Your codebase is leaking quality.
Find out where.

We analyze your PR history to find recurring bug patterns, then help you fix them. Diagnosis, remediation plan, implementation, monitoring.

Book a call

30 min intro — we'll show you what we find in your repo.

You have the tools. You don't have the diagnosis.

Linters, type checkers, CI pipelines, code review bots — your team already runs dozens of dev tools. But none of them tell you which recurring issues are actually worth fixing, or how much they cost you every sprint.

Tools catch symptoms, not patterns

Your linter flags a missing null check. But it doesn't tell you that null-handling bugs account for 30% of your fixes this quarter — or that contract tests would eliminate them.

12+tools in your CI

Same bugs keep shipping

Format handling, state sync, edge cases — the same bug types recur across different files and engineers. The tools you have don't connect the dots.

44%avg fix-to-feature ratio

Nobody has time to figure out which fix matters

Property-based testing, contract tests, payload validation — the solutions exist. But knowing which one to adopt, where, and why? That takes an analysis nobody has bandwidth for.

0hours spent on root cause

Diagnose. Prescribe. Fix. Verify.

01

Diagnose

We analyze your PR history. Where bugs cluster, what types recur, fix ratio trends. All from your GitHub data.

02

Prescribe

An expert reviews the diagnosis and creates a remediation plan specific to your stack, your CI, your team structure.

03

Implement

We open PRs with test infrastructure, CI config, and example tests. We pair with your engineers to set it up.

04

Monitor

Track whether interventions worked. Fix ratios, revert rates, pattern recurrence. See the before and after.

What the scan reveals

Real analysis from a public repository. Your report will look like this.

paradigm — diagnosis report
$ paradigm scan posthog/posthog
Analyzing 4,218 pull requests...
Classifying bug patterns...
=== DIAGNOSIS REPORT ===
Repository:posthog/posthog
PRs analyzed:4,218
Time range:Jan 2025 - Mar 2026
Quality Metrics
Fix ratio:44%(above 30% benchmark)
Revert rate:3.2%(normal)
Mean time to fix:4.7 days(up from 3.1 days)
Fragile Areas
replay31 bug-fix PRs
llma24 bug-fix PRs
batch-exports18 bug-fix PRs
Recurring Patterns
format-handling13 incidents-> contract testing
state-sync9 incidents-> state machine refactor
null-edge-cases7 incidents-> property-based testing
=== RECOMMENDATIONS ===
1.Add contract tests for payload boundaries in replay + llma
2.Introduce payload corpus from production for format-handling coverage
3.Add property-based tests for null/edge-case clusters
Estimated impact: reduce fix ratio from 44% to ~28% within 3 months

Built for teams that ship fast and break things

Engineering Managers

You know quality is slipping but can't articulate why. Sprint velocity is down. Bug reports are up. You need data to make the case for investing in quality.

"We spend half our sprints on bug fixes but I can't prove it to leadership."

Staff & Principal Engineers

You see the systemic patterns. You know contract tests would fix it. But between feature work and on-call, there's no time to set up the infrastructure.

"I've been meaning to add property-based tests for six months."

CTOs at 20-100 eng orgs

Bug cost is high but you don't have a dedicated quality or platform team. You need someone who can diagnose, implement, and measure — not just advise.

"We can't afford a quality team, but we can't afford not to have one."