Data as of Oct 18, 2026Based on 380 AI responses
Reviewed by Dimitry Apollonsky
Rank and Mentioned in · since Jul 5
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | cloud-native predictive coding, continuous active learning, and early case assessment | cloud-native predictive coding, continuous active learning, and early case assessment | 78% |
| 2 | complex, large-scale litigation and defensible technology-assisted review workflows | complex, large-scale litigation and defensible technology-assisted review workflows | 50% |
| 3 | high-volume privilege detection, PII classification, and defensible audit trails | high-volume privilege detection, PII classification, and defensible audit trails | 45% |
| 4 | drag-and-drop document culling for budget-conscious small law firms | drag-and-drop document culling for budget-conscious small law firms | 29% |
| 5 | fast ingestion and AI-driven review for high-volume matters | fast ingestion and AI-driven review for high-volume matters | 21% |
| 6 | data visualization and behavioral analytics for early case assessment | data visualization and behavioral analytics for early case assessment | 21% |
| 7 | visual early case assessment analytics and privilege identification | visual early case assessment analytics and privilege identification | 20% |
| 8 | generative AI privilege logging and classification for massive datasets | generative AI privilege logging and classification for massive datasets | 12% |
| 9 | agentic AI inquiry and contextual examination across document collections | agentic AI inquiry and contextual examination across document collections | 11% |
| 10 | early case assessment reporting and cost forecasting via CaseAssist | early case assessment reporting and cost forecasting via CaseAssist | 9% |
| 11 | 8% | ||
| 12 | 8% | ||
| 13 | 7% | ||
| 14 | 6% | ||
| 15 | 6% |
What’s changing · since July
Since July, RelativityOnerising has become the most named platform for technology-assisted review to reduce document volume. Nextpointrising and Logikcullrising have become common answers for small firm litigation and cost assessment visualization.
Weeks of Jul 27 – Aug 23, 2026 · Since Jul 5
Widest split: Everlaw, 87% on ChatGPT Search and 70% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Everlaw | 87% | 70% | |
| Relativity | 57% | 42% | |
| RelativityOne | 41% | 48% | |
| Logikcull | 33% | 26% | |
| DISCO | 15% | 28% | |
| Brainspace | 21% | 20% | |
| Reveal Data | 20% | 20% | |
| Relativity aiR for Privilege | 12% | 12% | |
| Cecilia (DISCO) | 3% | 18% | |
| Casepoint | Outside ChatGPT Search's top 25 | 18% |
57% of citations point at the ranked brands’ own sites. gartner.com, learn.g2.com, reddit.com are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 5
Leading brands by topic
I need recommendations for eDiscovery tools that are actually practical for a small litigation practice.
Which eDiscovery platforms detect cross-language duplicates and cluster conceptually similar documents before attorney review?
We are facing a massive litigation with terabytes of email data. What platform uses AI to most accurately flag "privileged" documents to reduce manual review hours?
We need an early case assessment tool with strong visualization features to help us forecast litigation expenses prior to starting full data processing.
Can you recommend an e-discovery platform that excels at large-scale privilege and PII classification?