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You track revenue. Track delivery too.
Most teams lose 40–60% of engineering time to reviews, approvals, and waiting. AI makes this worse. Efiros shows exactly where work gets stuck — and why delivery slows down.
Up and running before lunch
Connect GitHub or GitLab and analyze real workflow data. Same data can power your AI agents. No disruption.
See where work actually gets stuck
See where PRs stall, who is overloaded, and how long work actually takes — especially when AI increases load.
Know before it becomes a problem
See when PRs sit too long, review queues form, and cycle time climbs. AI amplifies this fast — fix it early.
Team insights, not surveillance
Efiros analyzes how the delivery system behaves — not how individuals perform. No productivity scores. No individual rankings. Just visibility into where the process breaks.
From problem to solution
Problem
You can't see where delivery slows down
PRs sit for days. You don't know why — or how much AI-generated work is adding to it.
You notice slowdowns after they already delay your release — especially as AI increases volume.
One person reviews most PRs. AI keeps sending more. Everything waits on them until they burn out.
Leadership asks "are we getting faster with AI?" You don't have a clear answer.
Solution
See how engineering work actually moves
Efiros tracks every stage of delivery — from the first commit to the moment code reaches production.
You can see how long work takes to move through your workflow and where pull requests start waiting.
You understand how quickly reviews happen and where review queues start slowing the entire team down.
All of this is measured automatically from your Git history — without surveys, manual reporting, or process changes. Available for dashboards and AI agents via MCP.
Your AI agents can use the same data — who has capacity, where work is stuck, and how loaded the system is — before creating more work.
Features
Delivery velocity metrics
Track cycle time, deployment frequency, and throughput automatically from your Git history. See how long work actually takes to move from first commit to production — and whether that number is improving or quietly getting worse.
MCP API for AI agents
Expose cycle time, review queue state, and reviewer capacity to your AI agents via one MCP endpoint. Your agents query it before creating PRs, assigning reviewers, or generating more work — so they operate with system awareness instead of blind output.
Workload balance tracking
Spot when one engineer is reviewing most of the team's pull requests before it creates a single point of failure. See who's carrying the load, who's underwater, and where the imbalance is building.
Real-time context for AI agents
Your delivery data is always fresh. When an AI agent queries the MCP endpoint, it sees the current state of the system — not a stale report. That means smarter decisions on every PR, every reviewer assignment, every time.
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What our users say
"We had dashboards and reports, but conversations about work were still based on opinion rather than shared understanding. Efiros helped us see where interaction patterns and coordination friction were actually occurring — not just that metrics changed, but why work felt stuck. That gave us a common language to address issues without finger-pointing and improve how teams collaborate over time."
"We used plenty of metrics, but none explained what was happening between teams and roles. Efiros revealed interaction patterns that explained where work piled up — in reviews, handoffs, or coordination — and helped us turn that into actionable insights. The shared clarity it created across engineering and leadership was a game changer for our planning conversations."
"What stood out was how Efiros transformed raw activity into meaningful signals about how teams actually interact. We could confidently share these interaction pattern insights with product, leadership, and risk stakeholders — without any suggestion of judging individual performance. This made cross-functional alignment feel grounded and constructive."
faQ
How long does setup take?
Setup usually takes a few minutes. Connect your GitHub or GitLab repository with read-only access and Efiros begins analyzing workflow data automatically. Most teams start seeing initial delivery insights shortly after connecting.
What kind of insights does Efiros provide?
Efiros shows how engineering work actually moves through your delivery workflow. You can see how long pull requests wait for review, where review queues form, how delivery speed changes over time, and where coordination delays begin slowing the team down.
What engineering metrics does Efiros track?
Efiros analyzes Git workflow data to track key engineering delivery metrics. These include pull request cycle time, review latency, deployment frequency, contributor workload distribution, and waiting time between commits, reviews, and merges. All metrics are calculated automatically from real Git activity, without manual reporting, surveys, or workflow changes.
What happens after we connect our repository?
Once connected, Efiros analyzes commits, pull requests, and review activity to understand how work flows through your engineering team. Within a short time you can see delivery speed, review patterns, and where work is getting stuck.
Will Efiros track individual developer productivity?
No. Efiros focuses on team-level delivery patterns such as review queues, cycle time, and coordination delays. It does not rank developers or score individual productivity. The goal is to understand how the delivery process behaves, not to monitor people.
Does Efiros analyze our source code?
No. Efiros analyzes Git workflow data such as commits, pull requests, and review activity. File contents and business logic are never accessed.
Is Efiros secure for engineering organizations?
Yes. Efiros connects with read-only access and analyzes Git workflow metadata only. It does not access source code and is designed for privacy-conscious engineering organizations.
