Sense AI · Intelligent document processing

Convert documents into accepted records with field-level proof and exception control.

We engineer classification, extraction, validation, human review, and downstream acceptance as separate states. Quality is measured by field and consequence, not hidden inside one aggregate accuracy number.

Assess a document workflow See the processing path

Inputs: Document classes, layouts, quality conditions, channels, retention, and sensitive fields
Processing: File checks, classification, extraction, validation, review, and acceptance
Quality: Field-level precision and recall, exception routing, correction, and downstream compatibility

01 · Field contract

Extraction begins with a versioned record contract and acceptance policy.

We define classes, fields, evidence locations, normalisation, validation, confidence use, review triggers, retention, and downstream consumers.

Population

Document classes, versions, layouts, languages, scan conditions, channels, and representative frequency.

Field semantics

Meaning, type, cardinality, source region, allowed values, dependencies, and sensitive-data handling.

Acceptance

Deterministic rules, confidence use, cross-source checks, human review, and downstream rejection behavior.

02 · Processing path

A file is not processed until its accepted record reaches the intended consumer.

Azure AI Document Intelligence, Content Understanding, or another extractor may support the task; workflow state and acceptance remain application concerns.

Receive and classify

Malware and file checks, identity and channel metadata, duplicate detection, class, version, and quarantine.

Extract and normalise

Field value, source region, confidence signal, normalised type, and extraction trace.

Validate and review

Schema, business, cross-field, and external checks followed by focused exception review.

Accept and reconcile

Versioned record delivery, consumer receipt, correction propagation, and terminal batch state.

03 · Evaluation

Aggregate accuracy can hide the one field that creates material operational risk.

Evaluation includes extraction and the entire acceptance path, using representative ordinary and adverse conditions.

Detection and class

Missing file, unsupported class, wrong version, duplicate, corrupt page, and mixed-document package.

Routing performance by class and condition

Field and validation

Precision, recall, normalisation, business-rule result, and false acceptance by field consequence.

Threshold and review policy per field

Workflow outcome

Review effort, exception aging, correction rate, downstream rejection, and reconciliation completeness.

Service measure and operating threshold

04 · Technical view

Reviewers should spend attention on the uncertain or consequential part of the record.

The review experience prioritises exceptions, preserves source context, explains failed rules, records correction, and keeps batch and downstream state visible.

Illustrative reference patternDocument-to-record validation

Service request

synthetic example

FieldEvidenceRule

RequesterLine 02Accepted

Cost centreLine 05Accepted

ApproverMissingReview

Reviewer acceptsCorrection retained with source

Illustrative pattern. Each accepted field remains connected to source evidence, deterministic validation, and any reviewer correction.

Evidence beside value

The source region and document version remain available for every reviewed field.

Rule beside exception

The reviewer sees why a value failed and which authority can accept or override it.

Correction becomes data

Accepted corrections feed quality review without silently changing historical records.

05 · Engagement contract

The document population and downstream record contract will both change.

Handover establishes corpus ownership, exception operations, schema versioning, compatibility tests, quality cadence, and controlled support for new classes.

Document and field contract

Classes, versions, fields, evidence, validation, acceptance, retention, and consumers.

Processing service

Observable stages, quarantine, reviewer experience, idempotent delivery, and reconciliation.

Quality and change pack

Representative corpus, thresholds, drift review, schema compatibility, release tests, and runbooks.

Engagement contract

What must be true, who owns what, and what leaves the engagement.

A good fit when

High-volume forms, invoices, claims, service records, contracts, or correspondence
Processes where manual re-keying causes delay or error
Existing extraction pilots with high exception or downstream rejection rates

Required before delivery

A representative, lawfully usable document corpus
Named field and downstream record owners
Reviewers who can author acceptance and correction rules

Customer owns

Approve retention and sensitive-data handling
Define field meaning and downstream acceptance
Staff exception review and quality ownership

Delivery outputs

Versioned document and field contract
Document-to-record processing and review service
Evaluation corpus, quality thresholds, compatibility tests, and runbooks

Not included by default

Azure tenant, network, identity, Foundry, Search, monitoring, or platform foundation unless Sense Cloud is included in scope
A production guarantee based on a prototype or vendor benchmark
Unrestricted autonomous action or implicit approval authority
Customer policy, source ownership, or risk acceptance decisions
Acceptance of a record solely from a model confidence score

Questions to answer first

Which field error carries the greatest consequence?
What evidence must a reviewer see before accepting it?
How will a corrected record reach every downstream consumer?

The next step is a bounded working session: one workflow, its evidence sources, the decision owner, and the conditions under which the system must stop or hand over.

Assess a document workflow