Flexiana (own product. Gitrevio)
Building the system that tells owners where engineering value leaks
Leaders could see engineering activity, commits, tickets, deploys, but could not connect that work to project cost, risk and margin until the accounting period had already closed.
The intervention
We designed and operate Gitrevio: a multi-tenant platform that ingests 31 engineering, project and HRIS sources, resolves contributors to canonical identities, and runs a decision-science layer that forecasts cost-to-complete and flags margin and delivery risk while intervention is still possible.
Bayesian forecasting · Identity resolution · ELT / Prefect · Multi-tenant architecture
Result
A working, security-hardened product with 31 connectors and a 165-function decision-science layer, running inside Flexiana and offered commercially, the product and its pricing are public at gitrev.io.
Figures describe the product as built and operated by Flexiana. Client-specific outcomes are published only with the client's approval.
In production
The decision beneath it
Every founder and engineering leader makes the same repeated decision: is this project’s engineering spend still producing the result we expected, and if not, what can we still do about it? The honest answer usually arrives too late, in a finance report, after the decisions that caused the loss are irreversible.
What we built
Gitrevio connects the systems that describe engineering work (GitHub, GitLab, Jira, Linear, CI, observability) with the systems that describe cost and people (HRIS, finance), and resolves every human actor to a canonical identity with privacy controls built in. On top of that canonical model sits a decision-science layer, including the lognormal cost-to-complete forecaster documented in our research, and a library of analytical “skills” delivered through chat, reports, alerts and an API.
Why it is a decision system rather than a dashboard
The product’s job is not to show more activity metrics. It is to answer executive questions: plan versus reality, cost of delay, portfolio risk, the economics of AI-assisted delivery, without reducing an engineer to a single number. That constraint is a design principle: pro-owner and pro-accountability, without being anti-engineer.
What the evidence is
Everything above describes the product as Flexiana built and operates it. It is in production. Individual client outcomes, a specific margin recovered or a specific loss avoided, are only ever published with that client’s approval, and are absent here by design.
