The Enterprise AI Operations and Governance Platform
Tracelet is the control plane for governed enterprise AI usage. From a developer’s IDE to a marketer’s browser session, every AI interaction is observed, evaluated against policy, and turned into evidence — without disrupting the workflows your teams depend on.
The platform is built around six pillars. Each pillar serves both engineering teams — coding assistants, agents, MCP, source code — and business teams — browser AI, HR, finance, legal, sales — through the same control plane.
You Cannot Govern AI Usage You Cannot See.
Tracelet gives organisations a single view of how AI is actually being used. Across tools, models, users, teams, departments, agents, browsers, MCP servers, prompts, commands, files, and workflows — every interaction is observable and attributable.
See how shadow AI discovery works“You cannot govern what you cannot see.”
— Faros AI Engineering Report, 2026
See which AI coding assistants are active across developer machines, which MCP servers are configured, and which prompts and tool calls are being executed in real time.
See which browser AI tools are in use across HR, finance, legal, sales, and marketing — including unsanctioned tools that traditional DLP would never catch.
Turn AI Usage Policies Into Enforceable Controls.
The Tracelet policy engine defines and enforces AI usage rules across the organisation. Policies operate at the user, team, department, role, project, repository, tool, model, agent, MCP server, file path, data category, prompt, command, and workflow level.
See compliance bundlesWith 60% of AI-generated code now accepted into codebases, governance has to operate where the code is authored — at the agent, the IDE, and the MCP call — not bolted on at the review stage.
Apply rules like "The frontend team can only run AWS read-only commands. The platform team can run AWS write commands in dev and staging. Production write operations require approval."
Apply rules like "The HR team must not upload employee performance reviews to public AI tools." or "The legal team must not upload confidential contracts to unapproved AI services."
Stop Sensitive Data from Reaching the Wrong AI.
Tracelet detects sensitive content before it leaves the endpoint, identifies risky agent and tool behaviour, and surfaces unmanaged AI assets across the environment.
Detect when a developer pastes an .env file into a prompt, a private key into a tool call, or proprietary source into a public model. Block destructive shell commands and risky agent actions before they execute.
Detect when a finance employee uploads a sensitive forecast spreadsheet to a browser AI tool, or when an HR user submits performance review data to an unapproved model. Warn or block at the point of submission.
Govern AI-Assisted Work at the Point of Authorship.
Tracelet helps organisations distribute approved AI skills, prompts, standards, and quality controls across teams — shaping what AI produces in the first place, rather than catching problems downstream in review or production.
Industry data shows the quality gap from AI-assisted code cannot be closed by adding more reviewers — it has to be closed at the point AI generates the code. Tracelet is that intervention point.
Distribute secure coding standards, unit and integration test generation, API documentation rules, architecture decision records, Terraform review skills, and pull request review workflows. Enforce that every AI-assisted backend change includes tests and documentation updates.
Distribute approved prompt templates for HR policy drafting, sales proposal generation, legal review guidance, finance report summarisation, marketing brand voice, customer support responses, and operations SOPs. Require human review before publication where it matters.
Understand Whether AI Usage Is Creating Value.
Tracelet measures AI consumption and productivity so leadership can answer questions about cost, efficiency, and impact — not just risk.
Compare which models handle which tasks effectively. Identify when expensive models are being used where cheaper ones would suffice. Surface failed agent loops and repeated prompts that waste tokens. Track code churn, work restarts, and AI contribution survival.
Understand which paid AI subscriptions are actually being used, by which departments, for which workflows. Attribute AI cost to projects and outcomes.
Make AI Governance Auditable.
Every policy evaluation, every block event, every configuration change is captured as structured, queryable evidence. Audit packages can be assembled in minutes rather than weeks.
Full compliance documentationGenerate audit-ready evidence for AI coding assistant usage, MCP governance, secret protection, and engineering process compliance.
Generate department-level reports showing approved AI tools, blocked submissions, sensitive data events, and policy posture across HR, finance, legal, sales, and operations.
One Platform. Two Deployment Paths.
For Engineering Teams
Tracelet Engineering covers AI coding assistants, IDE-based AI tools, terminal AI agents, MCP servers, local agents, plugins, skills, project rules, source repositories, cloud and infrastructure tools, and documentation and testing workflows.
See deployment for engineering teamsFor Business Teams
Tracelet Enterprise covers ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity, SaaS AI assistants, browser-based AI tools, internal AI agents, and department-specific AI workflows.
See deployment for business teamsSee the Platform
in Action.
Request a demo tailored to your industry, compliance posture, and AI tool inventory.