R&D capitalization & board reporting
A patented allocation model built specifically for CFO-facing R&D capitalization is an earned strength.
Tetriz vs Jellyfish
Jellyfish reports AI spend. Tetriz reports the session behind it.
Jellyfish's AI Engineering Trends research, done in collaboration with Harvard economists, makes a genuinely reassuring industry-level case: AI adoption is lifting pull-request throughput without an aggregate hit to code quality. Tetriz answers a different, narrower question that aggregate research isn't built to: inside one specific org, which team or session is driving its own outcomes.
Aggregate research and one org's own outcomes answer different questions
Jellyfish's category-defining strength is engineering-to-finance translation: R&D capitalization and board reporting, earned over multiple consecutive quarters as a G2 Leader. Its AI Engineering Trends study, run with Harvard researchers across hundreds of companies, is a credible industry benchmark, but it's an aggregate, cross-company average. It can't say which team or session is driving a given organization's own numbers, or whether that org's harness and coaching loop are actually pulling their weight. The Tetriz Desktop App's data is built to operate at that resolution, org by org: the specific AI session behind a specific outcome, and whether it's actually moving that org toward AI-native.
Where Tetriz wins vs Jellyfish. Session-level data, mapped to the pull request it produced.
| Capability | Tetriz | Jellyfish |
|---|---|---|
| Prompt-to-PR attribution | Session-level, via direct IDE capture joined to pull-request outcomes through a four-signal cascade. | AI Impact module reports which tools are used and acceptance rates, with no prompt-level visibility. |
| Speed to first value | Every engineer gets a personal coaching view from day one, independent of any team or org rollup. | Onboarding is a guided implementation process built around org-wide reporting from the start. |
| Harness setup visibility | Inventories installed skills, sub-agents, and hooks per engineer, and flags silent conflicts. | AI Impact tracks which tools are used, not how they're configured or where configurations conflict. |
| 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. | AI Impact's individual-level breakdowns are usage metrics (tool adoption, acceptance rate) reported to managers, with no coaching signal returned to the engineer. |
Where Jellyfish wins. Said plainly, credit where it’s due.
Industry benchmark research
A large-scale study backed by a Harvard research partnership is a real and citable industry data point. Tetriz's session data explains the specific instance; Jellyfish's research establishes the industry-wide pattern.
Questions teams ask. Comparing Tetriz and Jellyfish.
It shows a useful industry-level signal: top-quartile AI adopters ship roughly twice the pull-request throughput without an apparent aggregate quality hit. That finding is a cross-company average, so it can't say which team or session inside one specific organization is driving that organization's own numbers. Tetriz's session data is built to answer exactly that, org by org.
Not usually. Jellyfish's R&D capitalization and board-reporting layer is built for a different, earned job. Tetriz adds the session-quality layer underneath it, and the two are commonly run together.