Sapyen monitors every PR and commit, scores code quality, and shows where your team is improving. All automatic, no time wasted.
Subscription required to use features.
Analyses
1,248
Avg score
87/100
Issues detected
342
~2h/day
given back to the tech lead
−63%
critical issues reaching production
< 120s
to analyze a full PR
100%
of PRs covered, no exceptions
Features
Bugs, complexity, technical debt and bad practices detected before you open the diff. You enter the review with a map of the terrain, not in the dark.
Each PR gets a score from 0 to 100. Each developer builds history. You see who's improving and who needs attention before it becomes a problem.
The LLM you chose applies the same criteria to every commit. No variation by day, mood, or who's on duty.
OpenAI, Claude, OpenRouter, Azure OpenAI, or AWS Bedrock. Your API key, your model, your privacy level.
Separate projects, clients, or squads into isolated codebases. Ideal for consultancies and companies with multiple products.
Summary, issues by severity, estimated effort and improvement suggestions — all structured, ready to paste into a 1:1 or retro.
How it works
A Personal Access Token and your organization name. Less than 2 minutes.
OpenAI, Claude, Azure, Bedrock, or OpenRouter. Enter the API key and select the model. You control the cost.
Sapyen scans your repositories, analyzes open PRs and commits, and delivers the first scores immediately.
Who's delivering well. Who's overloaded. Where technical debt is accumulating. With data, not perception.
You were hired to make technical decisions, mentor the team, and move the architecture forward. Not to spend the day reading PR diffs on formatting.
You open GitHub in the morning and already have 8 PRs waiting. Half are trivial. But you don't know which half without reading them all.
2–3h per day consumed in low-value review.
No visibility into who is improving or declining in the team.
Ad-hoc feedback, no data to back up 1:1s.
Quality standards vary by who's reviewing that day.
Inconsistency that creates friction and subjective debates.
Recurring issues are only detected after the merge.
Technical debt silently accumulating in the repository.
LLM does the first pass on each PR: bugs, complexity, coverage, patterns.
You review what matters. The rest has already been flagged.
Individual score per developer with historical trend.
1:1s backed by objective data, not perception.
Uniform quality criteria, defined by the LLM you chose.
Less subjective discussion, more real alignment in the team.
Issues detected before merge, with fix suggestions.
Technical debt under control since the first commit.
Can you answer right now who on your team is regressing?
Average quality score per developer · last 30 days
Ana Ferreira
@anadev
vs prev month
Carlos Lima
@carlim
vs prev month
Diego Souza
@dsouza
vs prev month
Mariana Costa
@mcosta
vs prev month
Rafael Torres
@rtorres
vs prev month
2 developers need attention — Rafael and Diego show a consistent decline. Consider reviewing workload or scheduling pair programming sessions.
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Set up in less than 10 minutes. Nothing to install, no change to the team workflow.
Connects with GitHub, OpenAI, Claude, Azure and OpenRouter.