.

Frequently asked questions