Transform unstructured documents into structured, actionable data. OCR extraction, LLM-based parsing, intelligent validation, and ERP-ready outputs — all automated.
From invoices and bank statements to agreements and reports — Cresolv DocAI handles every document type.
Advanced OCR combined with AI/LLM models to extract structured data from PDFs, scanned documents, images, and digital files.
Parse bank statements from all major Indian and international banks — extract transactions, balances, and structured financial data.
Intelligent invoice parsing with vendor recognition, line-item extraction, GST validation, and ERP-ready data output.
Extract key clauses, dates, parties, obligations, and terms from legal agreements, contracts, and vendor documents.
Configurable validation rules to verify extracted data against master records, thresholds, formats, and business logic.
Push validated, structured data directly into SAP, Oracle, Tally, Dynamics, or any custom ERP via API connectors.
Cresolv DocAI reads unstructured documents such as invoices, bank statements, agreements and financial statements, and turns them into structured, validated data that an ERP can take in. It combines OCR, which reads the text off a PDF, scan or image, with LLM-based extraction, which understands what the text means and which field it belongs to.
It is one engine used in two ways. Finance teams use it to key invoices into AP, to bring bank statements into reconciliation and to track contract obligations. Teams that want extraction as a service use the same engine through an API. Both get the same output: fields with confidence scores, checked against your rules.
| Document | What DocAI extracts | Where it usually goes |
|---|---|---|
| Invoices | Vendor, invoice number, date, line items, GST fields, totals, PO references | AP posting and matching |
| Bank statements | Transactions, dates, opening and closing balances, categorised output, from major Indian and international banks | Reconciliation |
| Agreements and contracts | Parties, signatories, dates, key clauses, obligations, renewal and expiry terms | Obligation and renewal tracking |
| Financial statements | P&L, balance sheet and MIS report line items and figures | Analysis and reporting |
PDFs, scanned documents, images and digital files are supported. Digital files parse more reliably than poor scans, and handwritten annotations vary more than typed text.
An accuracy audit trail is kept, so you can see what was extracted, what was checked and who corrected what.
A standalone OCR tool gives you raw text. Someone still has to work out which number is the invoice total, which is the tax, and which belongs to a line item. Template-based tools solve this for known layouts, and fail quietly when a vendor changes theirs.
LLM-based extraction reads the document for meaning, so a layout it has not seen before is still readable. That is why DocAI pairs it with confidence scoring and validation: understanding a new layout is useful only if the system also tells you when it is unsure. We go through the trade-offs in OCR vs LLM document parsing.
Invoice parsing extracts GSTIN, tax breakup and totals along with vendor, invoice number, date and PO reference, so the output is ready for GST-aware matching and posting. For matching beyond three-way, including GSTIN checks and tax verification, DocAI feeds the AP side: see AP Automation (APIQ) and the guide to multi-GSTIN 7-way matching.
Bank statement parsing covers formats from all major Indian banks alongside international ones. It is not a bolt-on: statement parsing and invoice parsing run through the same extraction and validation pipeline.
Extraction accuracy runs up to 95%, depending on document quality and type. Clean digital invoices and statements land at the high end. Skewed or low-resolution scans and handwritten annotations do not, and need more review.
The goal is to remove routine keying, not the review step. Low-confidence fields go to a person, so errors are caught before they reach your ERP and not after.
Connector scope depends on your ERP version and setup, so we confirm integration details during scoping and do not promise a plug-in for every environment.
A representative sample tells you more than a generic demo. Here is how a first test works.
Invoices from your five or six highest-volume vendors and a couple of bank statements.
We show extracted fields and confidence scores, and where exceptions would be routed.
We confirm your ERP version, document volumes and output format before anything is committed.
What finance and ops teams ask before automating document extraction.
DocAI combines OCR with LLM-based field extraction, so it doesn't just read text off a page — it understands document structure, extracts specific fields like vendor, line items, and GST numbers, and validates them against your business rules before handing off clean, ERP-ready data. A standalone OCR tool gives you raw text; DocAI gives you structured, validated records.
Extraction accuracy runs up to 95%, depending on document quality and type — clean digital invoices and bank statements score highest, while low-resolution scans or unusual formats need more human review. Every extraction also passes through the validation engine, so errors get flagged before they reach your ERP rather than after.
Invoices, bank statements (Indian and international formats), agreements and contracts, financial statements such as P&L, balance sheets and MIS reports, and other unstructured PDFs, scans, and images. Each document type runs through its own extraction model — invoice parsing focuses on vendor and line-item data, bank statement parsing on transactions and balances, contract parsing on clauses, dates, and obligations.
Fields below your confidence threshold go to an exception queue, where a reviewer confirms or corrects the value. Extracted data is also cross-checked against master data, such as vendor and PO records, and an accuracy audit trail is kept. The aim is to remove routine keying, not the review step.
SAP, Oracle, Tally, Dynamics, or any custom ERP through API connectors — validated data posts directly once it clears the validation engine, so your team isn't re-keying extracted fields into the ERP.
Yes. Invoice parsing includes GST and tax field extraction as a standard part of the pipeline, and bank statement parsing covers formats from all major Indian banks alongside international ones — this isn't a bolt-on for Indian compliance, it's built into the core extraction models.
Yes, DocAI includes handwritten text recognition alongside standard OCR for scans and images, though accuracy on handwriting varies more than on typed or digital-native documents — legibility and document quality still matter.
Yes. Share a representative sample, such as invoices from your five or six highest-volume vendors and a couple of bank statements, and we will show you the extracted fields and confidence scores on your own documents. Implementation timelines depend on your document volume, formats and the ERPs involved, so we scope those with you after the sample.
Related reading
Stop manual data entry. Let Cresolv DocAI extract, validate, and deliver structured data into your ERP automatically.