LLM Integration Consulting for Organisations

Large language models are most useful when they are connected to your own systems, data and processes. CatalytIQ Systems helps organisations plan and deliver LLM integration that is secure, measurable and fits the platforms you already run, bridging business requirements and AI capabilities.

What LLM integration involves

• Grounding a model in your own documents and knowledge, so answers reflect your content rather than general knowledge.
• Calling LLMs from existing applications and workflows through APIs.
• Letting AI assistants take actions in other systems, within clearly defined limits.
• Adding human review steps where accuracy or accountability matters.

Starting with requirements

Most LLM projects that stall do so because the task was never pinned down. Before choosing a model or tool, the work defines who the users are, what inputs the model will see, what a good output looks like, what level of error is acceptable and how success will be measured. A small set of real examples becomes the yardstick for evaluating options.

Connecting to your systems and APIs

An LLM feature is an integration like any other. It needs authentication, sensible limits, logging, error handling and a fallback when the model is unavailable or unsure. This work draws on API strategy and systems integration practice so the AI component fits your architecture rather than sitting beside it.

Security, privacy and data handling

• What data is sent to the model, and whether it needs to be reduced or de-identified first.
• Where data is processed and stored, and what the provider's terms allow.
• Access control, so the model can only reach information the user is allowed to see.
• Risks specific to LLMs, such as prompt injection and unreliable outputs.
• Audit logging and monitoring once the integration is live.

For organisations with compliance obligations, these decisions sit within a wider AI governance framework.

Typical deliverables

Depending on scope, deliverables can include:

• Requirements and evaluation criteria for the LLM use case.
• An integration design showing data flows, controls and dependencies.
• A working pilot, or a clear brief for your delivery team or vendor.
• Recommendations for monitoring, review and ongoing ownership.