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Novistu

Use case : automation

AI document extraction

AI document extraction turns unstructured documents into validated, structured data: contracts, reports, forms, statements and filings read layout-aware, fields extracted against your schemas, checked by business rules, and delivered into your systems with an audit trail per field.

The problem

Critical business data lives hostage in documents: analyst reports, contracts, application forms, supplier packs. Teams retype the important parts into systems by hand, slowly and with errors, because templates vary and classic OCR breaks on every new layout.

Systems it touches

Databases and data warehousesERP and internal systemsDocument storesReview interfaces

How it works

The automated version, step by step.

  1. 01

    Ingest

    Documents arrive from drives, email, portals or scans into one pipeline.

  2. 02

    Parse

    Layout-aware parsing preserves tables, sections and footnote relationships.

  3. 03

    Extract

    Fields pulled against your schemas with confidence scores per value.

  4. 04

    Validate

    Business rules check arithmetic, ranges, references and cross-document consistency.

  5. 05

    Deliver

    Validated data lands in your databases or systems; low-confidence fields queue for review with sources shown.

What changes

Documents become data: hours of retyping per document become a review pass, downstream systems finally trust their inputs, and every field can show where it came from.

Variations by industry

Applied across finance (statements, reports), legal (contracts), logistics (shipping docs), healthcare (referrals) and research (report collections), each with domain-specific validation.

Common questions.

Modern models read structure and meaning rather than fixed templates, so a new layout is handled as a variation. Genuinely novel structures get confidence-scored and queued for review, which also improves future extraction.

Parallel runs against your team's manual output on real samples, field-level confidence scores, validation rules that catch what models miss, and accuracy reporting as a standing dashboard.

Yes: open-weight models and in-region storage are supported configurations for sensitive document sets.

Tell us what is eating your team's hours.

A short brief, answered within one working day. The first call is free, and if AI is not the right answer, we will say so on that call.

First call free : honest about fit : no obligation