Private AI for
regulated firms.
Don't give away your secrets.
Runs on your hardware. Cites every answer. Audit-verifiable offline.
The privacy problem
The strongest cloud AI models are trained on terms of service that allow the provider to log prompts, retain outputs, and route requests through infrastructure you do not control.
For a partner reviewing a transaction, a regulator assessing a license application, or an auditor working through a discovery bundle, those terms are disqualifying. The data does not belong to the firm; the firm is the custodian.
Abila is the answer for everyone who has decided that "we'll just turn off training" is not enough. The data does not leave. There is no API call to a model the firm does not own.
What Abila does
Ask matters questions
Hybrid retrieval across the matter's documents with citations to the exact passage.
Learn more →Draft with the firm's voice
Two-stage retrieval: matter facts plus the firm's prior work as the style source.
Learn more →Audit every answer
Hash-chained log of every prompt, every model, every cited chunk — verifiable offline.
Learn more →Who it's for
Law firms
50–300 fee-earners, matter-centric work, engagement-letter exposure on AI use.
Learn more →Regulators
License applications, fitness-and-propriety assessments, regulatory returns.
Learn more →Accountants & compliance
Regulated client data, on-premise as policy, audit-trail demands.
Learn more →Hash-chained audit
Matter-scoped access
No cross-matter retrieval. Ethical walls enforced at two layers.
Citation-verified output
Every answer's citations are checked against the retrieved chunks.
Hash-chained log
Every prompt, model, and chunk is recorded; the chain is verifiable offline.
Pen-tested per release
Expected to be pen-tested by every enterprise customer.