QuillectIntelligent document processing: Every field, every document, traced back to the page.
Any document or scan becomes clean, validated, structured data — every value anchored to its exact place on the source page and cross-checked by a deterministic verifier.
A 30-minute working session, then a proof of concept on your own data — both at no cost. A specialist responds within one business day.
- Page-anchored
- Every value linked to source coordinates
- Scale, cost-effectively
- Machine-speed throughput at a fraction of manual cost
- Immutable log
- Every action recorded for audit & replay
Industries
All industries
Works with
Amazon Textract · PDF & scans · Master data
Built for
Shared services & operations heads · Finance & AP leaders · Banking, insurance & NBFC ops · Logistics & trade documentation teams
Built so the output can be verified, field by field.
Evidence
Page-anchored values
Every field the model returns is linked back to its exact bounding box and coordinates on the source page, so each value can be traced to its origin.
Control
Deterministic verification
A rule-based verifier cross-checks every field — arithmetic, format, 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 runs
Each stage, and what it hands to the next.
- 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.
Inside the product
What it actually runs.
Cost-effective, intelligent document processing at scale
Swipe the diagram
How Quillect works
Swipe the diagram
Included free · Demo + PoC
Send us a sample set — see the extraction on your own documents.
One document type · your sample set · no cost
How it runs
- 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
What 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
What you get back
- 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
The demo and the PoC come together, at no cost. You keep the findings whether or not you go ahead — no licence, no commitment, no procurement paperwork to start.
Before you ask us
Where it runs, what it touches, what it costs.
- Where does it run?
- In your cloud or ours, whichever your security review prefers. OCR runs through Amazon Textract in the region you nominate.
- What 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.
- Is 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.
- What 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.
How 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 human 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.
Does 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 look at rather than silently guessed.
What makes it cost-effective at scale?
Processing is tiered rather than sending every page through the heaviest model, so throughput is machine-speed at a fraction of manual cost. The PoC gives you a cost-per-document estimate at your actual volume.
Can 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.
Quillect
See Quillect on your documents.
How it works and about humaineeti
How it works
One free engagement. Then production.
01Free
The demo
30 minutes on your use case, with the product open.
02Free
The proof of concept
Scoped to your own data. The findings are yours either way.
03
Production, governed
Approval gates, audit trails and data residency, in your environment.
About humaineeti
Agentic, but accountable.
humaineeti — human + AI + neeti — engineers agentic AI for the enterprise from Mumbai and Kolkata. Ten solutions, and custom builds held to the same standard.
- Evidence, not assertion
- A human on the gate
- Your cloud, your data
- Auditable by design