DOCUMENT INTELLIGENCE
Turn documents into structured data you can trust.
Invoices, contracts, forms, statements, scanned PDFs — we build layout-aware extraction pipelines with validation and human review, so the data that comes out is accurate enough to act on automatically.
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Enterprise-grade delivery. Human-verified outcomes.
Built for teams that can't afford to guess.
Invoices, contracts, forms, statements, scanned PDFs — we build layout-aware extraction pipelines with validation and human review, so the data that comes out is accurate enough to act on automatically.
The data is trapped in the document.
Most back-office work is a human reading a document and typing what they see into a system. It's slow, error-prone, and it doesn't scale with volume.
Document intelligence automates that — but accuracy is everything. A pipeline that's 95% right still needs a human for the 5%, so we design the review step into the system from day one. The result: most documents flow straight through, and the rest land in a fast review queue instead of a person's inbox.
The extraction pipeline we build.
Ingest + classify
Documents arrive (email, upload, API) and are classified by type — invoice vs. contract vs. form.
Layout-aware OCR
Parsers that understand tables, columns, and scans — not naive text dumps.
Field extraction
Structured extraction of the fields you need, combining model extraction with rules.
Validation
Type checks, totals that must add up, cross-field rules, and lookups against your systems.
Human-in-the-loop
Low-confidence fields route to a review UI; everything else flows straight through.
Deliver
Validated data lands in your database, ERP, or downstream workflow via API.
What we extract from what.
Ways to engage.
A messy PDF in. Validated, structured data out.
Layout-aware parsing, schema-driven extraction, deterministic validation, then confidence-based routing.
Most documents flow straight through; the low-confidence ones land in a fast review queue instead of producing wrong data silently.
We measure accuracy on your real samples first.
Before committing to a build, we run a proof-of-value on your actual documents and report measured field-level accuracy.
That number drives the design of the human-review step — so you automate the volume safely and keep a person on the exceptions.
Send us a stack of your documents.
Share a representative sample. We'll run a quick assessment and tell you what's automatable, at what accuracy, and what it would take.
Document type — Typical output
Vendor, line items, totals, tax, dates → AP system
Parties, terms, dates, clauses, obligations → CLM
Field values, validation flags → your DB
Transactions, balances, categories → reconciliation
Identity fields + verification signals → onboarding
Best-effort OCR + confidence + review queue
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
How accurate is it?+
It depends on document quality and type. We measure accuracy on your real samples in the proof-of-value stage and design the human-review step around the residual error rate.
Do we still need people?+
Fewer, and doing higher-value work. The pipeline handles the volume; humans handle the exceptions through a fast review queue.
Can it handle our messy scans?+
Layout-aware OCR plus confidence scoring handles a lot. Truly illegible inputs route to review rather than producing wrong data silently.
Where does the data go?+
Wherever you need — database, ERP/AP system, or a downstream workflow, delivered via API or direct integration.