Setup speed (original detection layer)
Metadata-only, zero-clone setup that connects live during a discovery call is a real advantage for teams that only need an AI-code-ratio number quickly.
Tetriz vs Span
Span detects AI-written code. Whether it also captures the prompt is worth verifying directly.
Span's original AI-code detector is a genuinely strong, code-pattern-based classifier. Its newer Agent Traces feature describes capturing prompts and reasoning steps directly from Cursor and Claude Code. Tetriz couldn't find an installable IDE tool, extension, or local agent described in Span's public documentation, so it's worth asking to see one. The Tetriz Desktop App, installed on the engineer's machine, reads the session directly, so its own capture mechanism is disclosed by design.
A capability claim worth verifying
Span's original detector is a genuinely accurate classifier of AI-authored code, built entirely from code patterns in merged pull requests. Its newer Agent Traces feature describes something further: interaction history inside tools like Cursor and Claude Code, including prompts and reasoning steps, though Tetriz couldn't find an installable tool documented publicly that would explain how that capture works. A capture mechanism alone doesn't make an org AI-native, though. It takes a session worth capturing and a harness that's actually helping, both visible enough for the engineer to keep sharpening them. The Tetriz Desktop App, installed on the engineer's machine, is built to surface and coach on both.
Where Tetriz wins vs Span. Session-level data, mapped to the pull request it produced.
| Capability | Tetriz | Span |
|---|---|---|
| Prompt-to-PR attribution | A four-signal cascade links the session directly to the pull request it produced, with confidence scores visible in-app. | The original detector infers AI involvement from code patterns in the merged diff, a later and coarser signal than the session itself. |
| Weekly coaching loop | A weekly Session Quality Score, scored across six prompt-quality dimensions, returned directly to the engineer who ran the session so the next one improves faster. | The AI-code ratio and the newer Effectiveness suite report at the team or code level; neither documents an individual coaching signal returned to the engineer who wrote the prompt. |
| Session quality dimensions | Six decomposed, coachable dimensions: clarity, specificity, context, actionability, completeness, efficiency. | No decomposed quality dimensions are documented publicly, in either the original detector or the newer Effectiveness suite. |
| Harness setup visibility | Inventories installed skills, sub-agents, and hooks per engineer, and flags silent configuration conflicts. | No public documentation of IDE-side AI-configuration inventory, in either the original detector or the Agent Traces feature. |
Where Span wins. Said plainly, credit where it’s due.
Security-review speed
Read-only API access with no repository cloning clears CISO (security) review unusually fast for the original detection layer. That's a genuine advantage for security-cautious buyers moving quickly.
Questions teams ask. Comparing Tetriz and Span.
That's worth testing directly rather than assuming either way. Span describes Agent Traces as capturing prompts, tool calls, and reasoning steps, but Tetriz couldn't find an installable IDE tool, extension, or local agent described in Span's public documentation explaining how. Ask to see an Agent Trace record with prompt text in a demo. The Tetriz Desktop App is a local install, so its own capture mechanism is disclosed by design.
Span's own published accuracy figures for its original detector are strong, and it's a genuinely useful classifier for AI-versus-human code ratios. It's built from code patterns in merged pull requests, which is a different and later signal than the AI coding session itself.