Scan legacy codebases for AI integration opportunities and auto-insert hooks.
Enterprise teams struggle with integrating AI tools into legacy systems, causing massive delays and frustration in deployment.
ScanAI Bridge analyzes repos/DB schemas to identify AI enhancement points like search or analytics. It suggests and generates insertion points with minimal code changes. Teams validate and deploy hooks safely via previews.
Enterprise IT and development teams managing legacy systems
Deep codebase scanning with AI-specific recommendations, beyond static analysis tools.
friendly
Connect GitHub/GitLab to scan for integration hotspots.
AI identifies 20+ patterns for AI augmentation.
Insert non-breaking AI call snippets.
Diff view and local sim of changes.
Score changes for perf/security impact.
Push hooks as Git PRs.
Track hook performance post-deploy.
Community/shared hook templates.
Hooks for Jenkins/GitHub Actions.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| user_id | uuid | No |
| git_url | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| repo_id | uuid | No |
| opportunities | jsonb | No |
| completed_at | timestamp | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| scan_id | uuid | No |
| code_snippet | text | No |
| risk_score | int | No |
| deployed | bool | No |
Relationships:
/api/scansTrigger repo scan
/api/scans/:id/hooksList opportunities
/api/hooks/:id/previewGenerate preview
/api/reposConnect repo
/api/analytics/:hookIdHook metrics
10 hooks/mo
50 hooks/mo
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 250 | 1.5% | $94 | $1,128 |
| Month 6 | 1,500 | 4% | $1,500 | $18,000 |
Discover hidden AI opportunities in your codebase – insert effortlessly.
Leverage GitHub stars via open source legacy repos; Reddit r/learnprogramming enterprise threads; personalized outreach to devrel at enterprises.
Code quality
No AI focus
AI opportunity hunting
Security
Not integration
Proactive AI enhancements
Growing library of scan patterns from user data
Developer tools AI-ifying, legacy codebases vulnerable to disruption
Parsing diverse languages fails
Start with Python/JS/Java
GitHub API limits
Caching + app auth
Success: Positive Twitter feedback
Success: 3 testimonials
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