VIPRATECH LABS
Document Intelligence

Turn documents into checked, structured data

We build extraction pipelines that read the documents your teams re-type today, show the source of every value, and send anything uncertain to a person instead of guessing.

Most operations teams still copy data out of PDFs, scans and phone photos before the real work starts. Template OCR breaks on every new layout. We combine layout-aware OCR with large language models and your business rules, so the pipeline copes with new formats, cross-checks documents against each other, and keeps an audit trail of every value and every human correction.

What we build

  • Classification and splitting of mixed document bundles
  • Field and line-item extraction with confidence scores and source-page links
  • Cross-document checks and business-rule validation
  • Reconciliation against ledgers, rate cards or policy rules
  • Reviewer screens and exception queues
  • Integrations with ERP, core systems, CRMs and data warehouses

How we work

  1. 01Agree the extraction schema with the people who use the data
  2. 02Build on a sample of your real, de-identified documents
  3. 03Measure field-level accuracy against what your team entered
  4. 04Set confidence thresholds with you, then roll out

Related work

Claims reconciliation platform

Packaged-food brand (anonymised)

OCR and LLM platform that matches distributor claims spreadsheets against invoices and proofs, applies policy rules, detects duplicates, recalculates amounts, routes uncertain lines to an exception queue and keeps an audit trail. Finance keeps final approval.

RERA project data aggregator

Real-estate data project (anonymised)

Playwright scraper that collects state RERA project listings and full project details into structured Excel output.

Use cases by industry (74)

Legal

FAQs

01Do you need thousands of samples to start?

No. A pilot usually starts with a few hundred representative documents. Because the extraction uses language models with schemas, it does not need a template per layout.

02What happens when the AI is unsure?

Every field carries a confidence score. Below the threshold you set, the field goes to a reviewer with the source page highlighted. Nothing uncertain is pushed downstream silently.

03Can it handle Hindi or regional-language documents?

Yes for most printed documents, and partly for handwriting. The pilot reports accuracy by language and document type so you know where review is still needed.

Akhilesh Mittal

Founded by Akhilesh Mittal. 8 years building a security platform for a New Zealand company. IIT ISM Dhanbad and IMT Ghaziabad. Builds production agentic AI.