The teams building legal AI products. And the legal teams who rely on what those products produce.
Your AI reasons from legal authority. The teams that win in enterprise are the ones whose product can be stood behind: by partners, by GCs, by the attorneys who file the brief.
RAG on documents retrieves text. It doesn't tell you whether the case is still good law, which circuits are bound by it, or whether the language you retrieved was a holding or a dissent. Those distinctions are the ones that matter in production.
Run Omniarch alongside your model to verify citations at query time. Jurisdiction-aware. Treatment-aware. Circuit-specific.
Labeled SAO triples with full provenance for fine-tuning legal reasoning models. The same verified assertions your grounding layer queries, usable as training signal. Legal reasoning that starts from ground truth.
Augment document retrieval with structured holding metadata. Circuit agreement signals. Recency flags. Treatment history.
Your AI research tools don't change the obligation; they change the surface area. The answer is either verified or it isn't. The firms that stand behind their AI-assisted work are the ones who built verification into the process, not after it.
47 state bars have issued formal AI guidance. Malpractice inquiries naming AI research errors have tripled since 2022. The exposure is documented and accelerating. The firms addressing it are building an audit trail that exists before anyone asks for it.
Verify AI-generated citations before filing. Holding vs. dicta. Circuit weight. Recency. All structured and documentable.
The audit trail exists before the question is asked. Source named. Jurisdiction validated. Timestamped.
Legal authority structured at the assertion level across every relevant jurisdiction, with treatment history. Due diligence that holds up when the answer gets challenged.