Vibe coders welcome.
Let your internal vibe coders move fast while Pamer enforces corporate controls across every AI interaction.
Before the model
Evaluate, block, or augment the request.
After the model
Capture and validate the response.
Across the interaction
Monitor, archive, and learn from feedback.
Policy is enforced twice.
Pamer evaluates what goes to the AI and what comes back. That two-pass design is the heart of the system.
Interaction received
A prompt or request enters from chat, an API, a workflow, an agent, or an MCP-connected tool.
First-pass policy gate
A lightweight classification layer evaluates the raw request against contextual policy and hard rules, including sensitive data and attempted circumvention.
Pass, block, or augment
Compliant requests continue. Violations can be blocked, or sensitive elements can be replaced with context-specific placeholders that preserve the request’s intent.
External AI does the work
The approved or augmented request is sent to the selected outside model. Pamer does not require that provider to change its service.
Second-pass response gate
Pamer captures the AI response and checks it again so policy violations are not introduced on the way back to the user or agent.
Return, record, improve
A compliant result is returned—or a violation is blocked or flagged. The interaction can be archived for compliance and used as feedback for refinement.
The policy layer can evolve.
The filing describes example embodiments that combine a lightweight language model, policy gates, semantic search, feedback, and continuous learning.
Pamer is in development. These are patent-described implementation options, not a promise that every production deployment will use every component.
Contextual and hard policy rules
Checks can reflect acceptable use policy, user role, domain, brand guidance, and conversation context, while hard-coded rules override when required.
Semantic detection
Embeddings and vector search can identify synonyms, coded language, and attempts to work around policy—not only exact keyword matches.
Weighted compliance decisions
Multiple signals can contribute to a compliance score used to pass, augment, block, or flag an interaction.
Organization-specific knowledge
Policy data, labeled examples, domain material, and historical violations can inform the lightweight classification layer without retraining an outside LLM.
Human feedback and overrides
Reviewers can confirm or override decisions, creating feedback that helps refine future classification and thresholds.
Continuous adaptation
The provisional describes a continuous learning engine that can maintain violation patterns and adapt to policy changes and new circumvention attempts.
One policy layer, several ways in.
Pamer is designed to govern AI interactions wherever they occur, rather than binding the organization to one model or one interface.
Chat and assistant interfaces
Govern direct conversations with outside AI services.
APIs and application workflows
Evaluate natural-language streams inside software the organization already runs.
Critic in the loop
Place Pamer inside an engineered workflow to review proposed agent actions or outputs before they continue.
Agents
Apply acceptable use policy to agent requests and responses, not only human chat.
MCP
Expose policy evaluation through an MCP server for compatible tools and agent environments.
Evaluate the system against a real policy.
Pamer is patent pending, in development, and not generally available. Design partners help test the two-pass enforcement model against actual acceptable use policies, workflows, and review requirements.
A design partnership begins with a named owner, a real policy document, and a non-binding letter of intent.