Your operational data is trapped in scanned PDFs. We turn it into SQL rows you can query.

Faxes, filings, invoices, intake forms, and decades of archived paperwork hold real operational data, but as pixels, not records. An OCR-to-SQL pipeline turns those documents into validated database rows on a schedule, so the business queries data instead of opening files one at a time.

Request a workflow review See the pipeline, stage by stage

Manual re-keying is a hidden cost center

Every organization that receives paperwork eventually pays someone to read documents and type what they see into a system. That work is slow, error-prone, and endless.

Staff hours spent retyping documents

Invoices, filings, and forms arrive as scans, and someone opens each one to key the same handful of fields into a database or spreadsheet. Multiply minutes per document by the yearly volume and the number gets uncomfortable.

Typos become downstream data problems

Manual entry introduces transposed digits, wrong dates, and inconsistent names. Those errors surface later as failed reconciliations, wrong reports, and decisions made on bad numbers.

Archives nobody can query

Years of documents sit in folders as images. Questions like how many, since when, from whom, and what changed are unanswerable without opening files one by one, so nobody asks them.

The pipeline, stage by stage

OCR alone is not the product. The product is a disciplined pipeline where extraction is checked, validated, and loaded on a schedule, and failures are visible instead of silent. Before any model reads a page, we define the exact fields, allowed values, and missing-data behavior.

  1. Ingest

    Documents arrive from a watched folder, an inbox, a portal download, or a public source. Each file is registered with its origin, timestamp, and type so nothing is processed twice and nothing disappears.

  2. OCR

    Scanned pages become machine-readable text. Image quality, rotation, and layout are handled here, and pages that fail conversion are flagged rather than passed through empty.

  3. Extraction with a two-model cross-check

    Two independent models extract the same schema fields from the text. Where they agree, confidence is high. Where they disagree, the record is flagged for review instead of silently picking one answer.

  4. Validation

    Extracted values are checked against rules: required fields present, dates parse, numbers in range, identifiers match known formats, and cross-field logic holds. Failures route to an exception queue with the source image attached.

  5. Load

    Validated rows load into your SQL target with full lineage. Every row keeps a pointer to the source document, the extraction confidence, and the run that produced it.

  6. Scheduled refresh

    The pipeline runs on a schedule, nightly, hourly, or on arrival, with monitoring and run logs. New documents become new rows without anyone remembering to do anything.

In production: daily regulatory filings to Snowflake

This is not a proposed architecture. It is the pipeline running behind OilGasOrGrass.com every day.

OilGasOrGrass.com

North Dakota well intelligence. Nine stages, daily, unattended.

A fully automated nine-stage pipeline processes North Dakota Industrial Commission permit filings daily: new documents are identified and downloaded, scanned PDFs are OCRed, two independent AI models (Grok and Claude Haiku) extract and cross-check structured fields, and validated records load into Snowflake where they are searchable through Cortex Search. It runs unattended at 2 AM Mountain Time.

See the full pipeline

Working with medical records or other PHI? The same extraction discipline applies inside a HIPAA boundary. See HIPAA-compliant document search for how we handle deployment and agreements when documents are sensitive.

What separates a demo from a pipeline

Anyone can OCR one document in a notebook. A system is what happens when document ten thousand arrives on a Saturday.

What the demo does

Upload a clean sample PDF.
Extract the fields.
Show a table.

What the system has to survive

Skewed scans and stamps over text
Two models disagreeing on a total
A date that parses two ways
A vendor that renamed itself
A duplicate filing
A failed run at 2 AM
Every one of these visible, queued, and recoverable

The value is not the extraction alone. It is schema, validation, lineage, exception handling, and scheduling: the parts that make the rows trustworthy.

What a first project looks like

Incremental on purpose: you see extraction quality on your own documents before committing to a production build.

  1. Discovery conversation

    We look at the documents, the fields you actually need, the volumes, and the target system. You leave knowing whether the extraction is feasible and what the pipeline would look like.

  2. Scoped pilot

    A pilot batch of your real documents, including the ugly ones, run through the full pipeline so you can judge accuracy, see the exception queue, and check the loaded rows yourself.

  3. Production with scheduled runs

    Monitoring, run logs, lineage, and an operational runbook. The pipeline becomes infrastructure: documents in, validated rows out, exceptions visible, nobody retyping anything.

Questions teams ask before building this

How accurate is OCR on old or low-quality scans?

It depends on the source material, which is why we do not promise a blanket number. The pipeline compensates with a dual-model cross-check, validation rules, and an exception queue — so low-confidence extractions are reviewed by a person instead of loaded as fact.

What happens when the extraction is wrong?

Wrong values that violate validation rules never reach the database — they land in a review queue with the source image beside the extracted fields. Corrections are logged and used to tighten prompts and rules, so the same mistake gets rarer over time.

Can it read handwriting?

Printed text is the reliable case. Handwriting recognition varies widely with the source material, so we test it on your actual documents during a pilot instead of promising a blanket accuracy number up front.

Which databases can the pipeline load into?

Any SQL target. Production pipelines we run today load Snowflake and SQLite, and the same pattern applies to SQL Server, Postgres, MySQL, or a warehouse you already operate. The load stage adapts to the target — the discipline stays the same.

What does a first project look like?

A discovery conversation about the documents and the fields you need, then a scoped pilot on a real batch — including the worst scans you have — so you can judge extraction quality before committing to a production build.

Talk through your document backlog

Tell us what the documents are, roughly how many arrive and sit in the archive, and which fields you wish were in a database. We will tell you honestly whether a pipeline is worth building.

  • We reply by email with an honest answer on whether a pipeline is the right fix.
  • If it is, the next step is a scoped pilot on your real documents.
  • Denver, Colorado. We sign Business Associate Agreements where PHI is involved.