Ask the AI CTO
Natural-language questions. Expert-level answers. Backed by real code analysis across the entire repository, not a sample or a summary.
For investors
Stop guessing about the tech. Get direct, code-backed answers to the questions that matter in every deal.
Is this an AI wrapper or does it have a real technical moat?
This is not an AI wrapper. The product contains a proprietary data pipeline processing over 2M events daily, 4 custom ML models trained on domain-specific datasets, and significant infrastructure complexity that would take 18+ months to replicate. The LLM integration accounts for less than 8% of the codebase. Technical moat: strong.
What are the top 3 risks I should flag to my IC?
1. No secrets manager: API keys are hardcoded in 14 files (high severity). 2. Single-region infrastructure with no failover: a regional outage would take the product fully offline. 3. Test coverage at 12%: critical payment logic has no automated tests. These are fixable but require dedicated engineering time.
How long would it take a new engineer to be productive here?
Estimated onboarding time: 3 to 4 weeks. The codebase has reasonable structure but lacks inline documentation, has no architecture decision records, and the local development setup requires significant manual configuration. The main bottleneck is tribal knowledge, not technical complexity.
For founders
Not every startup can afford a full-time CTO. Get senior engineering judgment on the questions that keep you up at night.
What would break first if we 10x our user base tomorrow?
The primary bottleneck is the main database. All read and write traffic goes through a single unindexed Postgres instance with no connection pooling. At 10x traffic, query times would degrade within hours. Secondary risk: the image processing job queue has no backpressure and would accumulate a backlog, delaying user-facing features.
What is the most critical security issue to fix before fundraising?
Priority one: 3 environment files with production secrets are committed to the repository history. Even if removed from the current branch, they remain accessible in git history. Rotate those credentials immediately and audit who has had access to the repository.
Are we accumulating technical debt faster than we are paying it down?
Based on the last 90 days of commit history: yes. Technical debt is increasing. Code complexity has grown 22%, test coverage dropped from 41% to 29%, and 3 major dependencies are now 18+ months behind their current version. The team is shipping fast but without a structured approach to quality. Recommend a dedicated debt sprint.
How it works
Link a GitHub repo via read-only access. The AI CTO reads the entire codebase, not a sample. No code is stored after analysis.
Type any question in plain English. There are no templates or forms. Ask exactly what you want to know about the codebase, the team, or the risk.
The AI CTO analyses the repository in the context of your question and responds with a specific, evidence-based answer, including the files and patterns that informed it.
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