Company F is an investment management consultancy of around 50 consultants. Their team had already adopted AI to speed up research and data analysis. The issue wasn’t a lack of adoption, it was a lack of guardrails. Staff were pasting client research, deal memos, and financial forecasts into ChatGPT and Gemini through personal accounts that sat entirely outside corporate IT oversight. No one knew which models were being used, what data had been submitted, or how much the firm was spending across departments. A single unguarded prompt can expose data that took years to build.

So, we deployed a managed AI guardrails layer that sits between the team and every AI model they use. Every request now flows through one secure API key into a governed environment, where access policies, usage logs, and cost attribution are applied automatically. IT decides which models each team can use and how much each can spend, with full audit trails ready for GDPR and ISO 27001 reviews. There is no hardware to procure, no servers to install, and no months of setup. As a result, shadow AI consolidated into one auditable channel, runaway spend collapsed into one flat monthly fee, and the firm was up and running in 1 to 2 weeks*, rather than the months a traditional on-premise build would have taken. Cost attribution to user, team, and project gave Finance the reconciliation it never had before, and response caching cut both latency and cloud spend on repeated queries.