Backend
Python
A programming language for AI features, data processing and automation: the engine behind document reading, reports and smart search.
Used for
- AI features
- Document reading
- Reports and analytics
- Task automation
AI document processing
AI document processing: from scanned paper to validated records
Data extraction from invoices, KYC documents, statements and forms into your systems, with validation rules and human review for uncertain fields.
At a glance
The short version
- What you get
- Capture
- Classify and extract
- Validate
- Review and export
- How it works
- Email, upload or phone camera
- Classifier
- OCR and AI models
- Rules engine
- 2 more
- Runs on
- Web app
- Admin
- Android
- Built with
- and 3 more tools
- Who it's for
- Cost depends on
- Document types and layouts
- Straight-through target
- Image quality
How to start
Tell us what you need in your own words. You talk to the developers who would build it and get a written, line-item estimate.
Get a project estimateExtraction is only half the job
Reading text off a page is largely solved. The hard part is being sure the numbers are right before they reach your ledger or loan system; a misread GSTIN digit costs more than the manual entry it replaced.
So every field gets checked. Totals must equal line items plus tax, GSTIN check digits and PAN formats must validate, and the vendor must exist in your master data. Fields that fail, or that the model is unsure about, appear beside the highlighted source for review, and each correction becomes test data.
Challenges
Why manual document handling hurts
Month-end data entry backlogs
Supplier invoices keyed into Tally or an ERP against GST filing deadlines.
Every sender uses a different layout
Suppliers, banks and labs each differ, often arriving as skewed phone photos.
Onboarding waits on document checks
Loan and vendor onboarding stall while staff verify PAN cards and statements.
Errors surface late
Tax mismatches and duplicate invoices appear weeks later at reconciliation.
What is included
Pipeline stages
Platform: Web, mobile, email
Capture
- Upload portal, email-in and camera capture with edge detection
- Password-protected bank statement PDFs
Platform: Processing service
Classify and extract
- Invoice, PO, statement, ID or report detection
- OCR plus layout-aware or vision-language models
- Line items with HSN codes, rates and tax splits
Platform: Rules engine
Validate
- GSTIN and PAN checks, tax arithmetic, duplicate detection
- E-invoice IRN and signed QR data compared with printed values
Platform: Web app
Review and export
- Reviewer queue with field confidence and source highlights
- Export to Tally, Zoho Books, SAP or your API with an audit trail
How it works
How a supplier invoice becomes a Tally voucher
KYC documents, bank statements and lab reports go through the same capture, extract, check and review stages.
- Email, upload or phone camera
Step 1: The invoice is captured
Suppliers email PDFs, staff upload scans or a field team photographs a challan; the app straightens and sharpens the image.
- Classifier
Step 2: The document type is identified
The system tells a GST invoice from a purchase order, bank statement or ID card, and sends it to the right extractor.
- OCR and AI models
Step 3: Fields and line items are read
Supplier, GSTIN, dates, HSN codes, rates and tax splits are read into a fixed structure, each with a confidence score.
- Rules engine
Step 4: Every value is checked
Line items must add up to the total, GSTIN and PAN formats must validate, and duplicate invoices are caught before posting.
- Accounts team
Step 5: A reviewer confirms doubtful fields
Only uncertain or failed fields are shown, highlighted on the scan. Each correction is saved as test data for the next release.
- Tally, ERP or your API
Step 6: The voucher is posted
Approved invoices become purchase vouchers with ledgers mapped, each linked back to its source document for audit.
Integrations
Engines and destinations
Open-source OCR
Tesseract and PaddleOCR
Self-hosted inside your infrastructure.
Cloud OCR
Google Document AI, AWS Textract, Azure AI Document Intelligence
Compared on your samples.
Extraction
Vision-language models
Hosted or open models, outputs forced into a schema.
Accounting
Tally and Zoho Books
Purchase vouchers from approved invoices.
Financial data
Account Aggregator framework
Consented bank data instead of parsed PDFs.
Issued documents
DigiLocker
Fetched with consent instead of uploaded scans.
Verification
GSTIN verification
Taxpayer status via a GST Suvidha Provider.
Privacy & security
Handling identity and financial documents
Aadhaar masked, not stored in full
Only the last four digits are kept, per UIDAI requirements; eKYC runs through licensed providers.
Encryption and short retention
Encrypted in transit and at rest; originals purged after extraction on your schedule.
Self-hosted option
OCR and open-source models can run entirely in your cloud or on-premises.
Reviewer access controls
Role-restricted queues with logs of who viewed and corrected each field.
Platforms
Where documents are captured and checked
A field capture app for Android, a web app for reviewers, and an admin dashboard for accuracy, volumes and exports.
- Web
Web application
Browser-based applications with logins, roles and workflows, such as customer portals, SaaS products and internal tools.
- Back office
Admin dashboard
Back-office panels for operations, support and finance teams: orders, users, content, reports and permissions.
- Mobile
Android app
Apps for Android phones and tablets, tested on budget and mid-range devices and published on Google Play or privately.
Technology
Technology behind document processing, and why it matters
Python runs OCR and extraction models, NestJS and Node.js services manage queues and posting, Redis spreads heavy batches across workers, and PostgreSQL keeps every value with its audit trail.
Adds AI features such as chat assistants, document summaries and smart search to your product, built on OpenAI models with human review.
Used for
- AI chatbots
- Document summaries
- Smart search
- Drafting replies
Connects your product to AI language models from several providers, so each task uses a suitable model and you can switch later.
Used for
- AI assistants
- Answers from documents
- Workflow automation
- Ticket triage
Backend
Node.js
Runs the server side of apps: fast, scalable back ends that power your app, website and integrations.
