What teams ask before connecting.

Punk turns successful reasoning, research steps, tool plans, decisions, and outputs into reusable know-how. That reduces model cost and response time. New or uncertain work still goes to your original model.

Punk is for teams running AI agents at enough volume that repeated model work affects cost or response time. The best fit has repeatable requests and a person who can judge whether the output is acceptable.

Choose work that happens often, produces a reviewable answer, has a clear owner, and mostly reads rather than changes information. Support classification, internal summaries, and repeated research are practical starting points. Payments, account deletion, and other high-impact actions are not good first candidates.

No. You keep your current models, agents, and application. Punk works alongside them to learn from completed work and avoid eligible model work.

For compatible model requests, the first step can be a base URL and API key change plus identifiers that keep each customer’s work separate. Agents that use tools, take actions, or need deeper feedback may require additional integration.

Yes. Punk can begin in measurement-only mode. Your current model remains responsible for every user-facing answer while Punk finds repetition and estimates the opportunity. Estimated savings are reported separately from savings actually achieved.

Punk identifies work your agents perform repeatedly, tests whether a prior answer or proven process still works, and reuses it when the evidence and context match. This avoids eligible model calls without treating every similar-looking request as the same.

Punk compares the reusable result with completed work and fresh model answers. Proof applies only to the specific kind of work tested. Punk reports the examples checked, the matches, and any important differences; it does not guarantee that every future input will be correct.

The request goes to your original model when the reusable result is new, stale, ambiguous, out of scope, or fails to run. Prohibited work can still be blocked. Deployment availability, latency, and network failure behavior must be validated for your environment.

Punk treats actions that change data more cautiously than read-only work. High-impact actions can require approval, and tests do not perform real-world changes. Your team should review the exact tools and safeguards for its deployment.

Punk keeps a history of agent requests and results plus information derived from them, such as repeated patterns, test evidence, decision records, and reusable processes. Storage, retention, redaction, export, deletion, encryption, and access controls must be reviewed for the deployment in scope. Review security boundaries.

By default, each customer’s optimization stays separate and raw data is not used across customers. Any shared or aggregate learning should require explicit opt-in and deployment-specific review.

Punk supports compatible OpenAI and Anthropic requests plus configured model providers. Exact compatibility varies by client and feature, so use the integration documentation for the setup in scope.

Free is $0 with 10,000 monthly agent requests. Pro is $99 per month with 500,000 monthly requests and higher limits. New verified accounts receive a 30-day Pro trial without a credit card. Enterprise pricing is custom. Model-provider charges are separate. Compare plans.