Built with Sweep AI?
Let's make sure it's production-ready.
Sweep AI is an AI developer that converts GitHub issues into pull requests automatically. It reads your codebase, interprets the issue, generates code changes, writes tests, and responds to code review feedback. We help non-technical founders identify and fix the issues AI tools leave behind.
Common issues we find in Sweep AI code
These are real problems we see in Sweep AI projects during our audits — not hypotheticals.
PRs may be too narrow, missing related bug sources
Sweep fixes the specific symptom described in the GitHub issue but often misses related root causes in adjacent code. The bug reappears from a different trigger after the narrow fix.
Security changes in PRs not reviewed for regression
When a GitHub issue involves auth or permissions, Sweep's generated PR modifies security-sensitive code. These changes require careful human review that automated PR creation workflows can bypass.
Generated tests verify implementation, not behavior
Sweep writes tests alongside its code changes, but the tests assert that the specific implementation works rather than that the feature behaves correctly across realistic input scenarios.
Edge cases not covered when issue description is vague
Sweep implements what the issue says literally. Vague issue descriptions lead to implementations that miss important edge cases — null inputs, concurrent requests, or invalid data.
Performance impact of fix not evaluated
Sweep prioritizes correctness in its fixes without profiling the performance impact. A working fix may introduce N+1 queries, unnecessary re-computation, or blocking operations.
No deployment or migration artifacts generated
When Sweep adds new database columns, config values, or environment variables, it doesn't generate the migration scripts, deployment notes, or infrastructure changes needed to ship the fix safely.
Iterative feedback loop produces inconsistent code
When Sweep responds to code review comments by modifying its PR, each iteration can introduce subtle inconsistencies with the previous version without a holistic view of the full change.
Dependency additions lack version pinning strategy
New packages added by Sweep may not follow your project's versioning or lockfile management conventions, creating upgrade drift over time.
How we can help with your Sweep AI project
From security reviews to deployment, we cover everything you need to go from prototype to production.
Security Review
Deep security analysis and hardening
Fix Bugs
Resolve issues and unexpected behavior
Deploy & Ship
Get your Sweep AI app to production
Refactor Code
Clean up AI-generated or legacy code
Performance
Make your Sweep AI app faster and more efficient
Add Features
New functionality, integrations, capabilities
Testing
Add tests and improve coverage
Infrastructure
Set up and manage your Sweep AI backend
Start with a self-serve audit
Get a professional review of your Sweep AI project at a fixed price. Results reviewed by experienced engineers.
Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 checks
- SSL/TLS analysis
- Security headers
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerabilities
- Code quality review
- Dependency audit
- AI pattern analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Get a clear plan
We respond in 24 hours with scope, timeline, and cost.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Can I merge Sweep AI pull requests without manual review?
We recommend reviewing every Sweep PR before merging — particularly for security-sensitive changes. Auto-merging AI-generated PRs without review is one of the most common ways vulnerabilities enter production codebases.
How do I write GitHub issues that produce better Sweep PRs?
Be specific: include the expected behavior, actual behavior, edge cases to handle, and any performance requirements. Vague issues produce vague fixes. We can help structure your issue templates.
Can SpringCode review Sweep AI pull requests?
Yes. We review Sweep PRs for correctness, edge case coverage, security implications, and convention adherence before you merge.
Does Sweep AI work well for bug fixes versus new features?
Sweep is strongest for well-defined bug fixes with clear reproduction steps. Feature implementations benefit from more human direction to ensure the right architecture and scope.
What happens when Sweep misinterprets an issue?
You get a PR that technically passes review but doesn't solve the real problem, or one that introduces new issues. Our code reviews catch these misinterpretations before they reach production.
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Get your Sweep AI app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.