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AI and Automation

Document Processing AI (OCR + LLM)

OCR and LLM pipelines that turn documents into structured data.

Overview

What we deliver

We build document processing systems that combine OCR with large language models to extract, classify, and route data from any document format.

We design and deploy document processing pipelines that read, understand, and structure information from PDFs, scans, images, and mixed-format files. Our approach pairs production OCR engines with large language models to handle messy real-world documents, including handwriting, low-quality scans, and multi-language content. We build extraction logic that captures fields, tables, and signatures, then route the output to your systems through APIs, RPA, or direct database writes. Each pipeline includes validation rules, human-in-the-loop review for low-confidence results, and audit logs for compliance. We work with your team to map document types, define schemas, and tune accuracy against your business rules. The result is a document workflow that runs around the clock, reduces manual data entry, and gives your operations team a consistent stream of clean structured data ready for downstream use.

Fit Check

Built for teams like yours

Who it's for

  • Finance teams
  • Insurance carriers
  • Legal operations
  • Healthcare administrators
  • Logistics companies

Pain points we solve

  • Manual data entry backlogs
  • Inconsistent extraction quality
  • Slow document turnaround
  • High labor cost per document
  • Compliance audit gaps
What's included

Capabilities

Everything we cover in this engagement.

  • Document classification
  • Field-level extraction
  • Table and line item parsing
  • Handwriting recognition
  • Multi-language OCR
  • Confidence scoring
  • Human review workflows
  • System integration
How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We catalog document types, volumes, and downstream systems.

02

Schema design

We define extraction fields, validation rules, and routing logic.

03

Pipeline build

We build OCR and LLM stages with confidence thresholds.

04

Pilot

We run a controlled pilot and tune accuracy against your samples.

05

Rollout

We move to production with monitoring and review queues.

What you get

Deliverables & outcomes

What you get

  • Production document pipeline
  • Extraction schema documentation
  • Review queue interface
  • API endpoints and webhooks
  • Accuracy benchmark report
  • Operations runbook

Outcomes you can expect

  • Lower cost per document
  • Faster cycle time
  • Higher extraction accuracy
  • Reduced manual touchpoints
  • Better audit traceability
Timeline

6 to 10 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Azure Document Intelligence, AWS Textract, Google Document AI, OpenAI, Anthropic

KPIs we track

Extraction accuracy, Documents per hour, Manual review rate, Cost per document, Cycle time

Client stories

What clients say

"

We had 14 cornerstone pages stuck on page two for 18 months. Their SEO crew rewrote the internal linking, cleaned up our schema, and shipped 22 supporting briefs over a quarter. Eight of those pages broke top three by month five. Organic pipeline went from a trickle to our second-largest source. Felt like watching interest compound.

James T.
"

We were drowning in tier-one tickets about password resets and appointment changes. They built a deflection layer on top of our help desk and kept their agents in the loop for anything sensitive. Volume to humans dropped 58 percent in two months and our patient NPS held steady. The hybrid handoff is the part most vendors get wrong. They did not.

P.M.
FAQ

Frequently asked questions

Quick answers to the questions we hear most.

How accurate is the extraction?
Accuracy depends on document quality and field complexity, and we benchmark against your samples during pilot to set realistic targets.
Can it handle handwritten forms?
Yes, we use OCR engines tuned for handwriting and add LLM post-processing to improve reliability on cursive and mixed scripts.
What happens with low-confidence results?
We route them to a human review queue with field highlighting so reviewers can correct and approve quickly.
Do you support non-English documents?
Yes, we support most major languages and can configure pipelines for multilingual workflows.
How does it integrate with our systems?
We expose REST APIs, webhooks, or direct database writes, and we can integrate with ERPs, CRMs, and document management systems.

Ready to automate your document workflows?

We will scope a pilot pipeline against your real documents and show measurable accuracy.