LLM Structured Outputs: Stop Schema Drift in AI SaaS MVPs
Eliminate 500 runtime errors and silent database corruption by replacing naive JSON mode with strict schema enforcement and runtime validation.
Battle-tested software architecture, deterministic AI systems, and actionable 0-to-1 engineering roadmaps for founders seeking technical partnership.
Eliminate exact-match retrieval failures in AI SaaS MVPs. Combine BM25 full-text search with pgvector and Reciprocal Rank Fusion on a single Postgres database.
Eliminate 500 runtime errors and silent database corruption by replacing naive JSON mode with strict schema enforcement and runtime validation.
Naive MCP setups create massive security liabilities. Learn how to architect sandboxed, least-privilege MCP servers for enterprise AI SaaS MVPs.
Enterprise buyers reject fully autonomous AI agents due to hallucination liability. Learn how to architect durable, resumable HITL approval workflows.
Naive prompt-stuffing destroys AI margins and causes context rot. Learn how to architect a 2-tier memory system with asynchronous context compaction.
Eliminate catastrophic LLM outages and 429 rate limit crashes. Learn how to architect a resilient, token-aware AI Gateway for your SaaS MVP.
Eliminate cross-tenant vector data leaks in enterprise AI SaaS. Learn deterministic RAG isolation patterns using pgvector RLS, metadata pre-filtering, and strict namespaces.
Eliminate vibes-based prompt engineering. Learn how to install deterministic CI/CD LLM evaluation harnesses to prevent silent regressions and protect margins.
Discover why naive prompt chains crash in production and how deterministic state machines provide reliable, cost-controlled AI MVP architecture for founders.