abliteration.ai

Hi All, the creator of abliteration.ai here. abliteration.ai is a developer-controlled, OpenAI-compatible LLM API (drop-in replacement for /v1/chat/completions). We also offer an Enterprise Policy Gateway if you need governance: policy-as-code, safe rollouts, quotas, and audit logs. Why we built this If you’ve shipped anything user-facing with LLMs, you’ve probably hit two painful extremes: 1. Provider-side refusals are unpredictable (same request, different outcomes across models and small prompt changes). 2. “Unfiltered” models can be useful, but teams still need control, auditability, and safety in production. So we built an API where you decide behavior, and a gateway that makes those decisions deterministic and explainable. What’s different • OpenAI-compatible: keep your existing SDK and request shape, just change the base URL. • Instant migration tool: paste an OpenAI snippet, get a patch in seconds (Core API or Policy Gateway target). • Policy Gateway (optional): enforce outcomes like rewrite/redact/escalate/refuse with reason codes, plus simulate → shadow → canary → rollback and audit history. Privacy / data retention By default, prompts and completions are processed transiently and not stored; only operational telemetry (timestamps, token counts, error codes) is retained for billing and reliability. How to try it in 2 minutes 1. Grab an API key 2. Point your OpenAI client at https://api.abliteration.ai/v1 3. Call chat.completions with model abliterated-model If you’re evaluating governance, route calls through Policy Gateway and attach a policy_id. What I’m hoping to learn from you If you’ve built LLM apps in production, I’d love your take on: 1. What would make you trust a policy gateway sitting in the request path? 2. Which integration should we prioritize next: Vercel AI SDK, LiteLLM, LangChain, LlamaIndex, or something else? 3. What’s the one thing that would make you instantly bounce from this page/docs?