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Robinhood

robinhood.com
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Overall score
72.3Good
—
Rank
#86
Prev: #67
finance avg
61.2
54 peers
Agent runs / week
2
Across 1 providers

Trade stocks on Robinhood with commission-free investing & advanced trading tools. Access fractional shares, real-time market data, and more. Terms and fees may apply.

Audience
Developer
Markets
USGB
Locales
enen-usen-gb
Opportunity cost · finance

Robinhood could be leaving ~$47,090,000 in processed value on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of account opening and transaction initiation sessions in the finance sector will be initiated or completed by AI agents[1][2]. Robinhood currently scores 72.3 on the Stunt Double Index[3], with a 28-point gap to the ideal agent experience (100). The loss figure below applies that gap to the projected agent-driven slice of a typical annual processed transaction valuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
27.7 pts
Above finance avg
0.0 pts
Modelled flows at risk
$47,090,000

Estimate assumes a $500M annual processed-value baseline. Claim your domain to replace this placeholder with your reported processed value.

Sources
  1. [1]Gartner (2025) — Intelligent Agents in AI Really Can Work Alone. Here’s How.
  2. [2]Adobe Analytics (2025) — Generative AI traffic to U.S. retail sites (weekly trend)
  3. [3]Stunt Double (2026) — Stunt Double Index methodology & scoring rubric

Category breakdown

Brand awarenessCan agents recognise you exist?
80
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
85
Market rankingWhere do you sit in the lineup?
45
AccuracyDo they tell the truth about you?
80
Task completionCan an agent complete a task on behalf of a user?
70
Delegated accessDo you let agents in, safely?
100
Contact & communicationCan an agent reach a human?
50

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
100.0
Gemini
Google
0.0
Perplexity
Perplexity
Copilot
Microsoft
Browserbase Operator
Browserbase

Where agents get stuck

Public summary · full session replay available to verified owners
mediumBrand awareness
No Organization JSON-LD. Agents can’t tell who owns the site at a glance.
lowDiscovery
No llms.txt. You miss the emerging standard for giving agents a curated map of the site.
mediumInformation retrieval
No JSON-LD structured data. Agents must infer entities from markup.
mediumMarket ranking
No Product/Service schema on the homepage. Agents can’t easily compare offerings.
lowMarket ranking
No review or aggregateRating schema. Agents have no signal for ranking vs peers.
mediumAccuracy
No JSON-LD at all. Every factual claim must be inferred from prose.
lowTask completion
No server-rendered form detected. Agents without JavaScript can’t submit anything.
mediumTask completion
No mention of delegated auth primitives (passkey, OAuth, MCP). Agents must drive a full browser session.
lowDelegated access
robots.txt doesn’t mention any agent user-agents. The policy for agents is implicit.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.