Gain context, manage risk, and measure the real business impact of LLM adoption across your workforce — every provider, every seat, one source of truth.
Every provider ships its own dashboard, its own metrics, and its own definition of "active." Usage data is fragmented, stripped of business context, and silent on whether any of it is actually creating value. So every renewal, expansion, and reallocation decision gets made on instinct.
"We have four AI vendors and four dashboards. Nobody can tell me what we're actually using."
— CIO, on consolidating AI tooling
"The board asks what we're getting for our AI spend. The honest answer is: we don't know."
— CFO, on the renewal conversation
"I can see token counts. I can't see which departments are getting value and which licenses are gathering dust."
— VP of IT, on provider-native reporting
"Security wants prompt-level oversight. Legal says prompt data can't leave our environment. Pick one — until now."
— CISO, on AI governance
UltraTrace collects usage and identity data from every AI provider you run, normalizes it into one model, enriches it with the way your business is actually organized — and tells you what to do next.
Connect ChatGPT Enterprise, Claude, Copilot, Gemini, and more through their native admin APIs, audit logs, and exports. Sessions, tokens, API volume, and identity data are normalized into a single standardized schema — with users correlated across providers automatically.
Map every user to the dimensions your business runs on — by IdP attribute sync, bulk import, or manual assignment. Assignments are effective-dated, so when someone changes teams, history stays true to the org chart as it was at the time of usage.
Every user is classified into an engagement state — leveraged, active, churned, or never used — and rolled up by any dimension. Three diagnostic levers turn the picture into action: Expand where demand is proven, Elevate where engagement is shallow, Reallocate where seats sit idle.
An optional three-layer classification model summarizes what kind of work AI is doing — code generation, data analysis, drafting, research — plus a sensitivity signal. Classification runs entirely inside your environment, and raw prompt content is purged the moment it's categorized. Labels stay. Prompts don't.
UltraTrace ships from the cloud marketplace as a single-tenant application in your AWS, Azure, or GCP environment. Connect your AI providers with read-only credentials and your identity provider via SAML or OIDC.
Tell UltraTrace how your business is organized — departments, cost centers, projects, risk tiers. Sync from your IdP and the mapping maintains itself. Change the org structure and every metric recomputes automatically.
Dashboards, alerts, and Expand / Elevate / Reallocate recommendations — filterable by any dimension, exportable anywhere, and queryable by your own tools through an MCP-compatible API.
average share of provisioned AI seats found idle or churned at first scan
median time from deployment to first reallocation decision
customer data stored outside the customer's own cloud boundary
"We walked into our Copilot renewal knowing exactly which 400 seats were leveraged and which 180 had never been touched. That conversation went very differently than last year's."
"Legal blocked every prompt-analytics vendor we evaluated — until this one. Classification happens inside our VPC and the raw prompts are gone afterward. That's the architecture that got us to yes."
Thirty minutes with your own data. We'll deploy into a sandbox in your cloud, connect one provider, and show you your real adoption picture — leveraged, active, churned, and never used.