Service Document AI

The documents read themselves now.

Somewhere in your operation, smart people are retyping numbers from PDFs into systems. Contracts into abstract sheets, claims into queues, invoices into ERP fields, forms into forms. Modern document AI reads the messy, scanned, seventeen-format reality of enterprise paper — and I build the pipelines that extract, validate, and route it with accuracy you can audit and a cost-per-document you can watch fall.

~75% Typical cost-per-doc reduction
Human-in-loop Below threshold, always reviewed
Audit trail Every field, back to the pixel

Legacy OCR failed at this because enterprise documents are hostile: scanned at an angle, formatted seventeen ways by seventeen counterparties, with the critical clause on page 40 phrased differently every time. Template-based extraction breaks the day a vendor redesigns their invoice. So the work stayed manual, and "document processing" became a headcount line.

LLM-based document intelligence changed the economics. Models now read documents more like your analysts do — by understanding, not by template. My pipelines pair that with validation rules, confidence scoring, and human review queues, so every extracted field carries a confidence score and a link back to the exact spot on the page it came from. High-confidence documents flow straight through; edge cases go to humans whose corrections retrain the system.

The paper this eats

  • Contracts — terms, dates, parties, obligations, and renewal triggers extracted into your CLM or tracker, clause-level citations included.
  • Claims and submissions — intake packets classified, key fields extracted, completeness checked, and routed before a human touches the file.
  • Invoices and POs — line items matched, exceptions flagged, and clean records posted to the ERP — cents per document, not dollars.
If a document has been read the same way a thousand times, the thousand-and-first read shouldn't cost analyst hours.

From inbox to system of record, four stages.

Each stage measured — accuracy, straight-through rate, and cost per document.
01 / Ingest
Every format, one pipeline
PDFs, scans, emails, faxes, and photos normalized and classified on arrival — no pre-sorting by humans required.
02 / Extract
Understanding, not templates
LLM-based extraction that survives format changes, with every field scored for confidence and linked to its source on the page.
03 / Validate
Trust, verified
Business rules, cross-checks against your systems, and human review queues for anything below the confidence threshold.
04 / Route
Into the systems that act
Clean, validated data posted to your ERP, CLM, claims platform, or database — with an audit trail from field back to pixel.
ContractsClaimsInvoicesFormsOCR + LLMHuman-in-the-loopAudit trail

Questions, answered.

The ones every buyer asks first.
How accurate is AI document extraction?

On well-built pipelines, high-confidence fields routinely exceed manual keying accuracy — humans fatigue, models do not. The honest metric is field-level: each extraction carries a confidence score, everything below threshold gets human review, and the published accuracy number comes from ongoing audits, not vendor brochures.

What volume justifies building this?

As a rule of thumb: if a document type consumes more than one FTE of reading and keying, the pipeline pays for itself inside a year — usually much faster. Below that, I will tell you to keep the human and spend the budget elsewhere. The assessment is honest about this.

Can it handle handwriting and poor scans?

Largely, yes — modern vision models read handwriting, skewed scans, and stamps far better than legacy OCR. Genuinely illegible documents get flagged for human review rather than guessed at, which is the behavior you actually want in anything auditable.

How does this stay compliant for regulated documents?

The pipeline runs inside your boundary — cloud VPC or fully on-prem — with access controls, PII handling rules, and an immutable audit trail linking every extracted value to its source location and reviewer. Compliance is designed in at the architecture stage, not attested afterward.

Related work.

These usually travel together.
Service / 07
Enterprise workflows
Multi-system orchestration across Salesforce, ServiceNow, SAP, and your custom APIs — finally talking, with an audit trail.
Explore
Industry / 03
Legal
AmLaw firms, in-house counsel, and legal tech. Contract and document intelligence — every answer traceable to the document.
Explore
Industry / 04
Insurance
Carriers, brokers, MGAs, and insurtech. Underwriting support, claims automation, and submission intake — fully auditable end to end.
Explore
Service / 02
RAG systems
Retrieval pipelines that put your institutional knowledge to work. Grounded, source-cited answers on your own data — without leaking it.
Explore
Service / 03
AI infrastructure
Local model deployment and hardware builds for organizations that need their data to stay inside their walls. Nothing leaves the perimeter.
Explore
Service / 18
Data readiness
The unglamorous work that decides whether AI works at all: access, structure, quality, and permissions sorted before the build.
Explore

Retire the retyping this quarter.

