Legal Intelligence API

The grounding layer
legal AI has been missing.

Every legal assertion made by federal courts since 1951, extracted, structured, and source-traced to the paragraph. Jurisdiction-mapped. Machine-readable. The verification infrastructure your legal AI needs before it ships.

97.1%
Pipeline Accuracy
gold-set evaluation · 238 items
70+
Years Covered
federal courts 1951–present
3-voter
Annotation Method
Dawid-Skene consensus · multi-model
13
Circuits Covered
all U.S. courts of appeals
Accuracy measured against human-annotated gold set. Methodology available on request.
Extracted, not generated · Paragraph-level provenance · 97% extraction accuracy · Federal courts: 1951–present

The cases were real. The jurisdiction was not.

Legal AI is already in production.
The verification layer isn't.

Sanctions
1,954+
Documented court proceedings where AI-fabricated legal authority was submitted to a court or tribunal
Charlotin AI Hallucination Cases Database, Aug. 22, 2026
Hallucination Rate
17–33%
Legal AI tools hallucinate on 17–33% of queries, even specialized platforms. General-purpose models reach up to 88%.
arXiv hallucination benchmarking research, 2025
Liability
3×
Increase in malpractice inquiries naming AI research errors, 2022–2025
Law firm professional liability insurer trend reports, 2022–2025
Guidance
47/50
State bars have issued formal AI guidance citing citation accuracy risk
State bar AI ethics guidance compilation, 2024–2025

The model is not broken. The infrastructure was never built.

Why RAG isn't enough.

Retrieval-augmented generation returns documents. It doesn't tell you if a holding still applies, whether the jurisdiction agrees, whether what was cited as precedent was dicta, or whether the law moved since the model was trained. Legal authority is not a document. It is an assertion, made by a specific court, in a specific jurisdiction, at a specific time, with a specific precedential weight.

KeyCite and Shepard's are the best tools available today. They were built for human lawyers reading PDFs, not for AI systems that need structured, assertion-level data at query time. We built what comes next.

Today's approach
Document retrieval
Human-readable PDFs
Citation existence
Built for human readers
Omniarch
Assertion-level structure
Machine-readable JSON
Treatment history + jurisdictional weight
Built for AI systems
AI Legal Product
Unverified Output
LLM generates citation, brief, or memo without structural verification
UNVERIFIED
grounding
call
Data Layer
OMNIARCH
Structured assertion lookup · jurisdiction scope · treatment history
ASSERTION LAYER
verified
output
AI Legal Product
Grounded Output
Response with verified source, jurisdiction, and treatment status
VERIFIED
Omniarch is a data layer, not an end-user product.
Without Grounding
AI Response

Chevron deference requires courts to defer to agency interpretations of ambiguous statutes. See Chevron U.S.A. v. Natural Resources Defense Council, 467 U.S. 837 (1984).

OVERRULED 2024 — NOT GOOD LAW
With Grounding
typeHOLDING
actionoverruled
treatmentOVERRULED · 2024-06-28
bindingnationwide
confidence0.97
In Practice

See what verified legal reasoning looks like.

Legal Query
Is Chevron deference still good law?
Reason
Chevron deference Qualified immunity Cell phone search
No. Chevron deference was overruled by Loper Bright Enterprises v. Raimondo (2024). Courts must now exercise independent judgment on statutory meaning.
Chain of Proof
1
Rule Identification
Overruled
Chevron U.S.A. v. NRDC, 467 U.S. 837 (1984) established a two-step deference framework for agency interpretation of ambiguous statutes.
Chevron established two_step_deference
2
Doctrinal Rupture
HoldingBinding
Loper Bright Enterprises v. Raimondo, 603 U.S. 369 (2024) overruled Chevron. Courts must exercise independent judgment in determining statutory meaning. This is a holding, not dicta.
3
Corpus Scan
High
184 holdings in the corpus cite Chevron deference as dispositive authority across 712 opinions. Cross-referencing each against post-Loper Bright activity.
corpus found 184_chevron_dependent
4
Vulnerability Assessment
Med
47 reaffirmed on independent grounds. 23 explicitly revisited and modified. 114 remain potentially unstable — reasoning relied on Chevron and has not been revisited.
reaffirmed count 47  unstable count 114
Independent Corroboration
Holding Validity
Converge
Authority Chain
Converge
Doctrinal Arc
Converge
Statutory Text
Partial
Reasoning Analysis
Diverge
Case Timeline
1984
Chevron v. NRDC
Two-step deference established
2001
United States v. Mead
Limited to force-of-law
2015
King v. Burwell
Major questions exception
2022
West Virginia v. EPA
Major questions expanded
2024
Loper Bright
Chevron overruled
Jurisdiction
Federal circuits — relevance to this query
1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th DC Fed SCOTUS

