Quillect · Document Intelligence Every field, every document, traced back to the page.

Extract
Validate
Audit
IDP at scale

Turn any document or scan into clean, validated, structured data — every value anchored to its exact place on the source page and cross-checked by a deterministic verifier.

Book a free demo Start a no-cost proof of concept

Page-anchored: Every value linked to its source coordinates
Cost-effective at scale: Machine-speed throughput at a fraction of manual cost
Immutable log: Every action recorded for audit and replay

Why Quillect

Built so the output can be verified, field by field.

Evidence

Page-anchored values

Every field the model returns is linked to its exact bounding box and coordinates on the source page, so you can trace each value to its origin.

Control

Deterministic verification

A rule-based verifier cross-checks every field — arithmetic, format and master data — independently of the model, before the data reaches your systems.

Accountability

Immutable audit log

Every extraction, correction and approval is written to a tamper-evident log that supports full replay for audit.

How it works

How Quillect runs.

/ 01
Extract
Amazon Textract OCR reads text, tables and form fields from any document type or scan quality.
/ 02
Anchor
Every extracted value is mapped back to its exact bounding box on the source page.
/ 03
Validate
A deterministic, rule-based verifier cross-checks each field against expected logic.
/ 04
Audit
Every extraction, correction and approval is written to an immutable audit log.

See it on your own data

Watch Quillect work on a real use case, in a 30-minute live demo.

Free. Then a scoped proof of concept at no cost, before you commit.

Book a free demo Download the two-pager (PDF)

Inside the solution

Quillect, drawn out.

Cost-effective, intelligent document processing at scaleAny document, PDF or scan, goes in; each value is anchored to its exact place on the source page through Amazon Textract OCR; clean, structured, validated data comes out.

How Quillect worksExtract with OCR across any document type or scan quality, anchor every value to its bounding box, validate it against expected logic and write everything to an immutable log.

What the extraction pipeline runsAmazon Textract OCR, schema extraction, page anchoring, deterministic verification and an immutable audit log.

What you get

Outcomes you can hold us to.

Any document or field — PDF or scan — turned into clean, structured data
Every value anchored to its exact coordinates on the source page
A deterministic verifier checking arithmetic, format and master data
Machine-speed throughput at a fraction of manual processing cost
An immutable, tamper-evident log of every extraction, correction and approval
Full replay for audit, field by field

Built for

Shared services & operations heads
Finance & AP leaders
Banking, insurance & NBFC ops
Logistics & trade documentation teams

Industries

All industries

Works with

Amazon Textract
PDF & scans
Master data

Free demo and no-cost proof of concept

Send us a sample set and see extraction on your own documents.

One document type · your sample set · no cost

The scope

You pick one document type and send a representative sample
We configure extraction, verification rules and the field schema
You see page-anchored output and the verifier’s findings, field by field

You provide

A sample set of one document type (redacted is fine)
The target field schema, or your current manual template
A contact who knows the current exception cases

You get

Structured output for your sample, with every field page-anchored
A field-level accuracy and exception report
A throughput and cost-per-document estimate at your volume

Start a no-cost proof of concept Book a free demo first Download the two-pager (PDF)

FAQ · Deployment, data and cost

What buyers ask before they commit.

Have a question that is not answered here?

Ask a senior engineer

01How do we know the extracted values are right?

Two independent mechanisms. Every value is anchored to its bounding box on the source page, so a person can verify it in one click, and a deterministic, rule-based verifier cross-checks arithmetic, format and master data before the data reaches your systems.

02Does it work on scans and poor-quality documents?

Yes. OCR handles scans, and page anchoring means low-confidence fields are flagged with the exact region to check, rather than silently guessed.

03What makes it cost-effective at scale?

Pages are processed in tiers rather than all going through the heaviest model, so throughput is machine-speed at a fraction of manual cost. The proof of concept (PoC) gives you a cost-per-document estimate at your actual volume.

04Can we satisfy an auditor with this?

That is the design point. Every extraction, correction and approval is written to a tamper-evident log that supports full replay, and every value traces back to a coordinate on a page.

05Where does it run?

In your cloud or ours, whichever your security review prefers. OCR runs through Amazon Textract in the region you nominate.

06What can it change without us?

Nothing downstream. Extracted data reaches your systems only after the deterministic verifier passes it and, where you require it, a person approves it.

07Is our data used to train models?

No. Your documents are processed to produce your output. They are not used to train or fine-tune anything.

08What does it cost after the sample run?

The sample run is free and returns a cost-per-document estimate at your volume. Production is priced per document, not per seat.