Abliteration.ai

Abliteration.ai is an OpenAI-compatible chat completions API built for high-risk industries and workloads like cybersecurity, AI red teaming, trust & safety, synthetic data generation, healthcare research, and defense/government workflows. It’s designed to support legitimate professional prompts while applying governance before and during processing.

At the core is the Policy Gateway, which applies configurable policies-as-code to every model request. Teams can define per-project rules with one of five outcomes—allow, refuse, rewrite, redact, or escalate—optionally attach reason codes, and enforce access with scoped API keys and quotas.

For visibility and compliance, the platform streams audit logs to SIEM destinations including Splunk, Datadog, Elastic, and S3, with configurable privacy/retention options that include zero data retention. It also supports streaming and multimodal/document/image extraction, plus a training-data workflow for generating schema-validated dataset outputs ready for downstream pipelines.

What is Abliteration.ai best used for?

It’s built for high-risk professional workflows such as cybersecurity/red teaming, trust & safety, synthetic data generation, healthcare research, and defense/government use cases where teams need policy-governed LLM behavior.

Does Abliteration.ai support OpenAI-compatible requests?

Yes—use the OpenAI Chat Completions API via a base-URL change to abliteration.ai. It also supports Anthropic’s Messages API.

What is the Policy Gateway?

The Policy Gateway applies configurable policies-as-code to each request and selects one of five outcomes: allow, refuse, rewrite, redact, or escalate, along with reason codes where applicable.

How are audit logs handled for compliance needs?

Audit decisions and metadata are streamed to SIEM destinations like Splunk, Datadog, Elastic, and S3. The logging design supports governance by keeping prompts/outputs out of stored logs when zero data retention is enabled.

Is zero data retention available?

Yes. It’s configured so prompts, outputs, and identifying metadata are not retained or logged, with audit routed to your SIEM in a way that avoids keeping user/prompt/response-identifying content.

Can I generate training datasets with Abliteration.ai?

Yes. It includes a training-data workflow for generating dataset records (e.g., preference pairs, eval rows, classifier examples) with schema-validated outputs suitable for training pipelines.