Coverage: 1st–11th circuits, D.C. Circuit, Federal Circuit

Affected Holdings
Nat'l Cable v. Brand X
545 U.S. 967 (2005)
Extended Chevron to override prior judicial interpretations of ambiguous statutes.
Unstable
City of Arlington v. FCC
569 U.S. 290 (2013)
Applied Chevron deference to agency determinations of their own jurisdiction.
Unstable
King v. Burwell
576 U.S. 473 (2015)
Major questions exception — survived Loper Bright on independent grounds.
Reaffirmed
Util. Air Reg. Grp. v. EPA
573 U.S. 302 (2014)
Rejected EPA interpretation but applied Chevron framework in reasoning.
Uncertain
+ 110 additional holdings
What LLMs cannot guarantee. We can.
Deterministic
Same query. Same structured result. Every time. No temperature, no variation, no drift.
Inspectable
Every assertion exposes its source, reasoning chain, and jurisdiction. Nothing is averaged away.
Provenanced
Every triple traced to a named court, docket number, date, and paragraph. Nothing synthetic.
Debuggable
When a result is wrong, trace exactly where and why. LLMs cannot explain their own errors.
Explainable
Show why a proposition holds: which court decided it, which precedent chain supports it, how it's been treated since.
Three things only we do

The distinctions that matter most
are the hardest to make.

Speaker Attribution

The only system that knows the difference between a dissent and a holding.

Every AI system that retrieves legal text can retrieve a dissent. Most can't tell you it's a dissent. Omniarch tags every assertion with its speaker: majority, concurrence, or dissent. Citing a dissent as precedent is not a subtle error. It is the kind that surfaces in front of a judge.

Legal Typology

The only system that types every legal edge as a duty, a right, a privilege, or a liability.

An obligation imposed on a party is not the same as a right granted to one. Omniarch applies Hohfeld's legal typology to every extracted assertion. The type of legal relationship determines what the AI can safely conclude from it.

Temporal Precision

The only system that tracks three separate clocks.

When a case was decided. When the statute it interprets took effect. When the rule it establishes applies. These three timestamps are often different. Systems that track only one draw incorrect temporal conclusions. Omniarch tracks all three.

Coverage.

Federal
1951 to Present
All 13 courts of appeals: 1st–11th circuits, D.C. Circuit, and Federal Circuit. Supreme Court opinions included.
California
2000 to Present
California Supreme Court and Courts of Appeal. Full appellate coverage.
Additional States
Available
Additional state and federal corpus coverage available. Other jurisdictions are accessible. Tell us what your use case requires.
Talk to our team →
Who is this for?
Building legal AI
The grounding layer your product needs.
Structured assertion records via API. Every holding filtered by jurisdiction, confidence-scored, and traced to the paragraph it came from. Built for the systems that generate legal text, so you can verify it before your users rely on it.
See AI vendor use cases
Running a legal team
When the judge asks where that came from, you have an answer.
When a federal judge asks where that citation came from, your answer is either verified or it isn't. Omniarch gives your team a documentation trail for every legal assertion your AI produces: specific court, specific paragraph, specific date.
See law firm use cases

Thou Shalt
Not Lie.

Every claim your AI makes should survive a judge's question. Omniarch gives you the structure to verify it before they ask.

Talk to a Legal AI Expert or reach us at hello@omniarch.inc