We build document AI that reads invoices, bank statements, contracts and forms, extracts the fields you need, checks them against your rules and sends clean data to your ERP or database. It comes from the team that built DataSwitch, an AI document extraction platform, for a client.
Engineering team in Jaipur, India. Working with teams in the US, UK, Europe, UAE, Australia and India.
Digital PDFs, scans, phone photos and email attachments, in any layout. No template per supplier or form.
Every document goes through the same six steps, and every step is logged.
Documents arrive from an email inbox, an upload screen, a shared folder, a scanner or an API.
The model works out what each document is, splits multi-document PDFs and routes each one to the right extractor.
OCR and layout models read the page, and a language model pulls out the fields you need, including tables and line items, with a confidence score for each field.
Values are checked against your rules and records: totals add up, tax IDs are valid, the invoice matches its purchase order, the supplier exists in your system.
Fields that fail a check or fall below the confidence threshold go to a review screen, where a person confirms or corrects them. Corrections are logged and used to improve extraction.
Clean data goes to your ERP, accounting system, CRM or database, with a link back to the source document for audit.
Extracted data can also feed AI analytics, such as cash-flow forecasts.
Read supplier invoices from your AP inbox, match them to purchase orders and goods receipts, and post approved bills to your accounting system.
Typical outcome: your AP team reviews exceptions instead of typing invoices.
Turn statements from any bank into clean transaction data, then match transactions to invoices and payments.
Typical outcome: faster month-end close and credit checks.
Read ID, address and company documents, check them against the application and flag mismatches or expired documents.
Typical outcome: onboarding teams handle only the cases that need judgement.
Pull parties, dates, renewal and notice terms, payment terms and risky clauses into a searchable register.
Typical outcome: no renewal or notice date missed.
Extract data from insurance claims, medical forms and applications, and route each case by its content.
Typical outcome: shorter queues and fewer re-keying errors.
Let staff ask questions across contracts, policies and manuals and get answers with the source page cited.
Document Q&A| Template-based OCR | Intelligent document processing Our approach | |
|---|---|---|
| New supplier or form layout | Someone builds a new template | Handled without a new template |
| Tables and line items | Often lost or misaligned | Extracted row by row |
| Checking the data | Manual, after the fact | Rules and record matching built in |
| When it is unsure | Wrong value goes through | Field goes to a person for review |
| Scans and phone photos | Accuracy drops sharply | Cleaned up before reading |
| Over time | Templates pile up | Reviewed corrections improve extraction |
We don't only build document AI for clients. We run it in our own products, so we know what goes wrong with real scans, odd layouts and messy tables.
An AI document automation platform we built for a client. Teams upload PDFs and images one at a time or in bulk; DataSwitch classifies each document, extracts the data and returns structured output that is easy to check.
AI business management software for SMBs. Its document AI scans invoices, receipts and vouchers, extracts, validates and categorises them, and syncs them to the ledger.
See VyapaarSenseGeneric accuracy claims tell you little. We measure accuracy on your documents and show you the numbers field by field.
You share a sample of your documents and the fields you need. We return extracted data and a field-level accuracy report, and tell you honestly whether a custom build or an off-the-shelf tool fits.
We build the pipeline for one document type, connect it to one system and run it alongside your team, with every field reviewed.
We switch on automatic posting above your confidence threshold, add more document types and hand over monitoring, or run it for you.
Need AI engineers inside your own team instead? AI engineers inside your own team
Intelligent document processing (IDP) uses OCR, layout models and large language models to read a document, work out what type it is, pull out the fields you need and check them against your rules. The result is clean, structured data in your systems. Unlike template-based OCR, it handles new layouts without a new template for every supplier or form.
OCR only turns an image into text. Intelligent document processing also understands which text is the invoice number, the total or the due date, checks those values against your rules and records, flags anything it is unsure of for a person to review, and sends structured data to your ERP or database.
Accuracy depends on the documents, their scan quality and the fields you need, so we measure it on your own documents. Before you commit to a build, we run a sample set through our pipeline and give you a field-by-field accuracy report. In production, any field below the confidence threshold goes to a person for review instead of straight into your systems.
We process invoices, purchase orders, receipts, bank statements, contracts, onboarding and KYC documents, insurance and claims forms, shipping documents and HR forms. Inputs can be digital PDFs, scanned PDFs, photos from a phone or email attachments, and outputs are JSON, CSV or direct writes to your systems.
Yes. Extracted data is exported through your ERP, accounting or CRM system's API, or through webhooks, CSV files or a database you choose. Validation can match documents against your own records, such as checking an invoice against its purchase order before it is posted.
You choose where processing runs: in a cloud region you pick, inside your own cloud account, or on your own servers using open-weight models. Your documents are never used to train public AI models.
Projects run in three stages: a test on a sample of your documents, a pilot on one document type with your team reviewing the output, then production with monitoring and a review queue. Timelines depend on the number of document types, the fields you need and the systems we connect to, and we give you a fixed plan after the sample test.
An off-the-shelf platform is often enough for standard documents at moderate volume. A custom build pays off when your documents vary widely, you need your own validation rules and approval steps, or your data has to stay in your own cloud or on your own servers. After the sample test we tell you honestly which option fits.
Send us a sample of the documents your team types up today. We will show you the extracted data, the accuracy field by field, and what it would take to run it in production.