Most engineering leaders discover AI is bleeding cost or creating risk only after it hurts. You didn't wait. That matters.
One platform to track cost, enforce compliance, and measure productivity — across every AI tool, every team, every developer.
Your teams use a mix of AI tools. Each has its own billing, its own dashboard, and its own blind spots.
Four crises happening silently inside AI-enabled engineering orgs. Scroll through — most teams are living in at least three right now.
One tool. Growing fast. Monthly bill tripled. Nobody saw it coming until finance called.
CFO asks why AI bill tripled. Engineering has no data to answer.
Routine debugging session. Developer attaches a config file to an AI prompt. Live AWS keys, DB passwords, auth tokens — all sent to an external API. Security caught it during a log review. Not in real time. Weeks later.
You found it by accident. The next one you won't.
Six months of Cursor, Claude Code, and Copilot running across the org. Developers say they use it daily. Finance is asking for impact data. Engineering leadership is scrambling — there's no single place to pull the numbers from.
Your competitors are making data-driven AI investment calls. You're still going on gut feel.
No deploy happened. No one was online. Three AI agent tasks ran overnight across two developer machines. Any one of them could have caused it. There's no prompt log, no command trail, no way to isolate which agent, which task, or which instruction led to the drop.
When an AI causes the incident, "I don't know" is not an acceptable root cause.
AI strategy built on gut instinct. Compliance incidents become crises. Board conversations happen without data. Consolidation decisions delayed indefinitely.
Board loses confidence in AI transformation. A secrets-leakage incident reaches the press before the CISO even knew it happened.
AI becomes an uncontrolled budget line. The CFO sees a growing invoice with no way to attribute, optimize, or chargeback. Waste goes undetected for months.
Failed compliance audit. Secrets exposure with no paper trail. An AI agent executes a destructive command and nobody can reconstruct what happened.
Responsible for AI adoption but has no metrics to act on. Can't tell whether $20K/month in AI is producing $200K in productivity — or $2K.
When the product feels like surveillance, developers work around it. Those who keep using AI do so without guardrails — creating the compliance incidents everyone feared.
AI strategy built on gut instinct. Compliance incidents become crises. Board conversations happen without data. Tool consolidation decisions delayed because there's no cross-tool comparison.
Board loses confidence in AI transformation. A secrets-leakage incident reaches the press before the CISO even knew it happened.
AI becomes an uncontrolled budget line. The CFO sees a growing invoice with no way to attribute, optimize, or chargeback. Waste goes undetected.
Failed compliance audit. Secrets exposure with no paper trail. An AI agent executes a destructive command on production and nobody can reconstruct what happened.
Responsible for driving AI adoption but has no metrics to act on. Can't tell whether $20K/month in AI is producing $200K in productivity — or $2K.
When the product feels like surveillance, developers work around it. The ones who keep using AI do so without guardrails — creating the compliance incidents everyone feared.
AI Dev Sentinel unifies cost intelligence, compliance monitoring, and productivity tracking — across every AI tool your teams use.
Dashboards, drill-downs, real-time metrics
Role-scoped notifications & anomalies
Set policies, budgets, compliance rules
Investigate, remediate, export reports
Ask questions in plain English
10 priority-one capabilities — each mapped to a specific pain point, pillar, and set of roles.
We capture who, what, and why at the device level — before the API key is ever involved. Cost, compliance, and productivity traced to the exact person, not a shared key.
Most observability happens at the network layer — after the key is used, identity is already lost. We instrument at the source: the developer's machine. That single architectural decision unlocks everything else.
Full identity and context captured at step 2 — so every insight downstream is developer-level precise.
One consistent hierarchy across all three pillars — whether you use one AI tool or eight, vendor-hosted or self-hosted, per-seat or token-based.
No code changes. No pipeline modifications. No six-month integration projects. Just connect, deploy, and see.
API key integration for Cursor, Claude Code, Cline, Copilot, and Windsurf. Cloud connectors for AWS Bedrock and Azure OpenAI. Five minutes per tool.
Lightweight agent on developer machines. Captures identity and telemetry at the source — before the shared API key is even used. Zero performance impact.
Cost, compliance, and productivity data flows in within hours. Role-scoped dashboards activate automatically. Your CXO, Eng Lead, and team leads all see their view.
Your developers are already spending company money on AI tools every single day. The only question is: do you know where it's going, what risk it's creating, and whether it's actually working?