EquippedAI: three years rebuilding a private-equity software platform.

• Stabilized and refactored the product suite.
• Built AI workflows into Minerva, their core platform.
• Improved performance across the platform.
• Rebuilt the infrastructure underneath it (see below).

The saving didn't come from negotiating with the cloud provider. It came from fixing the architecture that was inflating the bill.

The product was essentially the same for every client, but it had drifted into per-client copies: separate app deployments, separate code branches, repeated background services, database servers that were barely used, and a graph database scaled up for a workload it didn't fit.

One constraint was real: each client's database had to stay accessible only to that client. So we kept that boundary and stopped duplicating everything around it:

• Pulled client-specific behavior back into one product core.
• Cut per-client app deployments and repeated background services.
• Retired database servers provisioned like primary systems but rarely used.
• Reduced reliance on the graph database where the product didn't need it.

• Cloud infrastructure costs came down by 75%.
• A faster path from development to production.
• Lower operational overhead, with the same output.
• A platform that is easier to release, debug and extend.

AI features only hold up on a platform that is stable, fast and affordable to run. Much of this work wasn't AI at all, and it is the reason the AI work could succeed.