Computer vision · OCR & data extraction
OCR data extraction for invoices, forms, and scanned documents — without the manual keying queue
Banao builds OCR and data-extraction pipelines that read your invoices, handwritten forms, PDFs, and scanned documents — pulling the right fields, checking them against your business rules, and pushing clean records into your downstream system without a person manually keying every line.
We deliver the full pipeline: the model, the pre-processing, the field mapping, the exception routing, and the monitoring that catches when accuracy starts to drift. It is built to the production standard we hold the AI we run our own company to.
Banao— our own operational documents run through the same extract-validate-route pipeline we build for clients.
What we build into an OCR and data-extraction pipeline
A production OCR pipeline is not a single model. It is the pre-processing, the extraction model, the field mapping, the validation rules, the exception queue, and the drift monitoring — we own all of it.
Printed and handwritten text extraction
Models tuned for your document types — invoices, delivery notes, consent forms, application packs — that read printed and cursive text under the scan quality your equipment actually produces, not the clean samples a benchmark was built on.
Invoice and purchase-order parsing
Structured extraction of supplier name, line items, quantities, unit prices, totals, due dates, and GL codes from unstructured invoice layouts — including those from suppliers who change their template without telling anyone.
Identity and regulated-form reading
Field extraction from national ID cards, passports, medical intake forms, and regulatory filings, with field-level confidence scores and a flagging path when the document is too degraded to trust.
Table and structured data extraction
Row-by-row extraction from tables in PDFs and images — bank statements, lab reports, shipping manifests — with the relationships between headers and values preserved rather than flattened into a block of text.
Multi-language and multi-script support
Pipelines that handle Arabic, Hindi, and other non-Latin scripts alongside English, because documents that mix languages break most off-the-shelf extractors and the GCC market routinely produces them.
Image pre-processing and quality correction
Deskewing, denoising, contrast normalisation, and resolution correction before extraction runs, so the model receives a consistent input rather than guessing through a photo taken at an angle on a warehouse floor.
Validation and business-rule checking
Post-extraction checks — totals that must add up, dates that must follow a sequence, codes that must match a reference list — so bad data is caught at the pipeline boundary, not discovered in an accounting reconciliation.
Exception routing and human-review loop
Low-confidence fields and failed validations routed to a structured reviewer queue — showing the original image, the extracted value, and the reason for doubt side by side — so a human spends time only on what the model genuinely cannot resolve.
What makes OCR reliable in production and what makes it fall over in a pilot
Most OCR pilot results look credible because the demo runs on clean, freshly scanned, standard-layout documents. A production environment delivers scanned photocopies of faxes, three different invoice layouts from the same supplier, handwritten corrections over printed fields, and a portrait scan of a landscape form. The gap between a pilot and a pipeline is almost entirely in those edge cases, not in the model.
Pre-processing closes the image quality problem. Layout-aware extraction closes the template variety problem. Confidence thresholds and a reviewer queue close the model-uncertainty problem. A trained model without all three layers is a prototype that will be switched off within a month when the false-acceptance rate climbs into the ERP.
Tuned on your document population, not a benchmark
Accuracy on publicly available datasets does not predict accuracy on your scans. We fine-tune on examples from your actual suppliers and forms — your scan quality, your field layouts, your defect patterns — because that is the only benchmark that matters.
Confidence routing, not binary pass/fail
Every extracted field carries a confidence score. Fields above the threshold flow straight through; fields below it go to a reviewer with the image and the extracted value in parallel. That split is what stops an 80%-confident model from silently polluting your data lake.
Tested against your acceptance criteria before go-live
We write the acceptance test before we write the model: field-level extraction accuracy and exception rate scored against a held-out set of your documents, with a written pass threshold. We ship only when the pipeline clears it.
Drift detection built in from day one
A new supplier template, a scanner firmware update, or a regulation form change can shift accuracy without anyone noticing until the error rate in the downstream system is too large to ignore. We monitor field-level accuracy in production and alert before that happens.
