AI & Data
Automate Repetitive Information Work with AI and Software
The strongest automation combines AI with deterministic software. We use OCR, extraction, database operations, matching, validation and APIs to reduce manual data handling while keeping business rules and human review where they matter.
What We Build
Invoice and receipt processing
Catalogue data extraction
Document comparison
Data matching and reconciliation
Natural-language report generation
Application-to-AI API workflows
Scanner/MFP document automation
How the Architecture Works
- Input capture
- AI/OCR extraction
- Validation and transformation
- Database/API update
- User review where needed
- Output/reporting
Production Controls
- Human verification on low-confidence or high-risk outputs
- Structured output schemas
- Logging and process history
- Access controls
- Exception handling
- Operational monitoring
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
- Educational publisher dual-extraction and automated comparison pipeline
- D-Law legal data processing pipeline
- MFP document-processing functions
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.