AI & Data
Integrate the Right LLM into Your Application and Workflow
Our MultiLLM AI Platform supports different AI models per bot so businesses can choose a more cost-efficient model for simple tasks and a more capable model for complex analysis. We can integrate model APIs behind a controlled application layer rather than binding a product to one provider.
What We Build
LLM API integration
Multi-model routing
Prompt and instruction configuration
Model parameter tuning
RAG integration
Database/tool integration
Application/API embedding
How the Architecture Works
- Application frontend
- Backend abstraction layer
- LangChain orchestration
- LLM provider adapters
- Data and retrieval sources
- Usage tracking
Production Controls
- Provider abstraction
- Token limits
- Timeout/error handling
- Data minimization
- Quality evaluation
- Model selection by task complexity
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
- Our MultiLLM AI Platform supports OpenAI, Gemini, Claude, Llama, DeepSeek, Mistral and additional models
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.