ServicesB2B OperationsDocument Processing
Per 1000 docs · ongoing

Document Processing.

Pull structured data out of contracts, KYC packs, invoices, and forms — at scale, with citations.

Document AI used to mean rules-based OCR that broke on every layout change. Vision-LLMs flipped that — but operating them at scale means handling confidence thresholds, hallucination guardrails, and the human-review queue for the 5–10% that genuinely need eyes. We run the whole stack.

The numbers
≥97%
field-level accuracy
100%
answers cite source span
≤8 sec
p50 per-document latency
12 langs
incl. Hindi · Devanagari OCR
▣ What you get

Deliverables.

Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.

01

Ingestion + classification

Documents come in via API, S3, email, or scanner. Auto-classified by type (invoice / KYC / contract / claim) with model confidence scoring.

02

Extraction layer

Vision-LLMs (Claude / Gemini / GPT-4V) extract structured fields per your schema. Every extracted value carries a bounding-box citation back to the source page.

03

Validation rules

Cross-field checks (totals match, dates in range, entity exists in your CRM), regex validators, and a confidence threshold that routes ambiguous docs to human review.

04

Human review console

Low-confidence docs queued to ops staff with the source page + extracted fields side-by-side. Corrections train the next iteration.

⌖ How we work

The engagement.

PHASE 011 week

Schema + samples

Define output schema, gather 100–200 sample documents covering edge cases. Score baseline accuracy on a stock model.

PHASE 021–2 weeks

Tune

Iterate on prompts, few-shot examples, validation rules. Tune the confidence threshold to balance auto-approval vs review queue size.

PHASE 031 week

Integrate

Wire to your CRM / DMS / claims system, set up the review console for ops staff, runbook for exception handling.

PHASE 04Ongoing

Operate

We staff the review pod (or you do). Monthly accuracy reports, schema-drift alerts, model upgrades when frontier models improve.

▤ Tools we use

Pragmatic stack.

Best-in-class where it matters; boring and battle-tested everywhere else.

OCR
Tesseract · AWS Textract · Google DocAI
Vision-LLM
Claude · Gemini 2.5 · GPT-5
Parsing
Unstructured · LlamaParse · Docling
Validation
Pydantic · custom rule engine
Storage
S3 · GCS + ACL-aware index
Review UI
Custom Next.js review console
¤ Pricing

Engagement model.

Per 1000 docs
From $30per 1000 documents (volume-tiered)

Per-1000 rate includes review pod time at agreed throughput; scales down significantly past 100K docs / month. One-time setup fee for schema + integration is quoted per project after the sample review.

  • Schema + sample-set tuning
  • Vision-LLM extraction
  • Validation rules engine
  • Review console + queue
  • Citations for every field
  • CRM / DMS integration
  • Monthly accuracy reports
? FAQ

Common questions.

What document types do you handle?

Invoices, purchase orders, contracts, KYC packs (PAN / Aadhaar / passport / utility bill), insurance claims, medical forms, legal contracts, GST filings. Pretty much anything humans currently key in by hand.

Can it run on-prem?

Yes — for BFSI, government, and healthcare clients with data-residency rules. We deploy vision-LLMs on your GPU cluster. Throughput is lower but compliance is intact.

What about handwritten documents?

Vision-LLMs handle clear handwriting reasonably well; messy doctor’s prescriptions and field forms still need a human pass. We auto-route based on confidence.

How do you measure accuracy?

Field-level F1 against a held-out gold set, plus end-to-end auto-approval rate. We share a monthly accuracy report with the breakdown by document type and field.

Now booking Q3 2026

Let's build the
next chapter of your business.

Quick chat on WhatsApp. We'll map your highest-leverage AI bet, show you a reference architecture, and price the first slice.

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shipped projects
12
industries
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certified
98.4%
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