Law firms are deploying AI faster than they can trust the outputs. Argos detects hallucinations, flags omissions, and directs attorney attention — before it reaches the client.
The Problem
Attorneys increasingly rely on AI to produce legal work — but firms have no systematic way to measure, monitor, or govern whether those outputs can be trusted.
Even purpose-built legal AI tools fabricate citations, misstate holdings, and omit critical clauses — with high confidence and no warning.
Firms are spending significant time and budget on manual AI review, governance, and QA — with no systematic way to show it's working.
No independent platform benchmarks AI tools across real legal workflows. Firms rely entirely on vendor-supplied accuracy claims.
If attorneys treat every AI output with equal skepticism, the time savings collapse. Trust has to be earned at the output level, not assumed.
How It Works
Argos sits between attorneys and their AI tools. It evaluates every output, surfaces only the highest-risk sections, and lets attorneys skip the rest with confidence.
Use Harvey, Legora, OpenAI, Claude, or any internal model. Argos is tool-agnostic and integrates without changing your workflow.
Six evaluation layers run in parallel: citation verification, omission detection, grounding checks, consistency, reasoning quality, and risk classification.
Attorneys receive a trust score, an estimated review time, and a precise list of sections requiring attention — not a 45-item compliance report.
Attorney corrections feed a proprietary reliability dataset, continuously improving scoring and building your firm's AI performance record.
The Experience
Not machine learning dashboards. Not benchmark reports. A clear verdict, an estimated review time, and exactly where to look.
Capabilities
Six evaluation layers run simultaneously on every output, producing a calibrated verdict attorneys can act on.
Every legal authority, statute, and case reference is checked for existence, accuracy, and current validity. Fabricated citations are surfaced immediately with the exact section.
Claims are traced back to source documents and firm knowledge bases. Unsupported legal assertions are flagged with the precise gap identified in plain English.
The system identifies what should have been included but wasn't — missing MFN provisions, indemnification language, environmental reps, consent requirements, and more.
Defined terms, cross-references, section numbers, and date logic are validated across the entire document for coherence — catching errors before they become disputes.
Legal conclusions are evaluated for logical soundness — flagging outputs where the AI reaches a plausible result through flawed analysis or misapplied doctrine.
Track which AI tools perform best for your practice groups and matter types. Build a proprietary benchmark that tells you which vendor to route each workflow to.
Positioning
Argos is to legal AI what Datadog is to cloud infrastructure — the observability and trust layer that makes everything else deployable at enterprise scale.
| Question | Today (without Argos) | With Argos |
|---|---|---|
| Did the AI hallucinate? | Unknown. Attorney re-reads everything. | Flagged instantly with section and confidence score. |
| Which AI tool is best for this workflow? | Vendor marketing and internal anecdotes. | Objective benchmark from your firm's own evaluation data. |
| Where should I focus my review? | Entire document. Uniform skepticism. | 3 sections. 9 minutes. Estimated time shown upfront. |
| Can I defend our AI usage if challenged? | No audit trail. No governance record. | Full provenance, evaluation history, and defensible logs. |
| Is AI performance improving or degrading? | No data. No visibility. | Longitudinal reliability tracking by tool, workflow, and practice group. |
Alpha Program
We're recruiting early design partners — attorneys, legal ops leads, and firm technologists who want to shape how the industry governs AI. No sales pitch. Just honest collaboration.
We'll follow up within 48 hours. No spam, ever.