Proof, not promises.

The problem

A Fortune 50 bank had grown by acquisition and by department. Every business line bought its own tools, and nobody could answer a simple question: how many applications do we actually run, and which ones do the same job? Licensing costs compounded, security review queues grew, and audit findings multiplied - while leadership had no trustworthy inventory to act on.

The approach

Cijara Group's founder built and socialized an enterprise metadata dictionary across more than 6,300 active applications - a common language for what each tool did, who owned it, what data it touched, and what it cost. Once every application was described the same way, redundancy stopped being an opinion and became a fact that could be measured, priced, and acted on.

The result

The catalog surfaced more than 2,000 redundant applications - nearly one-third of the active footprint - and drove $80M in annual deprecation savings. Just as important, the institution left the engagement with a governance discipline that kept the sprawl from coming back.

What this means for your business

The same pattern is repeating right now with AI. Departments sign up for chatbots, copilots, transcription tools, and SaaS plugins independently - overlapping subscriptions, unreviewed data terms, and no inventory. Our AI Vendor Risk Assessment applies this exact rationalization discipline to your AI stack: catalog every tool, verify what each vendor does with your data, consolidate what overlaps, and retire what you don't need. The savings from eliminated subscriptions frequently pay for the engagement.