Deterministic AI Agents, CI/CD Evals & Vector Retrieval
Move beyond vibes-based prompt engineering. Build predictable multi-agent loops, automated regression eval suites, and token-efficient AI workflows.
Practical, code-backed engineering teardowns for ai engineering & deterministic systems.
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.
Discover the true break-even economics of self-hosted open-weight LLMs versus managed APIs to prevent burning thousands in idle GPU cloud costs before product-market fit.
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.
Stop subsidizing power users. Learn how to architect real-time token metering, credit wallets, and hybrid billing to protect 75%+ SaaS gross margins.
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.
Learn how to slash your AI SaaS API burn rate by 80% using prompt caching, semantic vector cache layers, and deterministic model routing cascades.
Discover why naive prompt chains crash in production and how deterministic state machines provide reliable, cost-controlled AI MVP architecture for founders.
Non-negotiable architectural practices we implement across founder codebases to prevent premature rewrites and ensure investor-grade quality.
Never push prompt modifications or model routing changes to production without automated CI/CD evaluation suites testing schema fidelity and regression metrics.
Constrain multi-agent autonomy using strict state machine transitions, validating JSON outputs deterministically before triggering business actions.
Structure system instructions and static RAG context to leverage provider prompt caching, cutting API latency and recurring token costs by up to 80%.
Treat LLM calls as probabilistic text generators and isolate parsing, data transformation, and database mutations within deterministic TypeScript modules.
Modular Monoliths, Multi-Tenancy & High-Velocity B2B Systems
From Concept to Production Launch in 4–6 Weeks
Executive Engineering Strategy Without Cap-Table Dilution
Slashing Cloud Spend, API Burn & Dev Inefficiencies