Solution · Intelligent Document Processing
Documents in, structured data out — with a human checking what matters.
Invoices, resumes, contracts, KYC forms, purchase orders: most businesses run on documents that a person reads and re-types. Intelligent document processing gives that job to AI that reads at scale — with confidence scores and human verification where errors are expensive.
What is intelligent document processing?
Intelligent document processing (IDP) uses AI — including large language models — to read unstructured documents such as invoices, resumes, contracts, and forms, and convert them into structured, validated data your systems can use. Unlike traditional OCR, IDP understands context: it can find the total on an invoice layout it has never seen, or recognize that 'led a 5-person pod' is management experience. Low-confidence extractions route to humans for verification.
The problem
The cost of humans reading documents
Data entry is a full-time job that shouldn't exist
Someone reads each document and types its contents into a system. Multiply minutes-per-document by monthly volume and it's often a full salary — or several.
Every format change breaks the routine
A new vendor's invoice layout or a new resume template slows the human reader down. Template-based OCR breaks entirely; people just get slower and less accurate.
Backlogs decide your cycle time
Documents queue for the person who processes them. Approvals, payments, and shortlists all wait on that queue.
Errors are invisible until they cost money
A transposed digit in a bank account or an invoice amount isn't caught at entry — it's caught at reconciliation, or by the vendor calling.
How we work
How Hab implements it — measured, not promised
Every engagement follows the 4D Method: Diagnose, Design, Deploy, Deliver. Business problem first, technology second, results against a baseline.
Start with one document type
The highest-volume or highest-risk one. We baseline current processing time, error rate, and backlog before any AI touches it.
Extract with context, not templates
LLM-based extraction handles layout variety without per-vendor templates. Every extracted field carries a confidence score.
Verify where errors are expensive
High-confidence routine fields flow straight through; low-confidence or high-stakes fields (amounts, account numbers, legal terms) route to a human with the source highlighted.
Feed your existing systems
Validated data lands in your ERP, ATS, or database via integration. The document, the extraction, and the verification are all logged for audit.
What it returns
Outcomes you can hold us to
Published figures come with methodology; engagement figures are measured against your own baseline.
Seconds
per document instead of minutes — at any volume
12,400+
resumes per quarter processed through our own extraction pipeline
Field-level
confidence scores and audit trail on every extraction
Straight answers
Questions leaders actually ask
How accurate is AI document extraction?
High enough that routine fields flow through automatically, and honest enough to know when it isn't sure. Every field carries a confidence score; low-confidence extractions route to human verification. Accuracy on your documents is measured in the pilot, not asserted in a pitch.
Can it handle our specific document formats?
Almost certainly — LLM-based extraction understands context rather than relying on fixed templates, so new layouts don't break it. The pilot runs on a sample of your real documents so you see accuracy on your formats before committing.
What about sensitive documents and data privacy?
Data flows are documented, processing can be scoped to approved providers or private deployments, access is role-based, and DPDP/GDPR obligations are mapped during design. Sensitive-document workflows get stricter verification gates by default.
Which documents are the best starting point?
High volume + structured destination + measurable pain: invoices into the ERP, resumes into a ranked shortlist, KYC forms into onboarding. One document type, one pipeline, measured results — then expand.
Start with the diagnosis — not the demo.
A 30–45 minute working session on your actual process. If AI isn't the answer, we'll say so on the call.
No retainers to start · Pilot-first · Founder-accountable