valv

Your database, safe for agents to query

OpenAI DaySaaSArtificial IntelligenceData & Analytics

▲ 74 votes9 commentsLaunched Jul 23, 2026

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Daily #22Weekly #100

Connect Postgres, MySQL, ClickHouse and PostHog once. Scope each role down to the row. Your team's agents work on live data and never see more than you allow.

AI Analysis

📝 Summary

Valv connects Postgres, MySQL, ClickHouse and PostHog once, enabling teams to define row-level permissions for different roles. AI agents can then query live production data without seeing unauthorized information. It solves the key pain point of safely granting agents database access while preventing over-exposure, data leaks, and compliance violations. The core value proposition is providing a secure, scoped abstraction layer that lets agents operate on real-time data with confidence.

📈 Market Timing

With explosive growth of AI agents in 2025-2026, maturing LLM capabilities for data interaction, and heightened data privacy regulations, timing is ideal. Demand for secure agent-to-database interfaces is surging following OpenAI advancements. Excellent Timing.

✅ Feasibility

High feasibility. Leverages mature database row-level security features; technical integration for multiple DB types is achievable. Moderate development and operational costs for a SaaS proxy. Strong scalability potential with low supply chain risk and clear compliance upside. Suitable for teams experienced in databases and AI.

🎯 Target Market

Primary segments: AI/ML engineers, data platform teams, and technical product managers at SaaS and AI-first companies. Industries: artificial intelligence, analytics, fintech. Mainly US and Europe-based tech firms. TAM for AI infrastructure security tools exceeds $5B with strong SOM in agentic workflows. Users have high willingness to pay to mitigate data breach risks.

⚔️ Competition

Medium. Direct competitors: 1. Supabase (supabase.com) with Row Level Security, 2. Cube.dev (cube.dev) for semantic data layers, 3. PostHog's own permissions, 4. Native Postgres RLS implementations, 5. Emerging agent data tools from LangChain ecosystem. Advantages: purpose-built for agents with one-time connect and multi-DB support. Disadvantages: newer player may need time to build enterprise trust and advanced features compared to established platforms.

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