Pervaziv AI today announced its 3-Tier Cortex Inference Cache Architecture, a new performance and governance layer designed to reduce repeated work across enterprise AI without weakening the controls that keep context current, permissions intact and results trustworthy.
Three Tier Inference Caching in Cortex adds context, prompt prefix and exact response reuse, delivering up to 150x faster prefill, 11x faster response delivery and 2.25x throughput. Cortex adds context, prompt prefix and exact response reuse.
The architecture separates caching into three forms of reuse: application context, model prompt prefix and exact completed response. Each tier has a different validity test and security boundary.
Recent Cortex measurements show why that separation matters. In a repeated conversation style workload, prompt processing fell from 2,913 milliseconds cold to 19.3 milliseconds warm, approximately 150 times faster. In a separate approved read only text route, an initial request completed in 3,132 milliseconds and an identical repeat returned a reported cache hit in 281 milliseconds, approximately 11 times faster with about 91 percent lower end to end latency. A separate serving workload matrix completed 240 requests with zero request failures and reached 2.25 times the throughput for one mid length workload under moderate concurrency.
These measurements describe different parts of the system, not one universal speed claim. Prompt prefix reuse accelerates repeated input processing while the model still generates a new answer. Exact response reuse can avoid generation for an approved identical request.
"Enterprise AI has to get faster without becoming less trustworthy. Cortex brings context, compute and control together so AI can scale across complex workflows while staying current and reliable."— Anoop Jaishankar
"The next step in enterprise AI performance is not simply caching more," said Anoop Jaishankar, Founder and CEO of Pervaziv AI. "It is knowing exactly what can be reused, what changed, who is still authorized to use it, and when fresh computation is required. Cortex is turning inference caching into a governed capability, where speed comes from removing repeated work without removing the checks that make the result trustworthy."
Enterprise AI rarely begins from an empty prompt. A developer may continue a coding task across many turns. A security analyst may investigate changing evidence. A user in Cortex Discover may reason across browser tabs, then continue the objective in Visual Studio Code or Cortex Cloud.
Tier One: Reuse Current Context. Before a model generates a response, Cortex may need to gather conversation state, selected files, repository material, retrieved passages, browser content, workspace state and governing instructions. The first cache tier reuses eligible context preparation when source state and authorization remain current. Freshness is controlling condition, tied to source and authorization state.
Tier Two: Reuse Model Preparation. For eligible inference environments, Cortex can use prompt prefix reuse when the beginning of the model request remains identical under the correct isolation boundary. In one inference test, prompt processing fell from 2,693 milliseconds cold to 19.3 milliseconds warm, approximately 140 times faster. In a separate conversation style test, from 2,913 milliseconds to 19.3 milliseconds, approximately 150 times faster. Warm total latency 547 to 554 milliseconds, repeat runs within approximately 6 percent.
Tier Three: Reuse an Exact Response, Selectively. When an approved route receives a completely identical request, Cortex can return a previously completed response without running model generation again. A live test used an approved read only text task. First request was cache miss and completed in 3,132 milliseconds. Identical repeat returned hit in 281 milliseconds. Responses matched. Approximately 11 times faster and about 91 percent lower latency.
Governance across all three tiers ensures authorization remains separate from caching. Enterprise information does not follow one uniform ownership model. Cortex carries authorization into the reuse boundary, scope follows most restrictive material. If system cannot establish required boundary, architecture favors fresh computation.
The architecture also looks beyond speed of one warm request. For one mid length workload, moderate concurrency reached 2.25 times throughput, while 95th percentile latency increased by 19 percent. At larger inputs, additional concurrency produced little throughput benefit and much worse tail latency.
Pervaziv AI is treating inference caching as a route specific capability rather than a blanket switch. One tier can be enabled without assuming others are appropriate. The principle is simple: reuse context when its sources and access remain valid, reuse model preparation when the prefix actually matches, and reuse a finished answer only when the complete request is an approved exact match. If those conditions are not satisfied, recompute.
About Pervaziv AI
Pervaziv AI builds Cortex, an Enterprise AI Control Layer for secure, governed AI work across browser, mobile, development and cloud environments. Cortex coordinates specialized AI models and agents with model routing, search routing, skill routing, security, privacy, verification and managed execution capabilities.
