AI & Data
AI Chatbots and Copilots Grounded in Business Context
We build AI chat experiences that can work with private knowledge, live databases or both. Unlike a generic chatbot, the goal is a purpose-built assistant with clear instructions, controlled data access, role permissions and integration into the application where work happens.
What We Build
Knowledge-base chatbots
Database analytics assistants
Internal operations copilots
Customer or staff information assistants
Document-processing chat interfaces
Embedded chat inside existing portals
How the Architecture Works
- Chat UI or embedded interface
- Bot instructions and model parameters
- Knowledge base and/or database connector
- LangChain orchestration
- LLM provider
- Role and usage controls
Production Controls
- Clear system instructions
- Restricted data access
- Conversation memory where appropriate
- Budget controls
- API authentication
- Human escalation for uncertain cases
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, DB Link Bot and LLM + Database Bot
- D-Law portal integration through MultiLLM AI Platform API
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.