AI & Data
Build Generative AI Applications Connected to Your Business Data
We build generative AI experiences that combine modern language models with your own documents, databases, rules and applications. Our MultiLLM AI Platform is a governed multi-model platform where businesses can configure assistants, add knowledge, connect databases, control usage and expose bots through APIs.
What We Build
Internal knowledge assistants
Content summarisation and rewriting
Business data Q&A
AI copilots inside existing applications
Multi-model AI applications
Chart and report generation
How the Architecture Works
- Application/UI layer
- Backend orchestration using Python/Flask and LangChain
- Selected LLM provider
- Private knowledge or database context
- APIs and business systems
- Logging, permissions and usage controls
Production Controls
- Role-based access
- Private knowledge isolation
- Token/budget controls
- Controlled data sharing with LLMs
- Human review for high-impact outputs
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
- MultiLLM AI Platform General LLM Bot and hybrid LLM + Database Bot
- D-Law legal intelligence implementation
- Educational publisher natural-language analytics
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.