Workforce & HR · HR document automation
Your HR team is a document processing queue
Banao builds HR document automation that classifies incoming documents, extracts the data fields your HRMS needs, and routes each document to the right workflow — offer letters, ID proofs, tax forms, exit paperwork — without a human touching the inbox.
The same pipeline handles exceptions: documents that fail classification or extraction are flagged with a confidence score and reason, so your HR team spends ten minutes resolving edge cases rather than four hours processing everything.
The first call is free · 45 minutes · no obligation
What we build
What a Banao HR document automation build includes
A document pipeline is not just OCR. It is classification, extraction, validation, HRMS push, and an exception workflow — we own the full stack.
Intake classification across document types
The pipeline identifies each incoming document — offer letter, PAN card, degree certificate, Form 16, exit clearance — before any extraction runs, so the right schema and routing rules apply to each type.
Structured field extraction from unstructured inputs
Handwritten forms, scanned PDFs, and photo uploads all produce the same structured output: employee ID, joining date, tax fields, bank details. The extraction model is trained on your specific document templates, not a generic baseline.
Validation against your HRMS and policy rules
Extracted data is validated before it enters your system — PAN format checks, date logic, mandatory-field completion, and cross-document consistency. A joining date that precedes the offer date is caught here, not discovered in an audit six months later.
HRMS and payroll system integration
Validated records push directly to your HRMS, ATS, or payroll provider via API. No manual data re-entry, no cut-paste errors. We integrate with SAP SuccessFactors, Darwinbox, Keka, Zoho People, and custom HR databases.
Confidence-scored exception workflow
Documents that fall below the extraction confidence threshold are queued for human review with the reason and the specific field highlighted. Your HR team resolves only the hard cases — the pipeline processes everything else automatically.
Audit trail and compliance archive
Every document is stored with a timestamped processing log: when it arrived, what was extracted, who reviewed an exception, and what entered the HRMS. The archive satisfies labour-law and data-protection audit requirements without manual re-assembly.
Receipts
Where this is running
Metrics shown dotted (··) are being confirmed in our case-study metrics pack — published only once verified.
Document intake automated across a multi-site healthcare workforce
Hummcare's HR team processed joining kits, credential verifications, and compliance forms manually across sites. Banao built a classification and extraction pipeline with direct push to their HR system, replacing the manual inbox with a flagged-exception queue.
Dogfooding
We run this on our own hiring operation first
Banao processes every engineer hire through its own AI stack. InterviewGod handles candidate screening; the document pipeline handles the onboarding paperwork that follows — offer letters, ID verification, bank details, compliance acknowledgments.
By the time a document automation system reaches your HR team, it has already processed Banao's own 300-person workforce. That is the bar we build to, not a demo environment.
Screens every Banao engineering hire from shortlist to offer.
Runs Banao's demand-gen and pipeline reporting end to end.
The honest version
When HR document automation is the wrong investment
Document automation is not always the highest-value move for an HR team. We will tell you upfront:
- Low hiring volume: below a few hundred documents a month, a single trained coordinator outperforms any pipeline on total cost. We'll say so.
- Highly inconsistent formats: if your document types change every quarter or arrive from dozens of unstructured sources, classification accuracy degrades before it pays back. A format-stabilisation phase may come first.
- HRMS without an API: if your HR system cannot accept a structured push, the automation value ends at extraction and you have a spreadsheet, not a closed loop. We'll scope that dependency before we build.
How we start
How we start — map your documents before we build
We don't quote a pipeline without first auditing what your documents actually look like.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your real documents, map your document types and volumes, test extraction accuracy on your hardest templates, and hand back a feasibility brief and ROI estimate — yours to keep whether you proceed or not. If you build with us, the Sprint fee is credited against the project.
- 02
Build
Classification model trained on your document types, extraction schema per document, validation rules against your HRMS policies, and integration with your HR system and exception review workflow.
- 03
Operate and improve
Live pipeline with a monitoring dashboard, exception queue management, and a retraining cycle — new document templates are added without rebuilding the whole pipeline.
FAQ
Frequently asked questions
What document types can the pipeline handle?
Any document your HR team processes — offer letters, ID proofs (PAN, Aadhaar, passport), educational certificates, bank details, Form 16, policy acknowledgments, exit clearance forms. We train the classifier on your specific document set, not a generic library.
How accurate is the extraction on scanned or handwritten documents?
Accuracy depends on document quality and template consistency. The Discovery Sprint establishes a baseline on your actual documents before we commit to a build. For typed PDFs and standard forms we target 95%+ on required fields; handwritten inputs typically lower, with exception routing handling the gap.
Does it integrate with our existing HRMS?
Yes, if your HRMS has an API or accepts file-based imports. We have built integrations with SAP SuccessFactors, Darwinbox, Keka, Zoho People, and custom databases. The integration scope is confirmed in the Discovery Sprint.
How does the team handle documents the system cannot process?
Any document below the confidence threshold is routed to an exception queue with the confidence score and the specific field that failed. An HR reviewer resolves only those cases — the pipeline processes everything else automatically. Exception rates typically fall over the first few months as the model trains on new patterns.
Does this meet data-protection and labour-law audit requirements?
The pipeline generates a timestamped audit trail for every document: intake, extraction, validation, HRMS push, and any manual review. Retention policies and access controls are scoped to your jurisdiction during the build. Personal data is not stored beyond your stated policy.
Get started
Show us your document backlog
Bring a representative sample of your HR document types and current volumes. In 45 minutes we'll tell you what automation is worth building — and what it would cost.
Book a Discovery Sprint