Today we’ve added two more services:
- CodeSecurity — iterative application security review
- CodePerformance — structured performance improvement workflow
Why we built CodeSecurity
Most security tools generate a report and stop there.
In practice, teams:
- Fix a few issues
- Forget the rest
- Don’t re-verify properly
We designed CodeSecurity as an iterative loop instead of a one-off scan:
- Connect GitHub
- Select a PR or branch
- AI reviews for real, exploitable vulnerabilities
- Engineers fix
- Re-run → AI verifies whether issues are actually resolved
Issues are tracked with:
- Severity (High/Medium/Low)
- File + line numbers
- Concrete suggested fixes
- Status workflow (Open → In Progress → Resolved → Closed/Rejected)
- Full verification history
It behaves more like a managed security workflow than a static analyzer.
Why we built CodePerformance
Performance reviews often happen reactively (after something slows down in prod).
CodePerformance focuses on material runtime impact:
- Algorithmic inefficiencies
- N+1 queries
- Blocking I/O
- Memory pressure
- Concurrency bottlenecks
- Event-loop blocking (Node), GIL issues (Python), etc.
Same loop:
Find → Fix → Re-run → Verified.
Current platform
Everdone now includes:
- CodeDoc
- CodeReview
- CodeSecurity
- CodePerformance
Pricing:
- First 200 files free
- $0.05 per file per review (early access pricing)
- Unlimited users
- No contracts
Usage-based only.
We also have live demos on public OSS repos if anyone wants to explore without signing up.
We’re trying to build “Work as a Service” — AI systems that fit into real engineering workflows rather than replacing them or generating static reports.
Would love feedback from other founders or engineering teams.
Over the past few months, we’ve been building Everdone — an AI-powered engineering workflow platform.
We initially launched with: - CodeDoc (AI-generated code documentation) - CodeReview (structured issue detection + tracking)
Today we’ve added two more services: - CodeSecurity — iterative application security review - CodePerformance — structured performance improvement workflow
Why we built CodeSecurity Most security tools generate a report and stop there.
In practice, teams: - Fix a few issues - Forget the rest - Don’t re-verify properly
We designed CodeSecurity as an iterative loop instead of a one-off scan: - Connect GitHub - Select a PR or branch - AI reviews for real, exploitable vulnerabilities - Engineers fix - Re-run → AI verifies whether issues are actually resolved
Issues are tracked with: - Severity (High/Medium/Low) - File + line numbers - Concrete suggested fixes - Status workflow (Open → In Progress → Resolved → Closed/Rejected) - Full verification history
It behaves more like a managed security workflow than a static analyzer.
Why we built CodePerformance Performance reviews often happen reactively (after something slows down in prod).
CodePerformance focuses on material runtime impact: - Algorithmic inefficiencies - N+1 queries - Blocking I/O - Memory pressure - Concurrency bottlenecks - Event-loop blocking (Node), GIL issues (Python), etc.
Same loop: Find → Fix → Re-run → Verified.
Current platform Everdone now includes: - CodeDoc - CodeReview - CodeSecurity - CodePerformance
Pricing: - First 200 files free - $0.05 per file per review (early access pricing) - Unlimited users - No contracts
Usage-based only.
We also have live demos on public OSS repos if anyone wants to explore without signing up.
We’re trying to build “Work as a Service” — AI systems that fit into real engineering workflows rather than replacing them or generating static reports.
Would love feedback from other founders or engineering teams.
Happy to answer anything.
— Vinit