As organizations deploy more models and autonomous agents, the missing piece isn't another chatbot — it's the control layer underneath. We build the runtime that sits between your AI applications and the models they call: authentication, policy, risk, observability and compliance, governed by a knowledge graph of who can reach what.
Today's tools screen text — a regex or an LLM judge decides "allowed" or "blocked" in isolation. That misses the real question: what can this request actually reach? Governance has to understand context — the person, the department, the agent, the systems and the data behind a request — not just the words in it.
Prompt → keyword / LLM judge → allowed or blocked. Fast to ship, blind to blast radius. It can't tell you whether an approved request can quietly reach payroll, send email, or move money.
Every request is evaluated against a live graph of users, agents, models, tools, APIs and data assets — so policy decisions are based on reach and permission, not just the text of a prompt.
Your teams build the AI applications. We provide the runtime they all sit on — one place to authenticate, authorize, govern, observe and audit every AI interaction, whatever model is underneath.
We model the whole AI operation as a graph — users, sessions, agents, policies, models, tools, APIs and data assets. Every request is a path through it, so we can answer questions no prompt filter can: can this agent reach PII? what's the blast radius if this model is compromised?
Each is a lens on the same underlying model of your AI estate — which is what lets them work together instead of as disconnected point tools.
Understand a request in context — who sent it, from which department, through which agent, to which system — instead of judging text in isolation.
Borrowing attack-path analysis from cybersecurity: can this prompt reach payroll? can this agent send email or invoke a payment API? Map the paths before an incident does.
Move beyond user RBAC to Agent RBAC/ABAC. Fine-grained rules per agent — read invoices, draft reimbursements, but never approve payments or touch HR records.
Model your AI estate — apps, models, agents, tools, data, approvers — then simulate: "if this model is compromised, what's exposed?" Risk analysis before deployment, not after.
Not one number — scores across security, privacy, compliance, financial, operational and reputation risk, each with a plain-language reason and recommendation.
Business-readable policies, not code buried in a service: IF department = Finance AND model = external AND prompt contains account number THEN require human approval.
Score every AI application on security, explainability, compliance, reliability, cost and governance — so executives can compare AI systems across the organization on one scale.
Version-controlled, testable governance policies that live in Git and run in CI/CD — so controls are reviewed, diffed and deployed like any other critical code.
# Require human approval when finance data may leave the org policy: finance-external-approval when: department: Finance model: external prompt_contains: [ account_number, iban, card_number ] then: action: require_human_approval approver_role: finance_controller risk: { privacy: high, financial: high } audit: true
Illustrative policy — the point is that governance is explicit, reviewable and testable, not hidden inside application code.
All of it built on public, well-understood techniques — graph databases, attack-path analysis, policy engines, ABAC — combined in a way that's designed and implemented independently, from the ground up.
That's the gap we build for. Let's scope a pilot — inventory your AI estate, map the graph, and put a governance layer in front of it.
