ChatGPT and Claude are genuinely useful for legal research and drafting. But they cannot ingest your case evidence, cross-reference contradictions across channels, or build strategic evidence profiles. They were not built for adversarial litigation.
What General AI Does Well
General-purpose AI tools are useful for certain litigation tasks. Legal research: summarizing case law, explaining concepts, identifying precedents. Drafting: first-draft memos, letters, filings. Summarization: condensing long contracts or regulatory filings. Reasoning: walking through multi-step arguments and spotting weaknesses.
For solo practitioners and small firms, these tools have democratized access to analytical support that previously required deep research benches.
Where General AI Falls Short
The gaps appear when you move from generic legal tasks to an active dispute with real evidence.
You cannot upload 2,000 WhatsApp messages, 150 email threads, and call recordings into ChatGPT and ask it to find contradictions across all of them. Context windows have limits. General AI processes what you paste in, not what your case file contains.
Even if you could, general AI does not overlay channels. It cannot find that an email from Tuesday contradicts a WhatsApp message from Wednesday and a call recording from Thursday. It has no architecture for temporal, cross-party analysis.
Each conversation starts fresh. You cannot build a cumulative evidence base that the system references across sessions, team members, and months of a dispute.
And there is a real privilege risk. A 2026 U.S. federal court ruling questioned privilege protection for AI interactions on public platforms. Pasting case evidence into public AI tools creates exposure.
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How Major Legal AI Platforms Compare
- Harvey: Contract analysis, legal research. Big Law focus. No multi-channel evidence ingestion. $11B valuation reflects enterprise pricing.
- CoCounsel (Thomson Reuters): Legal research, document review. Built on Westlaw data, not adversarial evidence analysis.
- Relativity: eDiscovery and document processing at scale. Processes documents but does not cross-reference channels for strategic contradictions.
- General AI (ChatGPT, Claude): Versatile and accessible. No evidence persistence, no cross-channel analysis, privilege risk on public platforms.
None of these ingest WhatsApp exports alongside emails and call transcripts in a single analysis. None detect cross-party contradictions. None are scoped around a single matter under instruction of counsel.
What Litigation-Specific AI Looks Like
A purpose-built system is architecturally different from general AI. Multiple specialized agents work in parallel: an admissions agent, a contradictions agent, a pattern detection agent, a financial analysis agent. A synthesis agent cross-references all findings.
All case evidence is indexed and available across sessions. New evidence integrates with existing analysis. Every finding traces back to a specific document, message, or recording. No unsourced conclusions.
Deployment flexibility means firms choose the right security posture for each matter: on-premise, hybrid, or cloud.
When to Use Which Tool
Quick legal research question: general AI. Contract review: Harvey or CoCounsel. Large-scale document processing: Relativity. Active commercial dispute with evidence across multiple channels: litigation-specific AI.
The right answer is often both. General AI for research and drafting, litigation-specific AI for evidence analysis and case strategy. They are complementary.