How is this different from off-the-shelf OCR or a generic large-language-model extractor?

Operations teams need three things the generic tools do not deliver. A confidence score for every field, not just per document. A direct link from each extracted value to the region of the page it came from. And a reviewer-override audit trail that survives every release. Our platform is engineered for the kind of evidence an auditor would accept, so every extraction can be replayed later, even on a different model version.

Which kinds of documents can the platform read?

Any structured or semi-structured document the team handles today. Purchase orders, invoices, goods receipt notes, identity records, statements of account, claim forms, shipping and trade documents, contracts, application packets. New document types come online in one to two weeks of focused work.

What kind of accuracy can we expect on production documents?

Highly structured documents with clean scans tend to reach the high nineties on a per-field basis. Semi-structured documents settle in the mid-nineties with low-confidence values routed automatically to review. Free-form documents land in the low-to-mid nineties with an explicit exception flow. The platform always surfaces uncertainty rather than hiding it.

How is a reviewer override handled and recorded?

Every override records the reviewer, the timestamp, the original value, the new value, and the reason. The full audit trail is queryable per document, per reviewer, and across a time range. The platform can also replay the original extraction using the model version that produced it, so historical disputes stay reconstructable years later.

Where does the language-model call run, on a hosted service or inside our environment?

It is your choice per engagement. For teams with strict data residency or sensitivity needs, we run language models inside your own environment. For workflows that allow it, we integrate the leading hosted providers with consent and audit logging. The platform is provider-agnostic by design, so the choice can change later without touching the rest of the workflow.

What does a discovery to production timeline look like?

Discovery runs one to two weeks to align on document types, accuracy goals, reviewer flow, and audit needs. An end-to-end working version for a single document type lands in four to six weeks with confidence scoring and audit trail in place. Scaling out to more types, reviewer dashboards, and downstream integrations typically takes another four to eight weeks.

Solutions · AI Document Intelligence

Read every document. Extract every field. Act only on what differs.

Structured documents power every organisation — purchase orders, invoices, receipts, identity records, statements, agreements. We engineered an AI platform that reads them, understands them, compares them line by line, and surfaces only what genuinely needs a person to decide.

Book a discovery call See the flagship workflow

OCR + LLM pipeline · liveaudit-replayable

PAN scan

scan_28492.pdf · 3 pages

OCR

LLM

Audit

Extractedv1.4.2

PANABCPK1234E99%
NameRAVI KUMAR96%
DOB1989-03-1294%
AddressPune, MH 41100187%

lineage: scan_28492.pdf · region 2 · OCR v1.4.2 · LLM gpt-4-cyan

Confidence

Per-field

Logged

Source region

Audit-trail

Reviewer override

Why this matters

Every organisation runs on documents that already speak the same language.

Purchase orders, invoices, and receipt notes carry the same line items in different shapes. Identity records carry the same facts in different formats. Agreements and declarations echo each other across departments. When AI can read these documents the way a careful analyst would, comparison stops being a chore and becomes a moment of clarity.

How the platform works

Six capabilities working as one calm system.

Smart document reading

Reads scans, PDFs, and images of varying quality, identifies the kind of document on its own, and prepares each one for understanding without manual sorting.

Line-by-line understanding

Extracts every field with its own confidence score, recognises items even when wording or formatting changes, and quietly learns the patterns each partner uses.

Side-by-side comparison

Aligns the same line items across the related documents in a clear three-panel view, with quick visual cues for matches, near-matches, and the moments that need attention.

Configurable matching

Define what counts as acceptable variance for each field and each partner. The platform decides what fits, so the team only sees what genuinely needs a person.

Calm exception handling

When something does not fit, the platform surfaces a recommendation drawn from history, with a single click to accept or to escalate, and a note for the audit log.

Audit-ready by default

Every action carries who, when, why, before, and after. Reports cover matches, exceptions, ageing, and trends, exportable in the formats your auditors prefer.

The flagship workflow

3-Way Matching — the workflow we delivered first.

Accounts payable teams across industries compare three documents on every purchase — the purchase order they raised, the invoice the supplier sent, and the goods receipt note that confirms what arrived. The platform brings all three together, line by line, with the intelligence to decide what is acceptable and the discipline to record every step.

