AI & Data
Prepare Business Data for Reliable AI Applications
AI quality depends on data quality, structure and access. We build the database, retrieval and transformation layers that allow AI applications to work with approved, relevant information instead of uncontrolled data dumps.
What We Build
Database integration
Data normalization and transformation
Metadata preparation
Document chunking and embeddings
Vector data pipelines
Query result shaping
Analytics datasets
Structured output pipelines
How the Architecture Works
- Source databases/documents
- ETL/processing layer
- Relational database and/or Weaviate
- APIs
- LangChain orchestration where AI is used
- Application layer
Production Controls
- Allowed-table controls
- Result-only sharing with LLMs in DB Link patterns
- Data validation
- Access roles
- Usage/logging
- Backup and recovery planning
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
- D-Law structured legal data pipeline
- Educational publisher data extraction and matching
- MultiLLM AI Platform database and vector layers
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.