ATPL

AI · See and predict

Invoices, forms and contracts, read automatically.

For accounts, purchase, logistics and legal teams who re-type what is already printed: documents arrive as PDFs, scans and photos and leave as checked fields in your system.

Describe the problemAll AI capabilities

Paper in the loop

Still retyping PDFs by hand

Invoices, forms and contracts read into fields, checked, and filed in your system.

Describe the problem

  1. PDF

    Supplier invoices come as PDFs, scans and WhatsApp photos, in six layouts, and someone types each one into Tally.

  2. Retype

    A contract renewal was missed because the date was on page nine of a scan.

  3. Miss

    KYC forms, delivery challans and e-way bills are checked by eye and errors surface at audit.

Your data

Documents are processed in your cloud account or on your server; the files and extracted fields stay in your storage. Where a language model is used it receives one document at a time under terms that exclude training. Review decisions are stored so accuracy can be audited by document and by supplier.

Extract or OCR

Where it fits, where it doesn’t

  • Fits when
  • Documents that arrive in volume with the same fields each time, however varied the layout.
  • Teams with a destination system that can import records: Tally, an ERP, a sheet.
  • Work where a review queue for the uncertain cases is acceptable.
  • Doesn’t fit when
  • A handful of documents a week; a person is faster than a pipeline.
  • Handwritten forms in poor condition; extraction accuracy drops and review becomes the whole job.
  • Documents where a single misread digit is unacceptable and no one will check: payment instructions, for example.

Page to fields

A page becomes fields

The extract, the confidence and the queue for a person to confirm — not OCR dumped into a spreadsheet.

What a client typed

Our accounts team gets about eight hundred supplier invoices a month as PDFs and photos on WhatsApp and types each one into Tally with the GST details, and the auditor keeps finding mismatches between the invoice and the purchase order.

Brief ready

What it becomes

Problem
Manual entry of supplier invoices into Tally, with PO mismatches found only at audit.
Scale
About eight hundred invoices a month, from many suppliers in many layouts.
Systems
WhatsApp and email intake, Tally, the purchase order register.
First step
Extraction and PO matching for the top twenty suppliers, with every invoice reviewed on screen for the first month.

Fields you can trust

Extraction with a review queue

  • Validation against your records

    GSTIN format, PO number exists, totals add up, duplicate invoice numbers: checks run before anything is written to your system.

  • Confidence and review queue

    Each field carries a confidence score; low-confidence documents go to a person with the field highlighted on the image.

  • Posting into the destination

    Clean records land in Tally, the ERP or a sheet through its import or API, with the source file attached.

  • Layout drift handling

    When a supplier changes their format, the review queue catches it and the extraction is corrected without a rebuild.

Primary deliverable

Ships with the engagement

Live brief

01 · Lead outcome

Extraction per document type

Invoice, purchase order, challan, bank statement, KYC form, contract: each with its own list of fields, formats and validation rules.

  • 01Validation against your records
  • 02Confidence and review queue
  • 03Posting into the destination
  • 04Layout drift handling

How it ships

Sample, extract, check

  1. doc/01

    Understand

    We collect two hundred real documents of each type, including the bad scans and the photographed ones, because those decide the design.

  2. doc/02

    Design

    Fields, validation rules and the review threshold are agreed on paper; whether to use OCR, a layout model or a language model is chosen last, per document type.

  3. doc/03

    Build

    Extraction runs on the backlog with a person checking every document until confidence scores prove reliable, then only the flagged ones.

  4. doc/04

    Stay

    Review-queue rates are watched monthly, new suppliers and formats are added as they appear, and the evaluation set grows with corrected cases.

How we work

ATPL builds document pipelines around the GST invoice, the purchase order and the Tally import that Indian accounts teams already use; ATPL will confirm which document AI deployments may be named.

    Chosen for the job

    Python · Node.js · Laravel

    Decide the shape

    Before you start

    Plain answers on scope, data and what a first proof usually takes.

    Yes. ATPL builds document intelligence pipelines that read supplier invoices from PDFs, scans and photos, extract the GSTIN, line items, taxes and totals, check them against the purchase order, and post clean records into Tally through its import, with uncertain documents sent to a person first.

    Printed invoices and clean PDFs extract reliably; photographed and faded scans less so, which is why ATPL attaches a confidence score to every field and routes anything below the agreed threshold to a review queue. Accuracy is measured on your own documents in the first month, not quoted from a brochure.

    Document intelligence is software that reads business documents such as invoices, forms, contracts and bank statements, turns them into structured fields, checks the fields against your records, and files the result in your system. ATPL combines OCR, layout models and language models, chosen per document type.

    Yes. A document intelligence pipeline can find renewal dates, notice periods, payment terms and parties in contracts and put them in a register with a reminder, with the clause shown beside the extracted value so a person can confirm it. ATPL treats contract extraction as review-assisted, never fully automatic.

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