Tracemark sits in the execution path of your AI systems — not alongside them. We intercept, enforce, prove, and remediate so your AI behaves safely, compliantly, and in line with business intent.
AI models make high-stakes decisions across finance, HR, customer service, and supply chain. Yet teams have no runtime visibility, no enforcement, and no ability to undo what goes wrong.
Teams can't see which models acted on which data, or why decisions were made. Activity is scattered across vendor dashboards that capture fragments, never the full picture.
Governance today means spreadsheets and audits after the fact. Nothing enforces policy at the moment an AI system acts. Violations are discovered, not prevented.
The EU AI Act mandates traceability, transparency, and control for high-risk AI. Most enterprises have no mechanism to comply — not at runtime, not at scale.
When an AI system makes a wrong decision, there is no rollback. No undo. No scoped correction. Teams scramble with manual workarounds while damage compounds.
Infrastructure that observes, evaluates, governs, and when necessary remediates AI behaviour in real time.
Sits in the execution path of every AI call. Vendor-agnostic. Sees every input, output, and decision before it reaches production.
Evaluates every AI action against policy rules in real time. Blocks non-compliant outputs before they execute. Not after — before.
Tamper-proof record for every decision: who asked, what model responded, what policy applied. Regulator-ready.
The only governance layer that doesn't just detect problems — it fixes them. Scoped, policy-driven rollback with auditable compensating actions.
Your existing workflows continue as normal. Models, agents, and orchestration layers operate without modification.
Lightweight connectors observe every meaningful AI interaction — inputs, outputs, and decisions — as they happen.
Every action is evaluated against governance rules in real time. Non-compliant outputs are blocked or escalated.
Full provenance recorded: who asked, which model, what policy applied, what happened. Tamper-proof.
When violations occur, Tracemark executes scoped, policy-driven compensating actions — auditable and governed.
Databases have ACID transactions. Networks have firewalls. Deployments have rollback. AI has nothing. A deep examination of the single largest unaddressed risk in enterprise AI today — and why this is an infrastructure problem, not a policy one.
Read the white paperThe shift from AI-as-tool to AI-as-actor introduces action risk, chain risk, and reversal risk. What enterprises need to understand now.
Read the white paperHigh-risk system requirements, transparency obligations, and the enforcement framework — the regulation assumes you have governance infrastructure.
Read the analysisFrom rogue pricing agents to audit requests you can't answer — five concrete scenarios to pressure-test your AI readiness.
Read the articleWhy naive rollback doesn't work, what compensating actions look like in practice, and why remediation must itself be governed.
Read the articleWe're not looking for customers. Not yet. We're looking for partners — enterprises living this problem today who want to shape the infrastructure that solves it.
Early access to the platform before general availability
Direct engineering line — shape the product with our team
Preferential terms and priority onboarding when we launch
A voice in shaping a new category of enterprise infrastructure
We've spent decades in enterprise technology — deploying platforms, managing infrastructure, working alongside CIOs and CTOs at organisations that build things that matter.
When AI moved from experimentation to production, we watched the same organisations that would never deploy a database without transactions or a network without a firewall deploy AI systems with no governance, no control layer, and no ability to undo what goes wrong. Not because they didn't care — because the infrastructure didn't exist.
That's why we started Tracemark. Not to build another compliance dashboard or monitoring tool, but to build the infrastructure that should have existed from the beginning — a governance, control, and remediation layer that sits in the execution path and gives enterprises the ability to trust what their AI actually does.
We're building this for the CIO who can't sleep because an AI agent has access to production systems. For the compliance team preparing for EU AI Act enforcement with nothing but spreadsheets. For the platform engineer who knows that "we'll deal with governance later" always becomes "we should have dealt with governance sooner."
We're looking for like-minded people. If you believe enterprise AI needs real infrastructure — not just tooling, but infrastructure — we'd love to hear from you.