Polyrific Catalyst
The AI agent governance platform and runtime.
Catalyst deploys on your infrastructure, sits between your people and your data, and governs agents from the inside. It is model-agnostic by design. The AI recommends and calls out. Your people decide. The file keeps the record.
Why a governance runtime
Enterprise AI fails in production for the same reasons everywhere: data spread across dozens of systems, processes that do not connect, and tools that cannot see the full picture. Adding one more AI tool does not fix that. What fixes it is a runtime that holds the context, runs the agents, and keeps the record.
In insurance terms: a submission touches appetite rules, decline history, and the documents themselves. An agent on Catalyst works from all three, cites what it used, and the underwriter's decision lands on the same file.
It is why inference apps on Catalyst deliver outcomes, not experiments.
The Five Subsystems
Five subsystems, one runtime. Each does a specific job; together they produce the record.
Institutional Memory Manager (IMM)
Connects to enterprise data sources and abstracts them into persistent, queryable institutional knowledge: context, relationships, and evidence, not just embeddings. What your organization knows stops living in individual heads and starts compounding.
Agent Manager
Orchestrates and governs every agent Catalyst runs, from long-running workers to the ephemeral agents a task spawns, with intent parsing, delegation, shared-blackboard collaboration, and detailed execution logging. Nothing runs outside the runtime, so nothing escapes the record.
Model Intelligence
Continuously monitors AI providers and models across availability, capability, behavioral drift, latency, and price, maintaining look-alike mappings for failover without quality loss. The right model for each task today, and no lock-in when the landscape shifts.
Governance and Trust
Enforces policy, validation, and safety constraints in the runtime itself, with confidence thresholds and human-in-the-loop escalation. Hard refusals run on deterministic rules, and every recommendation is defensible under examination.
Observability
Captures runtime metrics, cost attribution, error tracking, and full replayability across every agent and model in any inference chain. When someone asks what happened on this file, the answer is a record, not a reconstruction.
Runs on your infrastructure
Catalyst deploys on customer infrastructure. Your data stays where your policies say it stays, and the runtime comes to it.
It governs from the inside. External observability tools watch a system from alongside it and produce dashboards. Catalyst sits between users and data, runs the agents itself, and produces a file-level record: what the AI recommended, and who decided.
Catalyst is licensed with the applications that run on it. Access is included with inference app subscriptions; dedicated instances and direct arrangements are available for specific enterprise requirements.
Catalyst Studio
The authoring environment for building inference apps on Catalyst: minimal input to working output, on the same governed runtime. For the teams closest to the work.
Inference Apps
Production applications built on Catalyst, each aimed at a specific business workflow. Browse what already exists before commissioning something new.
See Catalyst on a real workflow
