I open-sourced "AWS for AI." One docker compose for governed, compliant, auditable AI for your whole org. Gateway, guardrails, policies, observability, audit, etc - all wired together, built on open source.

Every piece you need to run AI in a company already exists as open source. A gateway to the models. Guardrails. PII masking. Policies. Evals. Audit. Lineage. Vector search. The problem was never the parts. It was wiring them into one thing that works — and keeping every team inside the rules. So I wrote an application layer on top of the best open source frameworks and made sure they actually talk to each other. One docker compose up and you get: - LiteLLM for the model gateway - one OpenAI-compatible endpoint across any model, on-prem or cloud - LLM Guard + Presidio for guardrails - PII redaction, prompt-injection, toxicity, secrets - OpenBao for secrets - Langfuse for LLM observability and tracing - OpenSearch for audit + SIEM - Marquez for data lineage - Temporal for durable agent runs - Qdrant for vector search / RAG - Airbyte + dbt to move data, ClickHouse for the warehouse, Great Expectations for data quality - Kestra for orchestration, Ragas + Evidently for evals + drift Then I built the part I think is the unlock: a lovable / bolt.new / replit.dev for your enterprise. You set up a pipeline and RBAC once, and now every employee can just talk to the system and build apps that replicate their workflows — inside the rules you already set. Human-in-the-loop reviews, reports, and autonomous agents included. A tax analyst or a claims adjuster builds a real governed workflow in plain language, and it physically can't step outside the guardrails, policies, and audit you defined. That's the whole idea: set your rules once, everyone builds governed AI on top.