Scenario / 01
Request recognition
The file is classified, needed fields extracted, and format plus business rules applied.
1File2OCR3Fields4Check
Documents Finance, ops, and legal teams with streams of repeat PDFs, scans, and forms
Documents become validated data without manual typing
We turn a document from an attachment into a managed dataset: classify, extract fields, apply rules, and route exceptions to people.
01PDF / scan
02Fields
03Check
04Ops
OutputValidated record
Confidence below threshold creates a manual review task.
Practical outcome
A document is classified, fields are extracted and checked; low confidence goes to a human
Problem
Fields are typed by hand, errors appear after entry, and exceptions mix with standard files
Primary route
document → field extraction → validation → ops system
Working scenarios
From attachment to validated record
Demonstration routes, not client results. Exact logic depends on your rules, data, and systems.
Scenario / 02
Approval route
The document reaches owners by rule; decisions and comments are stored.
1Document2Rule3Decision4History
Scenario / 03
Transfer to ops
Confirmed data maps to the target contract; failures stay in a queue.
1Approve2Map3API4Status
Solution scope
Extraction, rules, and control
- Document classifier
- Field extraction and validation
- Approval route
- Working-system integration
- Decision and error log
Data and systems in the loopPDF and imagesOCR / LLMEmailCloud storageREST APIDatabases
A good fit for
Which documents follow a standard route
Finance, ops, and legal teams with streams of repeat PDFs, scans, and forms
Inbound documents
File classification, field extraction, and structured record prep.
Completeness checks
Required fields, formats, links, and mismatches against known data.
Approvals
Routing by amount, type, department, or another formal rule.
Engagement trigger
When manual entry became a risk
Document volume grew and review plus registration became the bottleneck
- 01Details are typed from PDFs and scans
- 02Errors appear after system entry
- 03Approvals live in email threads
- 04Decision history cannot be reconstructed
Fit / boundaries
What must not go unchecked
Quality depends on documents; doubtful values are never silently guessed
01Quality depends on readability, structure, and document diversity
02Low confidence must trigger manual review, not a hidden guess
03Legally significant decisions need agreed human control
How we launch
From a real process to a working loop
Tools are chosen after we verify inputs, exceptions, and the success criterion.
- 01
We collect samples
Document types, variance, file quality, and required fields.
- 02
We describe checks
Formats, directories, allowed values, and manual-review conditions.
- 03
We set extraction
OCR or AI approach matched to real document structure and quality.
- 04
We build the route
Roles, approvals, statuses, SLAs, and safe result delivery.
- 05
We measure quality
Field-level and type-level accuracy — not one overall number.
Which documents can you recognize?+
Invoices, requests, acts, forms, and other recurring layouts. Accuracy and approach follow real anonymized samples.
What happens with a bad scan?+
The system scores quality and field confidence. Unreliable results go to review, not auto-write.
Can values be checked against directories?+
Yes. Extracted values can match known counterparties, products, formats, and other sources.
Is multi-person approval supported?+
Yes. Roles, sequential or parallel stages, SLAs, comments, and escalation rules.
How do you estimate accuracy before build?+
Gather a representative set and test a prototype per significant field and file type.
First step
Assess document handling
We'll map inputs, exceptions, and constraints. You leave with a priority scenario and a next step — no obligation to start a project.
Assess document handlingService: Documents and OCR