One platform that tests before you ship, monitors what’s live, and enforces policy in real time. Audit-ready evidence comes standard.
See what full control looks like
| Priority | Test name | Result | Threshold | Past results | Created by |
|---|---|---|---|---|---|
Med | Contains PAN or PIISecurity | Passed0 | Row count <= 0 | VNVikas Nair | |
Med | Has prompt injectionSecurity | Passed0 | Row count <= 0 | RRRishab Ramanathan | |
Med | Mean latency (ms) < 5000 msPerformance | Passed812 | Mean latency <= 5000 | GRGuilherme Rodz | |
Low | At least 10 test scenariosCompliance | Failed7 | Row count >= 10 | RRRishab Ramanathan | |
Med | Context utilization > 0.5LLM-as-a-judge | Passed0.64 | Context utilization >= 0.5 | GRGuilherme Rodz | |
High | Answer relevancy > 0.8LLM-as-a-judge | Passed0.95 | Answer relevancy >= 0.8 | VNVikas Nair | |
High | Hallucination score < 0.2LLM-as-a-judge | Passed0.10 | Hallucination <= 0.2 | VNVikas Nair | |
High | Groundedness score > 0.8LLM-as-a-judge | Passed0.86 | Mean judge score >= 0.8 | RRRishab Ramanathan |
Your controls are notkeeping up.
Copilots, agents, and models are going to production across every business unit. Most companies can’t list them, few can test them, and almost none can prove to a regulator what any of them did last Tuesday.
88%
of AI agents built never make it to production due to governance and eval gaps
(source: S&P Global, 2026)
79%
of enterprises do not have a governance model for the AI agents they are already running
(source: McKinsey, 2026)
$207M
is the average projected AI spend per US enterprise over the next 12 months
(source: KPMG, Q1 2026)
Each layer delivers value on its own. Together, they turn scattered AI into systems you can govern, observe, and prove are under control.
Know what
AI you have
Know every AI system running, org-wide inventory and automatic shadow-AI detection.
You can’t govern what you can’t see, so this is where control begins.
Catalog and
assess risk
Turn a raw inventory into an accountable registry. Register each system with its owner, purpose, data sensitivity, and risk tier using intake and risk-assessment forms you build to fit your policy.
Catch problems
before they ship
Build evaluation suites and run them pre- and post-deployment against your own data. Benchmark every change and block regressions in CI, so quality is proven rather than hoped for.
See what your AI
does in production
Trace every input and output. Monitor quality, cost, latency, and drift on live traffic, and get alerted the moment behavior changes — across apps you build and tools you buy.
Protect AI in
real time
Protect AI in real time, blocks prompt injection, jailbreaks, PII and PHI leakage, and off-policy outputs before they reach a user, enforces access and routing policy at the gateway so every request is governed, not just watched.
Map frameworks and
prove compliance
Map everything to the frameworks you answer to: EU AI Act, NIST AI RMF, ISO 42001, and your internal policy. Compliance isn’t a separate workstream, it’s powered by every layer beneath it.
Know and
control spend
Know and control spend, token spend by project, team, and user, hard budget caps per API key or team, usage dashboards for finance and leadership, ROI visibility that ties spend to outcomes.
6x
Faster from development to deployment
90%
Reduction in audit preparation lift
53%
Higher throughput with Openlayer
100%
Client renewal rate
Every major LLM provider. SDKs, CLI, REST API, Git, OpenTelemetry, and Snowflake. Vendor AI, homegrown agents, and classic ML, governed the same way.
Capability
Code you own
Traffic you can route
Connected vendor AI
Opaque vendor AI
Discover
Registration
Testing
Observation
Security & Guardrails
Compliance
See one AI system tested, monitored, and governed from end to end.
