Navigara vs Faros AI: two approaches to engineering intelligence

Faros AI built its 2026 platform around AI cost. Its token intelligence product describes a “Token Attribution Ledger tying every token to the work, team, and outcome”; the headline metric is “cost per verified engineering outcome” and the company claims “over 60 engineering data sources.” Navigara comes at the same question from the repository: score what shipped, compare it with the same team’s pre-AI year, then set spend against the change. Of the platforms in this comparison series, these two are the closest on intent and the furthest apart on architecture.

What Faros AI measures

Faros AI is integration-led. Source control, CI/CD, ticketing and incident management feed a model of the delivery lifecycle that Faros calls its Engineering World Model.

Token intelligence attributes spend by team and model, too, and tracks it against budgets set for each team. Cost per verified engineering outcome then divides that spend by work the platform treats as verified.

The word doing the work in that phrase is “verified.” How an outcome qualifies determines the entire metric, and that definition is what to interrogate in a demo. Ask what counts, ask what happens to work that ships without a linked ticket, and ask how the figure behaves in a quarter of heavy maintenance.

Two gaps are worth carrying into the evaluation. Faros AI describes “a consumption-based pricing model” and states that “Specific pricing details are not publicly documented,” so expect a discovery call before a number. And the faros.ai/software-capitalization URL now shows the token-platform homepage, with no capitalization page in the current navigation. If capitalization is part of your requirements, confirm its status in writing.

On individual reporting, Faros AI’s public pages frame accountability at the team level and state that there is no policy either way. The policy in practice is whatever your admin configures.

What Navigara measures

Navigara reads the repository and scores each merged change.

Engineering Throughput Value (ETV) sorts each merged commit into one of five categories (features, maintenance, tests, docs, and fixes) and sums them with equal weights. Three lines of code can be any of those.

The point of comparison is your own history. Navigara “reads back through years of commits” and reports the current quarter against the pre-AI year. The Pro tier then reports “AI ROI: capacity added vs. spend,” which is the same ratio Faros AI is building, assembled from a different numerator.

Navigara’s Q2 2026 Engineering Performance Report shows what that looks like in the public domain. In the open cohort of 699 qualifying engineers (793 a year earlier), performance per engineer is up 130% year over year. In the fixed panel of 388 engineers active throughout, it is up 82%. Quarter over quarter, the open cohort is up 17.5%, with a 95% confidence interval of -0.7% to +38.8%, and the report says so directly: “The quarter-over-quarter change cannot be distinguished from zero.” The sample is 137,592 qualifying commits across 65 public repositories over 78 weeks.

The report is equally direct about what it does not claim: “The study does not quantify what share of the level shift, if any, is attributable to that adoption, and a single quarter of deceleration in a noisy series is not yet evidence of a plateau.”

Publishing the interval lets a reader see how much of one quarter is noise.

Side by side

Where the two overlap

Both are trying to determine whether AI tooling spend is yielding any return. Both attribute cost. Both report above the individual in practice.

Cost attribution alone no longer separates vendors in this category. DX, LinearB, and Swarmia all publish token or cost attribution too. The divergence is the other side of the ratio, and it determines the setup cost. A 60-source model can do more once it is wired and depends on every one of those sources staying wired. A repository-only model is narrower and has one dependency.

If you want the baseline half of that ratio first, connect a repository, and Navigara reads back to your pre-AI year.

Which one fits your org

Faros AI fits a large org with a mature toolchain, a platform team that can own connectors, and an appetite for an enterprise cycle. If token spend across many tools and many teams is the problem you are funding, Faros AI is the vendor that built its whole platform around it.

Navigara fits an org between roughly 20 and 100 engineers that needs a defensible throughput figure this quarter, with repository access as the only integration. Pricing is on the website, so a 40-engineer org can budget it this afternoon.

What neither one sees

Navigara scores “only merged commits on connected repositories,” so code review, incident response, mentorship, and planning sit outside the score. Cost per verified engineering outcome inherits a version of a related problem: a verified outcome can still be an outcome nobody needed.

Talk to us if you want the baseline comparison run against your own history.