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Overall score
68.0OK
—
Rank
#146
Prev: #0
ecommerce avg
61.6
118 peers
Agent runs / week
1
Across 1 providers

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Locales
en
Opportunity cost · e-commerce

Weight could be leaving ~$5,440,000 in revenue on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of product discovery and checkout sessions in the e-commerce sector will be initiated or completed by AI agents[1][2]. Weight currently scores 68.0 on the Stunt Double Index[3], with a 32-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 e-commerce revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
32.0 pts
Above e-commerce avg
0.0 pts
Modelled revenue at risk
$5,440,000

Estimate assumes a $50M annual e-commerce revenue baseline. Claim your domain to replace this placeholder with your reported revenue.

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?
100
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
90
Market rankingWhere do you sit in the lineup?
45
AccuracyDo they tell the truth about you?
100
Task completionCan an agent complete a task on behalf of a user?
80
Delegated accessDo you let agents in, safely?
0
Contact & communicationCan an agent reach a human?
20

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
lowDiscovery
No llms.txt. You miss the emerging standard for giving agents a curated map of the site.
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.
mediumTask completion
No mention of delegated auth primitives (passkey, OAuth, MCP). Agents must drive a full browser session.
highDelegated access
No MCP manifest. Agents can’t auto-discover tools or delegated capabilities.
highDelegated access
No public API docs detected. Agents have no scoped way in; they must drive a browser.
lowDelegated access
robots.txt doesn’t mention any agent user-agents. The policy for agents is implicit.
highContact & communication
No contact page linked or reachable. Agents acting on a user’s behalf have nowhere to send a message.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.