AI Reliability & Accuracy
AI makes things up. Every model, every provider, every platform. The question isn't whether your AI will hallucinate — it's whether your system catches it before it reaches a client, a courtroom, or a decision-maker. Here's how we built a system that does.
⚠️ The hallucination problem is real — and the industry knows it
AI hallucination in legal research isn't a theoretical concern. It's happening right now, to real firms, in real cases.
Attorneys sanctioned for AI-generated fake citations
Multiple attorneys across the U.S. have been sanctioned after filing briefs containing case citations fabricated by AI tools — cases that didn't exist, holdings that were invented, and docket numbers that were pure fiction. The AI presented these with complete confidence and proper formatting.
LexisNexis and Westlaw AI tools produce hallucinations
Independent testing has found that AI-powered features in LexisNexis (Lexis+ AI) and Westlaw (CoCounsel) produce hallucinated case citations, incorrect holdings, and merged case facts — despite being marketed as purpose-built legal AI. The models are confident, well-formatted, and wrong.
Studies confirm: legal AI hallucination rates are significant
Academic research has documented hallucination rates across legal AI tools ranging from 5% to over 30% depending on the task complexity. The more nuanced the legal question, the higher the risk of fabricated or inaccurate responses.
Triple verification system
Our answer to hallucination isn't "trust the AI more carefully." It's "never trust a single pass." Every critical analysis runs through three independent verification lenses.
Pro Pass
Build the strongest possible case for each finding. Gather supporting evidence, identify corroborating sources, and construct the affirmative argument. This is the pass that finds what you're looking for.
Antagonist Pass
Actively try to destroy every finding from the Pro pass. Look for contradictions, challenge sourcing, identify logical gaps, find counter-evidence. If a finding can be broken, this pass breaks it.
Neutral Pass
Evaluate both the Pro and Antagonist positions without advocacy. Weigh the evidence objectively, assess the strength of each side's arguments, and determine whether the finding is supported, disputed, or insufficient. This is the pass that decides.
Scenario: "Determine whether the MRI findings support acute traumatic injury vs. pre-existing degenerative changes."
"MRI dated 3/15 uses 'acute disc herniation' and 'traumatic' — radiologist's own language distinguishes from degenerative. ER notes from 3/12 document no prior cervical complaints. Treating orthopedist's 3/28 note states 'injury consistent with acute trauma from MVA.' Three independent providers support acute causation."
"⚠ MRI also notes 'mild degenerative disc disease at C4-C5 and C6-C7' — adjacent levels show wear. No pre-incident imaging exists to prove C5-C6 was normal before the collision. Defense will argue the herniation was a progression of existing degeneration, not acute trauma. PT intake notes from 4/2 mention 'occasional neck stiffness for years' — potential pre-existing symptom."
"FINDING: SUPPORTED WITH CAVEATS. The 'acute' language from the radiologist is strong evidence. However, adjacent-level degeneration and the PT intake note create a defense opening. Recommendation: obtain pre-incident primary care records to close the gap. The Eggshell Skull doctrine applies if degeneration is confirmed — defendant takes plaintiff as found."
The triple verification framework is fully customizable. Define your own lenses in plain English — add a "regulatory compliance" pass, a "financial risk" pass, or any domain-specific evaluation. The system adapts to your field.
Multi-model intelligence
Different tasks demand different levels of AI capability. Using a deep reasoning model for document classification is wasteful. Using a fast model for legal analysis is dangerous. We match the right model to the right task.
⚡ Fast
- Document classification & sorting
- OCR quality validation
- Metadata extraction
- Routine categorization
- Initial triage & filtering
Low token cost. High throughput. Used for tasks where speed matters more than nuance.
🔍 Standard
- Scenario analysis
- Evidence extraction
- Pattern detection
- Document summarization
- Cross-reference identification
Moderate cost. Strong comprehension. The workhorse for most analytical tasks.
