Documents in.
Structured records out.
Invoices, claims, KYC files, purchase orders, delivery notes and forms — extracted, classified, validated and written into your system of record, with a confidence score on every field and a review queue for anything doubtful.
Six stages, and a
human at the uncertain ones.
The value in document AI is not extraction — that part is close to solved. It is knowing which extractions to trust, and routing the rest to a person before they reach your ledger.
Ingest
From email attachments, a scanner drop, an SFTP folder, a portal upload or an API. PDFs, images, scans and photographs of paper.
Classify
What is this — invoice, credit note, delivery challan, claim form, ID document? Misclassification is caught here, not three steps later.
Extract
Fields pulled with a confidence score each. Tables, line items and handwriting are handled, with handwriting flagged as lower confidence by default.
Validate
Cross-checked against your own data — does this PO exist, does the total add up, is this vendor on file, is this a duplicate?
Review queue
Anything below the confidence threshold, or failing validation, goes to a person with the document and the extraction side by side.
Post
Written into Tally, Zoho, SAP, your ERP or a database, idempotently, with the source document linked to the record.
What we already
have models for.
Anything not listed is handled as a custom document type, which needs a sample set of around a hundred real examples to build and test against.
Send us a sample set →- Supplier and purchase invoices
- Credit and debit notes
- Bank statements
- Expense receipts
- Purchase orders
- Delivery challans and e-way bills
- Packing lists
- Proof of delivery
- Shipping and customs documents
- Inspection reports
- KYC document sets
- Address proofs
- Business registration papers
- Signed agreements and forms
Identity documents carry additional handling rules. Nothing is retained beyond the agreed window.
- Insurance and warranty claims
- Application and enrolment forms
- Survey and inspection sheets
- Handwritten job cards
Where document AI struggles
- Poor-quality phone photographs of creased paper — accuracy drops sharply
- Dense handwriting, especially in mixed scripts
- Documents with no consistent layout at all across a sample
- Fields that require a judgement call rather than a reading
- Any document type where you cannot supply around a hundred real examples
We test on your real documents during the pilot and report the measured accuracy per field before you commit to a volume. A number quoted before that test would be a guess.
Send us one process. We’ll tell you what it costs to automate it.
The discovery call is free and carries no obligation. If AI is the wrong answer for your process, we would rather say so in the first hour than in month three.