Pick your ugliest document queue — the one nobody wants to own. Send me a sample batch and I'll tell you what straight-through processing would cost and save.

Let's talk

Markets served.

Remote-first across the United States and internationally — including these markets.

New York City, New York (NY)

Los Angeles, California (CA)

Chicago, Illinois (IL)

Houston, Texas (TX)

Phoenix, Arizona (AZ)

Philadelphia, Pennsylvania (PA)

San Antonio, Texas (TX)

San Diego, California (CA)

Dallas, Texas (TX)

San Jose, California (CA)

Austin, Texas (TX)

Jacksonville, Florida (FL)

Fort Worth, Texas (TX)

Columbus, Ohio (OH)

Charlotte, North Carolina (NC)

Indianapolis, Indiana (IN)

San Francisco, California (CA)

Seattle, Washington (WA)

Denver, Colorado (CO)

Washington, District of Columbia (DC)

Boston, Massachusetts (MA)

El Paso, Texas (TX)

Nashville, Tennessee (TN)

Detroit, Michigan (MI)

Oklahoma City, Oklahoma (OK)

Portland, Oregon (OR)

Las Vegas, Nevada (NV)

Memphis, Tennessee (TN)

Louisville, Kentucky (KY)

Baltimore, Maryland (MD)

Milwaukee, Wisconsin (WI)

Albuquerque, New Mexico (NM)

Tucson, Arizona (AZ)

Fresno, California (CA)

Sacramento, California (CA)

Kansas City, Missouri (MO)

Atlanta, Georgia (GA)

Miami, Florida (FL)

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Raleigh, North Carolina (NC)

Omaha, Nebraska (NE)

Long Beach, California (CA)

Virginia Beach, Virginia (VA)

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Minneapolis, Minnesota (MN)

Tulsa, Oklahoma (OK)

Arlington, Texas (TX)

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Wichita, Kansas (KS)

Cleveland, Ohio (OH)

Tampa, Florida (FL)

Bakersfield, California (CA)

Aurora, Colorado (CO)

Honolulu, Hawaii (HI)

Anaheim, California (CA)

Santa Ana, California (CA)

Corpus Christi, Texas (TX)

Riverside, California (CA)

Lexington, Kentucky (KY)

St. Louis, Missouri (MO)

Stockton, California (CA)

Pittsburgh, Pennsylvania (PA)

Saint Paul, Minnesota (MN)

Cincinnati, Ohio (OH)

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Anchorage, Alaska (AK)

Plano, Texas (TX)

Lincoln, Nebraska (NE)

Orlando, Florida (FL)

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Newark, New Jersey (NJ)

Toledo, Ohio (OH)

Durham, North Carolina (NC)

Chula Vista, California (CA)

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Jersey City, New Jersey (NJ)

St. Petersburg, Florida (FL)

Laredo, Texas (TX)

Madison, Wisconsin (WI)

Chandler, Arizona (AZ)

Buffalo, New York (NY)

Lubbock, Texas (TX)

Scottsdale, Arizona (AZ)

Reno, Nevada (NV)

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Gilbert, Arizona (AZ)

Winston-Salem, North Carolina (NC)

North Las Vegas, Nevada (NV)

Norfolk, Virginia (VA)

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Tacoma, Washington (WA)

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Worcester, Massachusetts (MA)

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Jackson, Mississippi (MS)

Providence, Rhode Island (RI)

Fort Lauderdale, Florida (FL)

Sioux Falls, South Dakota (SD)

Tempe, Arizona (AZ)

Cape Coral, Florida (FL)

Springfield, Missouri (MO)

Pembroke Pines, Florida (FL)

Eugene, Oregon (OR)

Peoria, Arizona (AZ)

Corona, California (CA)

Lancaster, California (CA)

Rockford, Illinois (IL)

Salinas, California (CA)

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Springfield, Massachusetts (MA)

Charleston, South Carolina (SC)

Duluth, Minnesota (MN)

London, England (ENG)

Dublin, Ireland (IRE)