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Legal AI

Security and complianceAugust 2026 edition

Which questions do you win vs lose?

Get your brand’s prompt-level results

Recommendation share

How often each brand is named, across unbranded questions and all five engines

BRANDS

SHAREShare of answers naming this brand, averaged across the five engines

ChatGPTChatGPTShare of ChatGPT answers that name this brand
ClaudeClaudeShare of Claude answers that name this brand
GeminiGeminiShare of Gemini answers that name this brand
PerplexityPerplexityShare of Perplexity answers that name this brand
Google AI OverviewsGoogle AI OverviewsShare of Google AI Overviews answers that name this brand
CoCounsel (Thomson Reuters)
75.9
80.6
87.0
70.4
67.0
Spellbook
45.4
92.6
83.3
50.0
67.9
Harvey
50.0
94.4
91.7
46.3
43.4
4
Lexis+ AI (LexisNexis)
66.7
70.4
71.3
39.8
59.4
5
Luminance
8.3
58.3
31.5
13.9
16.0
6
Legora
27.8
62.0
8.3
16.7
1.9
7
Everlaw
9.3
19.4
20.4
7.4
7.5
8
Robin AI
0.9
9.3
37.0
4.6
0.0
How we measurePowered by daydream

Every question changed this edition. The previous editions asked a set we wrote for the category; this one asks questions derived from what buyers actually searched or posted, so a month-over-month figure would compare two different questions and read as movement. Comparison returns next edition, when there are two mined runs to set against each other.

Framing sensitivity

The same question asked at each company size

Share of answers naming the brand

SMBSmall firm / soloENTERPRISELarge law firmLexis+ AI (LexisNexis)67.0%89.4%Harvey43.0%97.8%CoCounsel (Thomson Reuters)86.0%97.8%Legora11.2%45.3%Luminance20.1%45.3%Spellbook81.6%49.2%Everlaw6.1%32.4%Robin AI6.7%5.0%

BRANDS

SMBShare of answers naming this brand when the question is asked at this company sizeSmall firm / solo

ENTERPRISEShare of answers naming this brand when the question is asked at this company sizeLarge law firm

SPREADThe gap between the highest and lowest column in this row, in percentage points

Harvey
43.0%
97.8%
54.7%
Legora
11.2%
45.3%
34.1%
Spellbook
81.6%
49.2%
32.4%
Everlaw
6.1%
32.4%
26.3%
Luminance
20.1%
45.3%
25.1%
Lexis+ AI (LexisNexis)
67.0%
89.4%
22.3%
CoCounsel (Thomson Reuters)
86.0%
97.8%
11.7%
Robin AI
6.7%
5.0%
1.7%
How we measurePowered by daydream

Model divergence

The engines that name a brand least and most often, and the gap between them

BRANDS

LOWEST ENGINEThe engine that names this brand least often, and its share

HIGHEST ENGINEThe engine that names this brand most often, and its share

SPREADThe gap between the highest and lowest column in this row, in percentage points

Legora
1.9Google AI OverviewsAI Overviews
62.0ClaudeClaude
60.2
Harvey
43.4Google AI OverviewsAI Overviews
94.4ClaudeClaude
51.0
Luminance
8.3ChatGPTChatGPT
58.3ClaudeClaude
50.0
Spellbook
45.4ChatGPTChatGPT
92.6ClaudeClaude
47.2
Robin AI
0.0Google AI OverviewsAI Overviews
37.0GeminiGemini
37.0
Lexis+ AI (LexisNexis)
39.8PerplexityPerplexity
71.3GeminiGemini
31.5
CoCounsel (Thomson Reuters)
67.0Google AI OverviewsAI Overviews
87.0GeminiGemini
20.1
Everlaw
7.4PerplexityPerplexity
20.4GeminiGemini
13.0
How we measurePowered by daydream

Citation trail

The sites the engines linked to when they answered

SOURCES

REACHShare of all answers that cited this source at least once% of all answers

LINKSEvery link to this source, counted across all answerstotal

DEPTHLinks to this source per answer that cited itlinks/answer

gc.ai
823
2.7
spellbook.com
433
1.6
aivortex.io
633
3.0
vaquill.ai
441
2.1
reddit.com
205
1.1
legal.thomsonreuters.com
196
1.1
americanbar.org
174
1.0
clio.com
217
1.3
haqq.ai
239
1.6
thelegalprompts.com
272
1.9
lexisnexis.com
178
1.4
thelawgpt.com
217
1.7
legesgpt.com
156
1.3
layer3labs.io
190
1.6
eve.legal
160
1.6
xantrion.com
110
1.2
mycase.com
94
1.0
darrow.ai
89
1.0
lumay.ai
96
1.2
legalaiinsight.com
89
1.2
How we measurePowered by daydream

More in Security and compliance

Compliance automation

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Which questions do you win vs lose?

Get your brand’s prompt-level results

How we measure this3 of 12 questions published

We write a fixed set of category questions, none of which names a brand, and count how often each brand comes up. A brand is present in an answer only when one of its names literally appears in the text, because a string match cannot hallucinate.

Every question starts from one somebody already asked. Some come from search demand, where the figure is how many people typed that phrasing in a month. The rest come from a public forum post, and we link to the page it was written on. We rewrite each one into a plain question, because a search string is a fragment and a forum post is written the way people type, and neither is a fair thing to ask an engine. We change the wording and never the subject, and the original is published beside every question we disclose. The set is frozen before a single answer is collected, so every engine and every edition is asked exactly the same thing.

12questions scored
3buyer framings each
36prompts run
5engines

The 3 we publish, of 12

Chosen to span both where the questions come from and how they are shaped, so the sample describes the battery rather than one corner of it. That is 25% of the questions the ranking comes from.

  • What is the best AI for legal researchas asked: best ai for legal research670/mocategoryawareness
  • What is the best AI model for legal workas asked: best ai model for legal30/mocategoryawareness
  • What are the top legal AI companiesas asked: top legal ai companies60/mocategoryawareness

Each is asked 3 ways

  • … for a large law firm with hundreds of attorneysLarge law firm
  • … for a small law firm with fewer than 20 lawyersSmall firm / solo
  • … for a lean in-house legal team on a tight budgetIn-house on a budget

Why the rest stays private

A published battery invites brands to write pages against the exact wording, at which point a score moves without the brand’s actual standing moving and the measurement stops describing anything. Benchmark suites keep a held-out set for the same reason. The method is public so it can be judged, a quarter of the questions are public so it can be checked, and the rest stays private so the numbers stay worth checking.

What the numbers are, and aren’t

  • The August 2026 edition is one dated run, not a rolling average.
  • Share is averaged across the engines rather than pooled, so an engine that returned fewer answers cannot look like a brand losing ground.
  • A move is marked only when a two-proportion test puts it outside what the sample size can explain. A few points between neighbouring brands is a tie.
  • Rankings reflect how often AI engines name a brand. They are not endorsements by daydream.
  • 8 further questions name competitors directly. None of them feeds the ranking, because a question that already names the contenders cannot measure who gets recommended. They do count towards the citation trail, which is measured over every answer we collected rather than the unbranded ones alone.