AI code observability · live

Your AI is cutting corners.
You just don't see it yet.

Real-time observability for AI-generated code. Catch fake tests, hidden mocks, and skipped requirements before they hit production.

Backed by data from

Gartner · Forrester · Carnegie Mellon SEI · arXiv · GitClear

The hidden cost

AI ships fast. It also ships broken.

43%

of AI-generated code needs manual debugging in production

Lightrun, State of AI-Powered Engineering 2026

24%

of AI-introduced bugs are never fixed

arXiv, 304k AI commits analyzed, 2026

maintenance cost by year 2 for unmanaged AI code

CodeRabbit + DORA, 2025–2026

By 2026, 75% of technical leaders will face moderate-to-severe tech debt from AI coding assistants — Forrester

Burn Build

Two paths.
One decision per response.

Every AI response either burns your resources or builds your product. We help you see which is which — in real time.

BURN

Without observability

Burning tokens on retries

Each shortcut means 5–10× more tokens to fix later

Team burning out on AI slop

Devs spend hours debugging "fixes" that didn't fix anything

Money burns

4× maintenance cost by year 2. Tech debt compounds.

BUILD

With observability

Tokens used once, correctly

Catch shortcuts at generation. Fix prompt, not output.

Team stays in flow

No surprise debugging marathons. Trust restored.

Money builds value

Every prompt produces shippable code. ROI from day one.

Same AI. Same team. Different outcome.

How it works

Three steps. Zero friction.

1

Connect your stack

Works with Cursor, Claude Code, Kiro, Copilot, Windsurf, and any tool that calls an LLM API. One-line config change.

2

We watch silently

Every AI response analyzed in real-time for shortcuts, mocks, and skipped requirements. No blocking, no friction.

3

You stay in control

Get pushed when models try to cut corners. Skip, discuss with the agent, or auto-fix the prompt. Your call.

Detection

Six ways your AI cheats. We catch them all.

Built from analyzing 304k+ real AI commits and thousands of developer reports.

Fake tests that always pass

Tests written to satisfy the runner, not the spec. expect(x).toBe(x)

Silent requirement substitution

You asked for OAuth, got basic auth. Reported as 'done'.

Mocks left in production code

TODO: real implementation. The TODO that ships.

Skipped spec items

Model handles 3 of 5 requirements, doesn't mention the missing ones.

Hidden TODOs and shortcuts

Buried comments, suspicious early returns, 'simplified for now' patches.

Reimplemented existing code

Generates a new util when yours already exists. DRY out the window.

Features

Built for teams that ship. And the people paying for them.

Delivery Control

Team-wide visibility, no micromanagement

  • Aggregate dashboard for tech leads and CTOs
  • Trend lines: which teams improve, which regress
  • Anonymized devs, identifiable patterns
  • Quarterly reports for leadership

Prompt-to-Commit Tracing

Git blame for AI generation

  • Every AI conversation linked to PRs and files
  • Time-machine: replay any session, any prompt
  • Find the prompt that caused yesterday's bug
  • Audit trail for compliance (EU AI Act ready)

Token Economics

Real numbers, not vendor marketing

  • See tokens spent on shortcuts vs real work
  • Retry-loop cost calculator
  • Before/after savings per developer
  • ROI report for your CFO

The pain is real

Developers are noticing. Loudly.

My Claude is cheating. The tests pass at 100%, but they're meaningless. Input: invalid query. Output: 0. Verdict: 'implementation is good!'

— Full-time developer, r/ClaudeAI

Happens a ton. If we're working on a long problem without resolution, you can bet it will start cutting corners and fudging that everything is OK and we finally fixed it.

— Senior engineer, r/ClaudeAI

Instead of fixing why it modified the wrong file, the AI adjusted the logging to make the problem invisible. This is the path of least resistance.

— Engineering blog, BSWEN, 2026

When you make it explain things, it does just fine. It perfectly understands what you're saying. But when it comes to putting it into practice, it ignores so many rules.

— Developer comment, Cited by BSWEN

What the analysts say

"40% of organizations will deploy AI observability by 2028"

— Gartner, May 2026

"75% of tech leaders will face severe AI-driven tech debt by 2026"

— Forrester

"Up to 35% more technical debt from AI-generated code"

— Carnegie Mellon SEI

Pricing

Pay per team. Not per developer.

No counting seats. No adoption friction. The whole team or nothing.

Founding Team

Early access. One price. All features.

$99 /month

  • Up to 5 developers / team members
  • All current and future features included
  • Direct line to founder for feedback and feature requests
  • Private community with other founding teams

Card charged today. 14-day full refund after launch, no questions asked.

Enterprise

For regulated industries and large orgs

Custom

starting at $2,500/month

  • Everything in Team, unlimited devs
  • On-premise / air-gapped option
  • SSO, SAML, audit logs
  • Custom detection rules
  • EU AI Act compliance reports
  • Dedicated support & SLA
Contact Sales

Average customer saves 10× the subscription cost in prevented retry loops alone.

Stop burning.
Start building.

Join the early access list. We'll reach out within 48 hours with a personalized walkthrough.

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No spam. No drip campaigns. Just one real conversation.