Used for
- App back ends
- Live order tracking
- Chat and notifications
- Payment processing
Backend
NestJS
A structured way to build back ends on Node.js, so large business systems stay organised, testable and easy to hand over.
Used for
- Business app back ends
- SaaS platforms
- Marketplaces
- Admin panel back ends
Data
PostgreSQL
A reliable database for the records your business runs on: orders, payments, bookings and stock, kept accurate and easy to report on.
Used for
- Orders and customers
- Payments and ledgers
- Stock and inventory
- Bookings
Data
Redis
Keeps frequently used data in fast memory, so apps stay quick on busy days and live features like order tracking feel instant.
Used for
- Faster apps
- Live order status
- Shopping carts
- Job queues and alerts
Cloud & DevOps
AWS
Cloud hosting for your app, website and data, with data centres in India and room to grow when traffic rises.
Used for
- App hosting
- File and photo storage
- Backups
- Busy sale days
Cloud & DevOps
Docker
Packages your software so it runs the same way on every laptop and server, which makes releases predictable and moving hosts easier.
Used for
- Reliable releases
- Same setup everywhere
- Faster onboarding
- Easy scaling
Plain-English glossary
Document processing jargon, explained
- OCR
- Optical character recognition, which converts the text in a scan or photo into characters a computer can process. It reads the words; deciding which number is the invoice total is the next step’s job.
- Confidence score
- A number the extraction model attaches to each field to show how sure it is. Fields below the score you choose go to a reviewer, so people check the doubtful few rather than every document.
- Straight-through processing
- Documents that pass every check and post to your books with nobody touching them. The share that can go straight through depends on document quality and how strict you want the rules to be.
- E-invoice IRN
- The Invoice Reference Number assigned when an e-invoice is registered on the government’s Invoice Registration Portal, returned with a signed QR code. Comparing the QR data with printed values catches altered or mistyped invoices.
- Account Aggregator
- A consent-based system, regulated by RBI, that lets a customer send their bank statements to a lender digitally. Lenders get clean data straight from the bank instead of PDFs that need parsing.
- Vision-language model
- An AI model that looks at the page image and its text together, so it can follow layouts such as tables and stamps. We force its output into a fixed format and still validate every value.
Product preview
What reviewers and field staff use
Illustrative screens, using an invoice and KYC processing system for a distributor with a lending arm as the example.
Field capture app. Field staff photograph a challan or invoice; the app finds the page edges, checks the image is sharp enough and asks for a retake if it is not. 
Review queue. Reviewers see only the fields that failed a check or scored low, highlighted on the original scan, and confirm or correct them in seconds. 
Operations dashboard. Operations staff follow every document from capture to posting, with failed checks and duplicates caught before they reach the books. 
Admin dashboard. Accuracy is measured per field on your own documents, so you can see which fields are reliable and which still need a person.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Industries
Paper-heavy industries
Services
Engineering behind the pipeline
AI app development
AI features and AI-first apps built on model APIs, retrieval over your own data, workflow automation or custom ML, with evaluation and human review.
Custom software development
Software shaped around how your business runs, from approvals and inventory to billing and reports, replacing spreadsheets and disconnected tools.
API development
Secure, documented REST and GraphQL APIs, plus integrations that connect your apps to payment, logistics, GST, messaging and business systems.
Web application development
Browser-based products, customer portals, dashboards and internal tools, built on clean data models with secure roles and integrations.
Admin panel development
Back-office dashboards built around your team's daily tasks: order queues, approvals, catalogue management, payouts, reports and audit logs.
Cost drivers
What changes a document-processing estimate
Document types and layouts
Many formats and handwriting need more samples and testing.
Straight-through target
Posting most documents unreviewed needs stronger validation.
Image quality
Poorly lit phone photos need pre-processing and retake prompts.
Destination systems
Tally, ERP or loan-system posting adds integration and reconciliation logic.
Hosting model
Self-hosted models need capacity planning; cloud OCR is billed per page.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksFAQ
Frequently asked questions
How accurate is AI document extraction?
It depends on document type, layout variety and image quality, so we measure it on your documents rather than quote a figure. The pilot reports accuracy per field, such as GSTIN, invoice date or line total, because averages hide the fields that matter. Clean digital PDFs extract far more reliably than crumpled phone photos. Validation rules and the review queue exist to catch what remains.
Can it read handwriting and Indian-language documents?
Partly. Printed Hindi and several other Indian scripts can be read by current OCR and vision models, with quality varying by script and font. Neat block capitals in form boxes often work; cursive notes and mixed-language scribbles on delivery challans usually need human review. We test your samples per language and design the review workflow around the results.
Should we parse bank statements or use the Account Aggregator framework?
If you are a regulated lender or partner with one, the Account Aggregator framework gives consented, structured data directly from the bank, avoiding parsing errors and tampered PDFs. Statement parsing is still needed for banks or customers outside the AA network, older periods, or where consent flows are impractical. Many lending products use both, with AA first and upload as the fallback.
Where are documents processed and stored?
Where you decide. Usually processing runs in your own cloud account in an Indian region, with files encrypted and purged after a retention period. If a hosted OCR or model API is used, only the necessary pages are sent, under terms that exclude training. For stricter needs, open-source OCR and models run entirely within your infrastructure, trading some accuracy for full control.
Can extracted invoice data go straight into Tally?
Yes. Approved invoices can create purchase vouchers with ledgers, GST rates and HSN codes mapped, through Tally’s XML integration or a connector you already use. A mapping screen lets the accounts team control vendor ledgers and cost centres. Duplicate checks run before posting, and every voucher links back to its source document for audit.
Next step
Send us a batch of real documents
Include the difficult ones. We will report field-level accuracy from a prototype before quoting.