AI evaluation and governance, on one platform.
Discover every AI tool and agent. Measure adoption depth and ROI. Evaluate runtime behavior continuously. Produce framework-mapped audit evidence. One pipeline.
See it. Measure it. Evaluate it. Prove it.
Discover every AI tool, embedded feature, and internal agent running in your organization.
Most enterprises find 150–300+ AI surfaces when they actually look: vendor SaaS, embedded AI features, internal agents, and unapproved usage. We map all four through endpoint detection, network analysis, SDK instrumentation, and vendor partnerships. One consolidated inventory by the end of week one.
Beyond login counts. See depth and ROI.
Login counts don’t survive a CFO conversation. We measure usage depth, workflow integration, and outcome linkage where the data permits. PR velocity, deflection rates, time-to-resolution. Spend intelligence is built in, and most customers find material license waste in the first quarter.
Runtime behavior. Not synthetic benchmarks.
AI is non-deterministic. A model that passes every test today can fail tomorrow on the same inputs. We evaluate behavior continuously in production: groundedness, policy, tool authorization, PII leakage, and drift, against a per-use-case baseline we help you set. Most deployments catch the issue before a customer complaint.
One evals. Multiple frameworks.
Framework compliance is a mapping problem. NIST AI RMF, ISO 42001, EU AI Act, and Singapore agentic AI guidelines each identifies categories of risk but doesn’t set thresholds for your use case. We emit versioned, timestamped, source-anchored evidence continuously. Audit packs export with one click against the framework your auditor cares about.
One pipeline. Two outputs.
The live operational view. The audit-grade evidence. Same trace data.
The architecture, in one picture.
Same stack for a 50-person pilot and a 50,000-employee rollout. The layers above compose (framework, policy, executive); the layers below stay stable (data, traces). Layer 4 handles frameworks, regulations, and guidelines.
The 12 levers every finance AI team has to pull.
Every framework, consultant, and competitor has their own model. We published ours because the ones in market are either too narrow (governance only) or too broad (transformation theater). The 12 Levers is the CIO’s reference guide: one page, every lever, mapped to who owns it.
Frameworks tell you what to track. They don’t tell you what “good enough” looks like.
Every major framework, including NIST AI RMF, ISO 42001, Singapore’s agentic AI guidelines, and the EU AI Act, identifies categories of risk: bias, hallucination, data leakage, safety. None of them define acceptable thresholds for a specific implementation.
A bias metric of 0.12: is that compliant? What about 0.15? The answer depends on the use case, the population, and the risk appetite of the organization. That judgment call is where evaluation actually happens.
Assurance requires baselines. Baselines require continuous measurement. That’s why the platform is built the way it is.
Why continuous beats comprehensive.
Point-in-time certification was built for deterministic systems. AI isn’t deterministic. A quarterly attestation means up to 90 days of unmeasured behavior between audits. Customers notice first.
Continuous evaluation runs with the system. Evidence is always fresh. An auditor asks a question on a Tuesday; you answer on Tuesday.
“A chatbot handling 200,000+ interactions per week cannot be assured through quarterly reviews or screenshot evidence.”
Works with the stack you already have.
TrustEvals is stack-agnostic. We integrate with the data and observability layer your environment already runs on (Snowflake, Databricks, ClickHouse, DuckDB, Postgres, Supabase, your ETL/ELT, dbt, Cube.dev) and with the operational systems your AI is actually used inside (CRMs, ERPs, customer-success platforms, helpdesk, knowledge, identity, code hosting, and a long tail of others). The integration is implied; we don’t enumerate every logo. If your stack isn’t supported, ask. We’ve added five new integrations in 2026 already.
Platform plus services. By design.
TrustEvals is a platform first. We deploy in one day and produce a discovery picture in week one. That is the default path.
Where customers ask for practitioner depth, we run engagement packages. Most start with the AI Audit (two weeks), the engagement we first shipped to a cybersecurity and compliance services firm and now run as our default Day-1 offering. From there: AI Transformation engagements (the PE-backed mid-market shape: full adoption + vendor eval + governance foundation), Evals (for AI product companies: eval pipelines, red teaming, optimizer), and Remediation Advisory (incident-driven).
We are not a dev shop. We don’t sell engineers by the hour. Every engagement transfers methodology. The platform is the backbone, practitioners are how it gets applied inside a customer’s environment.
See engagements →What a platform lead asks us first.
The SDK is asynchronous and batched. Traces flow out-of-band through the Ingest Gateway to the Eval Engine. Production agents see sub-millisecond overhead per call; evaluation happens off the hot path.
They aren't. Customer traces are single-tenant by architecture, not by policy. Our evaluation models run on your tenant; the platform does not train on your data. This is a property of the build, not a promise in the MSA.
Layer 4 is a mapping, not a monolith. When a new framework ships (or your compliance team writes an internal one), we add a mapping layer on top of the same Layer 1–3 infrastructure. Customers using TrustEvals for ISO 42001 in 2026 will use it for the next five frameworks without replumbing.
Book the AI Audit.
Thirty minutes to size the discovery surface: employees, devices, SaaS admin access, developer tooling, internal agents, Shadow AI exposure, and the outcome read you need at the end.