AI & Data
AI Agents for Structured, Multi-Step Business Tasks
Agentic systems are useful when a task needs multiple controlled steps such as extraction, validation, querying, transformation or reporting. We already use orchestration patterns in MultiLLM AI Platform and build custom workflow logic around them for each project.
What We Build
Document extraction agents
Database query assistants
Validation and review workflows
Task-specific AI assistants
API-connected business operations
Multi-step processing pipelines
How the Architecture Works
- Task definition and tool boundaries
- Model/orchestration layer
- Approved data/tool access
- Deterministic validation where possible
- Human approval for sensitive actions
- Result logging
Production Controls
- Least-privilege access
- Input/output validation
- Explicit tool permissions
- Timeout/retry handling
- Human-in-the-loop checkpoints
- Traceable results
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 configured bots for specific purposes
- OCR/image processing agents
- D-Law extraction + structuring + verification + querying pipeline
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.