Bilanc scores productivity, reveals where time goes, answers ad-hoc questions, auto reports the story, and recommends fixes.

Core Features




AI Impact Analysis

Engineering Productivity
It's hard to lead when you don’t trust the data. We give you a full, accurate view of your entire engineering org. You get a clear picture of your team, deliver with more confidence, and have the credibility to push back, or speak up. You know exactly where to focus and make improvements.
Context-Aware AI Reporting
You waste hours pulling context from Jira and Git just to explain what happened. We turn raw activity across your codebase and backlog into clear, accurate narratives. You get the context that comes with actionable feedback on what to improve at every level.
Data Studio
Data questions shouldn’t wait for a sprint to finish—or for a data‑team ticket. Data Studio puts your entire engineering warehouse at your fingertips: every GitHub commit, every Jira issue, every CI run. Write SQL if you want, or just say what you need in plain English and our AI writes the query for you. From ad‑hoc checks to board‑level dashboards, you get instant, trustworthy answers - so you can spot bottlenecks and validate hunches.
Measuring AI Impact
Leadership wants to know if AI is actually helping, but right now you can’t prove it. We measure real adoption, benchmark performance shifts, and show exactly how AI is (or isn’t) moving the needle. We correlate every engineer’s AI usage with their output (productivity), so for every extra dollar spent, you know exactly how much more productive your team becomes
We’ve been using Bilanc at Origin Financial for the past few months, and it’s been awesome. It gives our engineering leaders super clear visibility without adding extra overhead — which is rare.
The insights are actually useful (not just vanity metrics), and it’s helped us improve productivity across the board. It’s lightweight, easy to adopt, and just works.
— Gustavo Silva (VP of Engineering)
Hey,... Long story short… Metrics like lines of code (LoC) on their own can be misleading or only tell part of the story. But combining all that data with LLMs turned out to be surprisingly powerful. We ran a demo and got our first analysis back in under 24 hours (data ingestion period), and it closely aligned with my own subjective analysis of the dev team. Top and bottom performers were clearly identified.
— Andis Cirulis (VP of Engineering)
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Engineering Productivtiy
AI Productivity Analytics
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PR Categorisation/Allocation
GitHub
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GitLab
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Get in touch
Email: sam@bilanc.co