Are Your AI Coding Tools Actually Making Developers More Productive?

01 /The Concept

Most engineering leaders can tell you how many AI coding tools their teams are paying for. Far fewer can tell you what those tools are actually doing to developer output. Bilanc is built for that second question.

Setup involves integrating with Cursor Business or GitHub Copilot. From there, the platform surfaces data on what share of developers are using AI tools, how their output has shifted as a result, and – crucially – which AI models deliver the biggest gains on which types of tasks.

Beyond raw throughput, Bilanc also classifies the purpose behind newly written code: bug fixes, performance improvements, new feature work, and so on. This gives engineering leaders a clearer picture of where time and effort actually went during any given period.

The key insight behind the platform is that you can't measure AI's impact on productivity without a rigorous baseline for productivity itself. Naive metrics – line counts, commit frequency – capture activity, not output. Bilanc uses its own AI engine to evaluate the substance and complexity of code changes in context: how dense was the existing code that was modified, and how difficult was the new code to produce? Roughly speaking, it estimates how much effort an AI would have expended doing the same work instead of a human developer.

Weekly reports are generated for individual engineers and teams – giving managers a data foundation for recognition, course-correction, or both.

Pricing is $20 per developer per month for teams under 100 engineers; larger organizations can negotiate custom plans and enterprise integrations.

Bilanc already has paying customers. It's worth noting the company launched out of Y Combinator with an entirely different product – a unit economics analysis tool for B2B startups that earned its own [dedicated review](/review/bez-jetogo-pribylnym-startapom-ne-stanesh). Sometime last spring, the team pivoted to the developer productivity platform covered here, which surfaced via a recent Product Hunt launch.

02 /Why It Matters

Bilanc recently extended its metrics framework in two meaningful ways.

The first extension introduced a three-dimensional model distinguishing activity, productivity, and efficiency – which sound interchangeable but measure distinct things. Activity tracks the frequency and regularity of actions (commits pushed, PRs opened). Productivity measures the volume of work completed (lines of meaningful new code). Efficiency captures the ratio of effort to outcome: how many commits did it take to close a given bug?

The second extension added developer satisfaction scores and a collaboration metric reflecting team communication levels – a deliberate move to make the system useful for managing humans rather than machines.

These are solid, sensible improvements. But they're tactical, and the more interesting strategic question is whether Bilanc is thinking big enough.

Consider Workhelix, which raised a fresh $15M in February on top of the $15.3M it closed last November (a [recent review](/review/jeto-ne-gemorroj-a-vozmozhnost-eshhjo-bolshe-zarabotat) covered it in detail). Workhelix operates in the same space as Bilanc but has positioned itself very differently: as "your partner for AI-powered workforce success." Its platform covers three phases – identifying where AI will drive the highest productivity lift per employee category, selecting and deploying the right tools, and then monitoring adoption and outcomes continuously.

In other words, Workhelix sells a turnkey transformation program. Bilanc sells a measurement dashboard.

Workhelix launched last April, signed several Fortune 500 customers on day one, and has been raising significant capital ever since. Bilanc, over the same period, shipped automated reports and refined its metric taxonomy.

One platform is moving strategically. The other is moving tactically. The gap matters.

03 /Opportunities

AI's role in software development is already substantial and growing fast. The CEO of Anthropic expects 90% of new code to be written with AI assistance by the end of this year. The operative word is "with" – AI excels at code generation, filling the role of a coder, but replacing senior engineers who architect, reason, and make judgment calls is a much longer horizon.

The practical challenge this creates for companies: how do you organize the most effective collaboration between human engineers and AI coding tools? That requires rethinking workflows, team structures, and performance frameworks – and platforms that help plan, deploy, and measure that transformation are exactly where the opportunity sits right now.

Bilanc and Workhelix are two plays in that space, but not the only ones.

Y Combinator graduate NextByte ([covered here](/review/nachinaj-proverjat-sotrudnikov-na-sovmestimost-s-ii)) is tackling the hiring side of the equation: a platform that helps companies screen for developers who can work effectively with modern AI coding tools.

Incident.io, which just raised $62M, addresses a different downstream problem: AI-generated code may ship faster, but it also breaks more often. Their platform helps dev teams respond to outages and incidents as a normal, structured business process rather than a crisis.

The thread connecting all of these is the phrase worth internalizing: the real opportunity isn't in measuring or fixing individual pieces of the human–AI workflow, but in owning the full business process – planning, adoption, and results together. That's what separates a point tool from a platform.

Which specific business process in this space would you find it most interesting to own? It's a real question worth sitting with – this direction is both urgent and wide open.