Understand the documents
Collect representative formats, languages and difficult cases. Define required fields, source provenance and the target system.
NOVACOM / DELIVERY
Turn incoming documents into reviewable, usable records.
For operations teams retyping information from forms, invoices or business documents. We design extraction and validation workflows that route uncertain results to the right person.
WHAT WE BUILD TOGETHER
Extract and validate
Combine suitable extraction methods with business validation. Preserve the original source and flag missing or inconsistent values.
Connect the workflow
Prepare approved records for the destination system, with duplicate handling, human review and a recoverable process.
UNDERSTAND THE ENGAGEMENT
What it is.
How we deliver it.
What you are buying
Document AI extracts or interprets information from business documents so it can enter a defined workflow. It can combine text extraction, suitable models and deterministic validation. Human review remains important where uncertainty or consequences demand it.
Where it is useful
Useful candidates include repetitive intake of forms, invoices and operational documents with known destination fields. We assess format variation, scans, tables, languages and handwritten content before selecting the extraction approach.
What a delivery scope can include
The design preserves source references, defines a field schema and validates required values. Reviewers can compare proposed records with the original document. Integration work includes duplicate detection, retries, approval and a clear record of what was written to the destination system.
What to bring to discovery
- Representative documents, including difficult cases
- Required fields and validation rules
- Destination-system interface and duplicate-handling rules
- Review owners, data boundaries and retention requirements
How we evaluate the result
Measure field correctness and the proportion of records accepted after review. Inspect critical fields separately from low-impact text. Test missing pages, conflicting totals, duplicate submissions and integration outages using representative cases.
Decisions to make early
A demonstration on clean sample PDFs does not establish accuracy on the full document population. Confidential records must be handled within approved boundaries. Retrieval-based question answering is a related but distinct scope from validated record extraction.
BUILD YOUR UNDERSTANDING
Useful reading before we talk.
Private AI & RAG · 3 MIN
Explore all services ↗What is RAG? A practical guide to AI that uses your company knowledge
Learn how retrieval-augmented generation works, where it helps, and how to test an assistant against your own documents.
Build with AI · 3 MINHow to build an app with AI: from an idea to a tested workflow
Turn a business problem into a brief, a working slice and a verified application, with clear data and deployment decisions.
AI for business & the GCC · 3 MINWhat can your company do with AI? Practical examples by industry
Explore retail, logistics, professional services, property and government workflows, with data needs and sensible success measures.
A LITTLE MORE CLARITY
Good questions.
Clear answers.
Can you guarantee perfect extraction?+
No. Accuracy depends on document quality, variation and the fields involved. We test representative samples and define review rules for uncertain or consequential results.
How is this different from RAG?+
Document extraction turns content into defined fields or records. RAG retrieves relevant evidence to answer questions. A workflow can use both, with separate validation and permissions.
DISCUSS YOUR FIRST USE CASE