AI & Data
AI Readiness and Solution Design Focused on Real Business Value
We help organisations identify where AI can improve a workflow and where traditional software remains the better choice. A good AI engagement starts with the process, data, risk, users and measurable outcome before choosing models or tools.
The fastest way to start: our fixed-price AI Readiness Audit delivers a ranked list of AI use cases with estimated ROI in 1–2 weeks.
What We Build
AI use-case discovery
Data and workflow assessment
Architecture options
Proof-of-concept planning
Model and retrieval strategy
Integration planning
Governance and cost considerations
How the Architecture Works
- Business workflow map
- AI opportunity shortlist
- Data readiness findings
- Solution architecture
- PoC scope and evaluation plan
- Production roadmap
Production Controls
- Avoid unsupported automation
- Use human review for sensitive decisions
- Plan permissions and data boundaries
- Measure quality with representative inputs
- Track cost and latency
- Prefer simple deterministic logic where it is more reliable
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 architecture and governance capabilities provide a concrete reference platform
- Case studies show AI used to remove specific manual bottlenecks rather than as a generic add-on
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.