AI & Data
Extract, Compare, Translate and Structure Business Documents with AI
We have built OCR and document-intelligence implementations across legal files, educational catalogues and office MFP devices. The solution pattern combines scanning or upload, AI/OCR extraction, structuring, validation and delivery into business-ready formats or downstream systems.
What We Build
OCR to editable text
Searchable PDF creation
Document comparison
Translation
Bill and receipt recognition
Business card extraction
ID and passport recognition
Cheque recognition
Purchase order, sales order and invoice recognition
Handwriting-related processing
How the Architecture Works
- Document capture
- OCR/AI extraction
- Schema mapping
- Validation and review
- Export to Word/PDF/CSV or database
- API/email delivery
Production Controls
- Human review for accuracy-sensitive fields
- Language and document-format constraints agreed for each implementation
- Process history/logging
- Controlled output destinations
- Error handling and retries
How We Deliver
Use-case and data assessment
Prototype or proof of concept
Evaluation against representative questions/documents/data
Integration with applications, databases or workflows
Access controls, logging and cost controls
Production rollout, monitoring and iterative improvement
Relevant Proof
- MFP-based AI Document Processing Solution
- Docinight: a mobile app with AI-powered OCR features for documents
- D-Law AI-OCR legal pipeline
- Educational publisher catalogue extraction
FAQ
Frequently asked questions
Do we need to choose one AI model?
No. Our MultiLLM AI Platform architecture supports multiple LLM providers, allowing model choice by use case. A project can also be designed with an abstraction layer so the application is not unnecessarily tied to one provider.
Can the AI use our private documents?
Yes. Our MultiLLM AI Platform supports private knowledge bases using document chunking, embeddings and Weaviate-based semantic retrieval.
Can AI query our existing database?
Yes. The DB Link Bot pattern sends the question and allowed table structure to the model, executes generated SQL against the database, and returns query results for summarisation rather than sending the entire dataset.
How do you reduce AI risk?
Use-case-specific controls can include role permissions, restricted data access, human review, token limits, validation, logging and evaluation with representative examples.
Build an AI Solution Around Your Workflow
Bring us a real workflow, dataset or document process. We can help identify where AI adds value, what should stay deterministic, and how to move from prototype to a governed production solution.