Make your company
legible and executable
to AI agents.
Operandi is a living company knowledge layer. It captures fragmented organizational knowledge, models it into structured processes, and exposes it through agent-native interfaces — so your agents can act safely and consistently without a human in the loop.
Your agents are running. Your knowledge is not keeping up.
AI agents are already deployed in production at fast-moving companies. But the knowledge they depend on — refund thresholds, escalation policies, exception rules — lives across Slack threads, Notion docs, Linear tickets, and in people's heads. When that knowledge is wrong or stale, agents fail visibly.
Four layers. One coherent system.
Operandi connects each layer into a compounding knowledge graph. The longer it runs inside your company, the more accurate and complete the model becomes.
Capture & Legibility
Ingest fragmented knowledge from Slack, Linear, GitHub, Notion, call recordings, emails, and databases into a queryable, structured representation of how your company actually operates.
Knowledge Modeling
Structure ingested data into a living model of company processes and decisions — refunds, pricing exceptions, escalation policies, incident response — capturing both documented and tacit know-how.
Agent Execution Interface
Expose the knowledge model through machine-readable, agent-native interfaces: skills files, APIs, MCPs, CLIs, and executable specs that agents consume directly without human mediation.
Closed Loop
Monitor agent outcomes against stated intent and continuously update the knowledge model to stay current. Prevents model drift as your company evolves.
Customer support agents that act with authority.
Refund decisions, escalations, and policy enforcement. High volume, measurable outcomes, clear ROI, and a visible failure mode when the underlying knowledge is wrong. The ideal starting point.
Most teams deploying a customer support agent spend weeks hardcoding rules that break the moment a policy changes. Operandi makes the knowledge layer maintainable, auditable, and continuously synchronized.
Operandi ingests your refund thresholds, escalation rules, and exception handling from wherever they live — Notion, Slack, recorded calls.
The model structures that knowledge into executable policy specs your agent can query: refund up to $50 without approval, escalate orders older than 14 days.
The customer support agent queries the model on each ticket. It gets a grounded, up-to-date decision, not a hallucinated one.
When agents make decisions, Operandi tracks outcomes and flags when results diverge from policy intent — then updates the model.
Built for AI-forward teams shipping agents in production.
Operandi is not general-purpose document search. It is infrastructure for companies that are already betting on agent autonomy and need the underlying knowledge layer to be reliable, up-to-date, and agent-readable.
20–200 person company actively deploying AI agents
Engineering leader (CTO, VP Eng, Head of AI) who owns agent reliability
Knowledge fragmented across Slack, Notion, Linear, GitHub, and calls
Agents in production with excessive human review or visible failure modes
Teams that cannot afford to build and maintain a custom knowledge layer
Legibility enables autonomy.
We are working with a small group of AI-forward startups as design partners. If your team is deploying agents in production and running into the knowledge problem, we would like to hear from you.
No pitch deck. We will reach out directly if there is a strong fit.