You own the piece of the firm's AI program that has to pass Rule 11, ABA 1.6, and the malpractice carrier's underwriting questionnaire — and no LLM wrapper sold to BigLaw today can pass those three tests at once. Teleox.ai is the deterministic-output substrate underneath the vendor of your choice: a 13-embedder retrieval stack makes citation verification an architectural property, not a post-hoc audit, and a constrained logit decoder plus Constellation Guard re-embed every output with a cosine score and a human-readable rejection reason. W3C PROV-JSON audit trail falls out of the architecture — it is a native property of the model, not a governance consultancy engagement — which is what makes citation-grade legal AI at the $20–50B-by-2032 category actually deployable inside the firewall.
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“Any reasonable attorney should know that a case is meritless if the only authority on which he can rely is a figment of imagination.”
Rule 11 and FRAP 38 have converted hallucinated citations from an ethical violation to a directly-underwritten financial liability, which is why the malpractice carriers are now writing AI-research-assistant riders on a per-output-audit-trail basis. A point-solution LLM wrapper like Harvey cannot underwrite that — there is no determinism guarantee, no on-prem option for privileged material, and no per-sentence artifact a litigator can file under Rule 11. Citation-grade is not a content-filter problem; it is the underlying capability stack. The firms that move now capture the $20–50B-by-2032 legal-AI category as owners of the verification substrate rather than renters of somebody else's model.
Harvey raised more than $1 billion cumulatively and closed its Series C at an $11 billion valuation in March 2026. In May 2025 it moved to multi-frontier routing across Anthropic and Google — an acknowledgment that single-frontier dependence was untenable.
But the multi-frontier posture still leaves every AmLaw and F500 Harvey customer with zero determinism guarantee, zero on-prem option, and no per-output audit artifact. When OpenAI, Anthropic, or Google absorb vertical legal AI, Harvey's enterprise customers re-procure.
Teleox.ai is the verification substrate Harvey — and every other legal-AI vendor — can run on. Privileged material stays inside your firewall.
The firm owns the citation-grade verification substrate that every brief, NDA, contract review, and deposition summary routes through. Harvey sells velocity; Teleox sells the proof that the velocity is defensible in court.
TCT meaning-extraction builds a firm-proprietary clause ontology that compounds with every contract reviewed. After twelve months, a rip-and-replace means rebuilding the ontology, the LoRA, and the verification guard — which is the durable position the firm, not the vendor, captures.
LoRA determinism plus meaning-labeled citation embedders make the model incapable of producing a citation that doesn’t exist in the training corpus with matching quote. Per-sentence audit artifact files alongside the brief. Converts Rule 11 and FRAP 38 risk from latent to provably mitigated.
9–50+ embedders build a firm-proprietary clause ontology that compounds with every contract reviewed. Displaces Spellbook and Harvey where the IP currently sits in a generic hosted model rather than in the firm’s own substrate.
0.961 SECS voice pipeline for verbatim playback; determinism for summary; on-prem so privileged material never leaves the firm. Directly satisfies ABA Model Rule 1.6 without a vendor DPA.
Your GC or CLO owns the mandate. You own whether the platform ships without a Rule-11 incident.
Bring Teleox to your firm's AI committee — 15-minute briefing plus a 48-hour POC on a slice of sample briefs your team picks.
Teleox.ai is a citation-grade, deterministic legal-AI infrastructure built on two pillars: meaning extraction across 9+ embedders that produces 100x+ labeled training signal from a firm’s own brief bank, contract corpus, and research library, and LoRAs that arithmetically constrain the decoder so the model is structurally incapable of producing a citation that is not matched in the training corpus with a verbatim quote. Every output ships with per-sentence audit artifacts — cosine similarity, source document, timestamp, and human-readable reason for acceptance. This converts Rule 11 and FRAP 38 exposure from a latent liability to a provably mitigated risk. The March 2026 Sixth Circuit decision in Whiting v. City of Athens imposed $116,315.09 — the largest AI-hallucination sanction on record — on a firm that filed briefs with fabricated authorities; Teleox prevents that failure mode by construction, not by content filtering.
The entire Teleox.ai stack is air-gapped-capable and deploys inside the firm’s firewall or the firm’s managed tenant. Training, inference, and guard verification all run on firm-owned hardware, so privileged and confidential client material never traverses a hyperscaler boundary and is never shared with a third-party model provider. That eliminates the third-party-disclosure question under ABA Model Rule 1.6 and removes the vendor-BAA and data-processing-addendum risk that currently blocks cloud-only legal-AI tools at most AmLaw 200 firms. The stack is model-agnostic and wraps open-weight models (Llama, Mistral, Gemma) for fully on-prem workloads — so the firm can run citation-grade generative AI without sending a single privileged document to OpenAI, Anthropic, or Google.
Harvey raised more than $1 billion cumulatively and closed its Series C at an $11 billion valuation in March 2026. Its co-founders have stated on the record that OpenAI is indirectly their biggest competitor, and in May 2025 Harvey moved to multi-frontier routing across Anthropic and Google — an acknowledgment that single-frontier dependence was untenable. But the multi-frontier posture still leaves every AmLaw and F500 Harvey customer with three unresolved exposures: there is no determinism guarantee on any output, there is no on-prem option for privileged material, and there is no per-output audit artifact a litigator can file under Rule 11. When OpenAI, Anthropic, or Google absorb vertical legal AI, Harvey’s enterprise customers will re-procure. Teleox.ai is the verification substrate Harvey and every other legal-AI vendor can run on, with privileged material staying inside the firm’s firewall.
Yes. Teleox.ai is designed for on-prem deployment on firm-owned hardware or inside a firm-managed tenant — a single-GPU workstation is sufficient for most workloads. No network egress is required for training, inference, or guard verification. The stack runs fully air-gapped for firms that require it, and the 48-hour POC runs on a slice of sample briefs or contracts your team picks inside your own environment. Day one, the stack installs behind the firewall, ingests the sample, and runs meaning extraction across 9+ embedders. Day two, the team trains an intent-locked LoRA for the target workflow, activates the Constellation Guard verification layer, and hands over the Constellation Guard verification results plus per-output cosine scores. The firm keeps all labeled signal, all model weights, and all artifacts regardless of outcome.
Teleox.ai is the verification substrate underneath the workflow vendor of the firm’s choice. Harvey, Thomson Reuters CoCounsel, Lexis+ AI, Spellbook, and Paxton AI all sell velocity at the interface layer; Teleox sells the citation-grade determinism and on-prem provenance that make the velocity defensible in court. The firm’s workflow vendor becomes a consumer of the Teleox verification layer rather than a competitor to it, and every brief, NDA review, contract redline, and deposition summary routes through Teleox before it leaves the firm. The firm owns the trust layer and the firm-proprietary clause ontology built by 9+ embedders — a switching cost that compounds with every contract reviewed.
“Hallucinated citations are not a content-filter problem. They are an architecture problem.
Teleox ships the stack where the citation either exists with verbatim quote — or the model declines.”
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