What ships in the box

Request and approval workflow that turns a draft into an approved purchase order, with line-item-level review
Document upload and classification that knows a purchase order from an invoice from a goods receipt note on its own
AI extraction engine that pulls every field with confidence, even when scans are imperfect or vendor formats differ
Matching engine that compares all three documents line by line, with tolerances tuned per field and per vendor
Side-by-side comparison view with green, amber, and red cues, plus expandable rows for deeper inspection
Exception dashboard with AI-suggested resolutions, override notes, and escalation paths recorded for audit

Three clean outcomes

Exact match

Every line agrees within the configured tolerance. The platform approves automatically and records the evidence.

Within tolerance

Minor variance inside the allowed range. The team sees a gentle flag and can accept or review with a single click.

Needs attention

Something is meaningfully different. The exception flow takes over, with the AI suggesting the best next step.

Four roles, working together

Requestor
Starts the workflow with the goods or services they need, with the business reason in plain language.
Reviewer
Refines the request, comments on the items, and forwards it onward when it is ready.
Approver
Gives the green light at the line-item level, with the platform handling the rest.
Admin
Manages tolerances, partners, templates, and the people who use the system day to day.

Reports your auditors will recognise

Match summary across documents
Purchase order lifecycle from request to approval to match
Invoice ageing grouped into the buckets your team uses
Exception resolution with category and time-to-clear
User activity for every action and override
Partner-level patterns and repeated variances

The same engine, more workflows

Wherever structured documents need to be read and compared.

The same platform that powers 3-Way Matching fits any workflow with the same shape — across industries. If your team compares two or three documents to decide on a transaction, the engine is ready for it.

Compare a request to its fulfillment

Any time a team asks for something and needs to confirm what arrived matches what was requested.

Compare a claim to its supporting records

Any time a submission needs to be checked against the documents and references that support it.

Compare a declaration to its evidence

Any time a statement of fact must agree with the underlying records that prove it.

Compare an application to its referenced records

Any time a packet of documents has to read consistently, end to end, for a decision to be made.

Built for the kind of trust auditors recognise

Every extraction, every decision, every override, on the record.

Each extracted field linked to the exact region of the page it came from
A confidence score for every field, surfaced before anyone trusts it
Reviewer overrides recorded with reason, original value, and timestamp
Model version logged on every extraction, so any past result can be replayed
Tamper-proof audit logs scoped per role, queryable across any time range
Documents stored securely at rest, transferred securely in transit, every step protected

Frequently asked

AI Document Intelligence, the questions teams ask first.

How it differs from off-the-shelf tools, what it reads, how accurate it is, how audit trails work, where the AI runs, and how long it takes to go live.

How is this different from off-the-shelf OCR or a generic large-language-model extractor?
Operations teams need three things the generic tools do not deliver. A confidence score for every field, not just per document. A direct link from each extracted value to the region of the page it came from. And a reviewer-override audit trail that survives every release. Our platform is engineered for the kind of evidence an auditor would accept, so every extraction can be replayed later, even on a different model version.
Which kinds of documents can the platform read?
Any structured or semi-structured document the team handles today. Purchase orders, invoices, goods receipt notes, identity records, statements of account, claim forms, shipping and trade documents, contracts, application packets. New document types come online in one to two weeks of focused work.
What kind of accuracy can we expect on production documents?
Highly structured documents with clean scans tend to reach the high nineties on a per-field basis. Semi-structured documents settle in the mid-nineties with low-confidence values routed automatically to review. Free-form documents land in the low-to-mid nineties with an explicit exception flow. The platform always surfaces uncertainty rather than hiding it.
How is a reviewer override handled and recorded?
Every override records the reviewer, the timestamp, the original value, the new value, and the reason. The full audit trail is queryable per document, per reviewer, and across a time range. The platform can also replay the original extraction using the model version that produced it, so historical disputes stay reconstructable years later.
Where does the language-model call run, on a hosted service or inside our environment?
It is your choice per engagement. For teams with strict data residency or sensitivity needs, we run language models inside your own environment. For workflows that allow it, we integrate the leading hosted providers with consent and audit logging. The platform is provider-agnostic by design, so the choice can change later without touching the rest of the workflow.
What does a discovery to production timeline look like?
Discovery runs one to two weeks to align on document types, accuracy goals, reviewer flow, and audit needs. An end-to-end working version for a single document type lands in four to six weeks with confidence scoring and audit trail in place. Scaling out to more types, reviewer dashboards, and downstream integrations typically takes another four to eight weeks.

Start the conversation

Bring your structured documents into one calm pipeline.

Start with a 30-minute working session. We will walk the documents your team handles today, the moments that cost time, and what a focused engagement could look like.

A senior engineer reviews your submission — not a sales rep
Response within one business day
NDA available before scoping if needed