The runtime control layer
your AI workflows are missing.
Each team builds policy logic into each workflow separately. No standardization, no central audit, inconsistent enforcement. Corules is the enforcement layer you call once — and every AI workflow inherits.
The fragmented AI governance problem
When AI workflows are built team by team, each team implements its own interpretation of the approval rules. Finance validates differently from Sales. Operations has a different escalation threshold than HR. Nobody can audit across all of them.
This isn't a technology problem — it's an architecture problem. Every AI workflow needs policy enforcement, but there is no standard layer to provide it. The result is risk accumulating without visibility.
“Each team builds custom rules in each workflow separately. We have no standardized enforcement layer, no central audit, and inconsistent governance across the estate.”
One control plane. Every AI workflow.
Corules is a standalone runtime deployed once in your infrastructure. Every AI workflow that needs policy enforcement calls the same API. Policy logic is maintained centrally — not duplicated across workflows.
Policy authors define rules centrally
Business rules, compliance constraints, and escalation thresholds are expressed in CEL and maintained in one place — not embedded in each workflow.
Corules compiles and publishes policy sets
Rules are compiled at publish time into a versioned policy set. Compilation catches errors before deployment. Every change is versioned.
Any AI workflow calls /v1/validate before acting
Salesforce flows, Power Automate, Azure OpenAI agents, and custom LLM agents call the same REST endpoint. The enforcement logic is identical regardless of caller.
Every decision is logged to a central audit ledger
Immutable records across all workflows. Query by use case, actor, date range, or outcome. Replay any decision with the policy version that was active.
Integrates with every platform in your AI estate
Corules is integration-agnostic. Any system that can make an HTTP call can call Corules. Native integration guides are available for:
What governance standardization delivers
One audit trail across all AI workflows
Every AI decision — regardless of which workflow made it — is in a single queryable ledger. No more piecing together audit evidence from six different systems.
Policy changes deploy once, apply everywhere
When a compliance rule changes, you update one policy set. Every workflow that calls Corules inherits the change immediately — no coordinated rollout across teams.
Consistent enforcement across business units
The same rule produces the same outcome whether Sales, Finance, or Operations is running it. Enforcement is deterministic, not interpretive.
Governance evidence for regulators and auditors
When an auditor asks how a decision was made, you replay it: policy version + input → same outcome. No manual reconstruction from email threads.
Questions from CIOs
How does Corules integrate with existing AI workflows?
Corules exposes a REST API (/v1/constraints and /v1/validate) and an MCP server. Any workflow that can make an HTTP call can integrate — Salesforce flows, Power Automate, Azure OpenAI agents, custom LLM agents, and more. Your existing workflows call Corules before executing any AI-proposed action.
Does every team need to learn new tooling?
No. Corules is a thin enforcement layer teams call via API. Policy authors maintain rules centrally. Application teams integrate via REST or MCP — no new SDK, no agent framework lock-in. Policy logic lives in one place; every workflow inherits it automatically.
How do we maintain a central audit trail across all AI workflows?
Every decision Corules evaluates is written to an immutable audit ledger — regardless of which workflow called it. You query the ledger by tenant, use case, date range, or actor. Each record includes the policy version, normalized input hash, outcome, and reasoning — replayable at any time.
What happens to governance when a policy changes?
Policy changes are versioned. Corules compiles new policy sets at publish time and activates them atomically. Historical decisions remain linked to the policy version that was active when they were made — so audit replays always produce the exact same outcome.
Standardize AI governance across your entire estate.
One enforcement layer. Central audit. Consistent policy — for every AI workflow in your organization.
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