How an OCR pipeline connects to the systems that act on the data
Extracted data is only worth something when it reaches the system that uses it — the ERP that needs the invoice, the CRM that needs the form, the data warehouse that needs the shipment record. An OCR pipeline that delivers a CSV to an inbox is a half-built tool. Wiring the validated extraction into the destination system, with the error handling and audit trail that system requires, is what turns document reading into genuine process automation.
We build the integration as part of the pipeline scope, not as a follow-on engagement. That means field mapping to your destination schema, posting to your AP or ERP system with duplicate detection, and handing your operations team a complete audit trail from raw image to posted record — so any dispute or audit request has an answer in seconds.
Direct ERP and AP-system posting
Extracted invoice and order data pushed to SAP, Oracle, Tally, or your AP system with the field mapping, validation, and duplicate detection your finance team relies on — the pipeline ends at the posted record, not a staging table someone has to review.
Webhook and REST API output
For systems without a direct connector, validated extractions are published via webhook or REST API so any downstream service can consume them in its native format, with retries and dead-letter handling for failed deliveries.
Reviewer corrections that feed retraining
Every correction a reviewer makes in the exception queue is logged and used in the next retraining cycle, so the model improves on your actual document edge cases rather than remaining frozen at the accuracy it had on launch day.
Image and decision audit trail
Every document image, every extracted field, and every validation decision retained with timestamps and field-level evidence — because a dispute, a customer audit, or a regulator three years from now will need exactly this.
We hold an OCR pipeline to the standard we run our own AI to
Banao runs a ~300-person engineering operation on its own AI in production, every week. InterviewGod processes and scores applications for our own hiring; Vikaas extracts and routes signals from our own pipeline. Neither is a document extraction system — but both are AI that has to be right on real inputs, monitored for drift, and trusted by our own team, or it gets replaced.
An OCR pipeline lives and dies on the same discipline: a model tuned on your specific document population, a confidence threshold your downstream system can act on, a reviewer queue for what the model can't resolve, and a retraining path for when the distribution shifts. We bring the standard we hold our own systems to — not a best-effort attempt billed to your backlog.
- InterviewGodReads and grades Banao's own job applications in production — monitored weekly against a measured accuracy bar.
- VikaasExtracts and routes demand signals from Banao's own pipeline — running in production, watched daily.
Where we build and run OCR data-extraction pipelines
UAE & GCC
Arabic-English bilingual documents are standard across the Gulf, and most off-the-shelf OCR tools handle them poorly. We build pipelines for both scripts within a single extraction pass, and keep document images inside UAE boundaries where the PDPL and client policy require it.
India
Our Bangalore and Chandigarh bench delivers OCR pipelines for India's financial services, healthcare, logistics, and government-forms markets — handling Hindi, Tamil, Kannada, and other regional scripts alongside English, under the DPDP Act.
United Kingdom
UK pharma, financial services, and professional-services firms carry strict document-retention and audit obligations. We build to UK GDPR, with a field-level audit trail for every extracted record and data residency inside the UK where required.
United States
AP automation, healthcare forms, and insurance document processing are the highest-volume OCR use cases in the US market. We build to SOC 2 controls and the audit logging US compliance and risk teams require for any pipeline that touches sensitive records.
When OCR and data extraction are not the right answer
Most vendors will apply an OCR model to any document problem. We would rather name the mismatches before you commit a budget to one:
- The document is already digital: if your source system produces structured XML, JSON, or an API response, extracting data from a rendered PDF of that same record adds processing steps and error surface for no gain — read the source directly.
- Volume too low to recover the build cost: a team processing fifty invoices a month may never recover the pipeline build, tuning, and maintenance cost over a competent person and a spreadsheet template.
- Too many uncontrolled layouts to train reliably: if your supplier set is constantly adding new invoice formats and nothing stabilises, a general-purpose LLM-based extractor may outperform a fine-tuned model trained on last quarter's templates — we will tell you which approach fits.
- The accuracy requirement exceeds what OCR can deliver: heavily degraded handwriting on low-quality paper, or free-text narrative fields with no structure, may need human transcription as the primary path, with OCR as an assist rather than the extractor.