🧠 Deep Think
- Legal reasoning & analysis
- Triple verification passes
- Synthesis generation
- Report writing
- Complex multi-step reasoning
Higher cost. Maximum capability. Reserved for tasks where accuracy is non-negotiable.
Cost management is built in
You choose the model tier per scenario, or let the system auto-select based on task complexity. A 500-document classification job runs on the Fast tier at a fraction of the cost. A settlement assessment runs on Deep Think because the stakes demand it. You see the estimated cost before every operation and the actual cost after.
We're continuously adding specialized models — including specialized models for sensitive data processing. The multi-model architecture means new models plug in without disrupting existing workflows.
Automatic bias detection
The most dangerous bias is the kind you don't know you have. Our system analyzes every analysis instruction for potential bias before running it.
"Identify all evidence supporting our client's claim of permanent disability."
⚠ Confirmation bias: Instruction assumes permanent disability is established. Analysis will selectively find supporting evidence while ignoring contradictory data.
⚠ One-sided framing: "Supporting our client's claim" creates advocacy bias. The AI will advocate rather than analyze.
"Analyze all evidence regarding the permanency of the client's disability, including both supporting and contradicting factors. Assess the strength of the permanency argument and identify vulnerabilities."
Bias detection runs automatically on every scenario. You can override it when deliberate advocacy is the goal — but the system makes you acknowledge the bias first. This protects against the subtle, unintentional biases that creep into research design.
How we compare to legal research platforms
Traditional legal research tools were built before AI. They've added AI features on top. We built AI-native from the ground up — accuracy and verification are architectural, not afterthoughts.
| Reliability Feature | Help Wizards | LexisNexis (Lexis+ AI) | Westlaw (CoCounsel) | Casetext (CoCounsel) | Harvey AI |
|---|---|---|---|---|---|
| Triple verification (Pro/Antag/Neutral) | ✓ Built-in | — | — | — | — |
| Automatic bias detection | ✓ Every scenario | — | — | — | — |
| Hallucination detection | ✓ Cross-pass validation | Known issues | Known issues | Some checks | Some checks |
| Multi-model (Fast/Standard/Deep) | ✓ Per-task selection | Single model | Single model | Single model | Limited |
| Cost transparency | ✓ Per-operation | Subscription only | Subscription only | Subscription only | Limited |
| Local LLM option (data stays on-prem) | ✓ Coming soon | Cloud only | Cloud only | Cloud only | Cloud only |
| Source attribution (finding → document → page) | ✓ Full chain | Basic | Basic | ✓ | Basic |
| Adversarial review synthesis | ✓ Built-in | — | — | — | — |
| Customizable verification framework | ✓ Plain English | Fixed | Fixed | Fixed | Limited |
| Your own documents as primary corpus | ✓ Any document | Limited upload | Limited upload | ✓ | ✓ |
| Multi-engine OCR (degraded documents) | ✓ 3 engines + forensic | Basic | Basic | — | — |
| Full customization | ✓ Every aspect | Fixed platform | Fixed platform | Fixed platform | Config only |
| Per-seat licensing | None | $$$$/seat/mo | $$$$/seat/mo | $$$/seat/mo | $$$/seat/mo |
| You own the code and data | ✓ No vendor lock-in | SaaS | SaaS | SaaS | SaaS |
The bottom line
Every AI system hallucinates. The difference is whether you catch it before it matters. We built triple verification, bias detection, multi-model intelligence, and source attribution into the architecture — not as features bolted on after a lawsuit, but as foundational design principles from day one.
The legal industry's largest platforms have spent billions and still produce hallucinations. We took a different approach: assume the AI is wrong, verify it's right, and make that verification automatic, transparent, and customizable.
The result is a system that virtually guarantees unbiased, solid information — triple verified, source-attributed, and bias-checked before it ever reaches an attorney's desk.
See the verification system in action.
We'll run a triple verification on your own documents and show you exactly how it catches what other tools miss.
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