Objective engineering analytics that leaders and teams actually use
Start line and Current Position
Two years ago we pivoted. We stopped running a B2C product and doubled down on what we do best: building technology products. From day one we solved client problems from first principles.
Early on we noticed the industry talks in boxes like React Engineer or Python Engineer. That lens never made sense to us. When a client shared a problem, we did not send a rate card. We showed exactly how we would solve it and the team that would do it.
It worked. We grew from 16 people in 2023 to 35 in 2024 and now 110.
Growth brought a new problem. The people I used to work with closely now work across many client teams. I had no practical way to see performance, collect feedback, or spot issues early. Waiting for a client to get frustrated wastes time, hurts relationships, and erodes trust.
So we did what we do. We used software. Enter GreyMonk.
What is GreyMonk
GreyMonk is an engineering team analytics platform. It shows how teams are doing, both qualitatively and quantitatively.
We asked a simple question. How do you measure performance in engineering teams in a way that is fair and useful? You look at effort, attitude, outcomes, and the quality of those outcomes. GreyMonk brings the data together so leaders can coach and teams can improve.
Integrations include Jira, GitHub, Bitbucket, ClickUp, Slack and more. AI agents process the stream, connect the dots, and produce metrics that are easy to trust.
Breaking it down
Effort
Effort is straightforward and essential, especially in remote teams or when the client is not sitting with the team. Effort is the minimum signal you need to keep a healthy pace.
We track a few simple inputs:
- Commits over time and meaningful trends
- Git push or pull events, PRs opened and updated
- Issue movements that show real progress, not busywork
Effort visibility is not for blame. It is for coaching, removing blockers, and balancing load.
Quality
Measuring quality is hard and usually tedious. Many of us have done it during performance reviews. GreyMonk makes it objective by measuring what actually matters and keeping it auditable.
Core signals include:
- Code readability and churn patterns
- Test footprint and coverage movement where it matters
- PR analytics like review depth, unresolved comments, and post-merge rework
You get a fair picture of where quality is strong and where risk is rising.
AI Summaries
AI should remove busywork. In GreyMonk, agents read code diffs, PR comments, and task updates, then generate short summaries you can trust. At any time you can ask GreyMonk to produce a team or individual summary that combines Effort, Quality, and clear improvement areas.
Useful views:
- Daily or weekly team digest with wins, risks, and next steps
- Individual snapshot for coaching conversations
- Sprint summary that explains changes in velocity and workload distribution
Every statement links back to source events, so you can verify in one click.
CI/CD Metrics
Continuous Integration and Continuous Deployment are key to reliable delivery. Short release cycles and a quick rollback path show how prepared a team is.
We track the basics that matter:
- Total pipeline runs in a period and success rate
- Average pipeline duration
These metrics make release health obvious and help teams ship safely.
Pull Requests Metrics
PRs often decide how fast a team moves. We have seen teams where review and merge time is longer than building the feature. That is not ideal.
GreyMonk tracks:
- Merge rate and time to merge
- Time to first review and review coverage
- Trends over time so bottlenecks are visible and fixable
Jira/Clickup Charts
See Sprint Progress, issue distributions, and team activity. Less guessing, more delivery. Cycle time, blocked items, and aging work are in one place so you can steer the sprint without spreadsheets.
Our Roadmap
More integrations are coming. We are also focused on doing right by different personas. Today, GreyMonk is most useful for individual contributors who spend most of their time writing code. If we force that lens onto Technical Leads or Engineering Managers, we will show an incorrect picture, because their main job is to unblock people and product, drive decisions, and review design.
This is where we are making progress. Role-aware views will include signals like review response time and depth, design and ADR activity, incident follow up quality, mentorship impact, cross-team influence, and delivery predictability. Integrations will power this. More on this soon.
Want to try GreyMonk?
Tell us and we will take care of everything. We will connect the tools, set the scopes, and you will start seeing clear, fair signals you can act on.