- A deterministic parser already handles it: if a vendor portal offers a structured export and an existing integration already pulls it, adding OCR to the same documents does not help and adds a failure mode.
How we start — extract from your hardest document type first
We don't quote a pipeline build off a brief. We test feasibility on your actual documents before we propose a scope.
- AI Discovery Sprint2 weeks · fixed price
We take a sample of your real documents, run extraction, and hand back a field-level accuracy benchmark on your hardest layouts, a field-mapping plan, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
- Build and integrate
We tune the extraction model on your document population, build the validation rules, wire the exception queue, and post clean records into your downstream ERP, CRM, or data warehouse — integration is a deliverable in the build, not a hand-off.
- Production and drift monitoring
We deploy with monitoring on field-level accuracy and exception volume, a retraining cycle triggered by monitoring rather than by a support ticket, and a reviewer queue that keeps humans focused on what the model genuinely cannot resolve.
Frequently asked questions
What is OCR data extraction and how does it work?
OCR (optical character recognition) converts text in a scanned image or PDF into machine-readable characters. Data extraction then parses that text to pull specific fields — supplier name, invoice total, due date, line items — and routes them to a downstream system. A production pipeline adds pre-processing, field mapping, business-rule validation, and exception routing around the OCR model to turn raw recognition into clean, trusted data.
How accurate is automated OCR extraction on real business documents?
On clean, printed, consistent-layout documents a well-tuned model reaches high field accuracy. On handwritten, variable-quality, or mixed-layout documents accuracy is lower and depends heavily on pre-processing and fine-tuning to your specific document population. We benchmark on your actual documents before quoting, set the confidence threshold so what passes to your downstream system is trusted data, and design the reviewer queue so a human handles only what the model cannot resolve.
Can you handle handwritten forms and mixed handwritten/printed documents?
Yes, for legible handwriting on structured forms. We use handwriting-specific models fine-tuned on your form types and confidence routing so that a field the model is uncertain about goes to a reviewer rather than through unchecked. For free-text handwritten narrative fields with no structure, accuracy limits apply and we will tell you the realistic expectation before we build anything.
Which document types and languages do you support?
Invoices, purchase orders, delivery notes, consent forms, identity documents, shipping manifests, lab reports, bank statements, and regulatory filings — any document with repeating field structure is a candidate. Language support includes English, Arabic, Hindi, and other scripts; bilingual documents common in the GCC and India are handled within the same extraction pass.
How does the pipeline handle documents it can't read with high confidence?
Every extracted field carries a confidence score. Fields below the threshold go to a structured reviewer queue — not an inbox — where the reviewer sees the original image and the extracted value side by side with the reason for the hold. Every correction the reviewer makes is logged and fed back into the next retraining cycle, so the exception queue shrinks over time as the model improves on your real edge cases.
How does extracted data reach our ERP or downstream system?
We wire validated extractions directly to your destination — SAP, Oracle, Tally, a CRM, or a data warehouse — with the field mapping, duplicate detection, and validation your finance or operations team relies on. For systems without a direct connector we publish via webhook or REST API, with retries and dead-letter handling. Integration is a deliverable inside the build scope, not a follow-on project.
How do you prevent extraction accuracy from drifting after go-live?
We monitor field-level accuracy and exception volume in production and alert when either moves outside the baseline. A new supplier template, a scanner firmware update, or a regulatory form change can shift the document distribution without any visible signal until the downstream error rate climbs. The answer is a retraining path built into the pipeline from the start, triggered by monitoring data rather than discovered in a quarterly audit.
How long does it take to build and deploy an OCR data-extraction pipeline?
A common path is a 2-week Discovery Sprint to benchmark accuracy on your hardest documents and map the integration requirements, then a build and integration of roughly 6–10 weeks depending on the number of document types, the downstream connections, and the exception-routing complexity. Banao's engineering bench means work starts in weeks, not months.
Bring your hardest document type and we will benchmark it
Bring the invoice layout your current process struggles with, the handwritten form that fills a reviewer's inbox, or the document type your team keys in by hand. In 45 minutes we will tell you what an OCR pipeline can reliably extract from it — and what building one would take.
Book a 45-min scoping call