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Local Car Hire and Carsharing

getaround.com
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Overall score
61.2OK
—
Rank
#272
Prev: #270
saas avg
41.7
360 peers
Agent runs / week
3
Across 0 providers

Rent a car in an instant, open the car with the Getaround app, #1 for carsharing! 24/7. 200mi/day and insurance included.

Markets
USATBENO
Locales
en-usfrdeesde-atfr-benl-beno-no
Opportunity cost · SaaS

Local Car Hire and Carsharing could be leaving ~$2,638,400 in new ARR on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of trial, signup and plan selection sessions in the SaaS sector will be initiated or completed by AI agents[1][2]. Local Car Hire and Carsharing currently scores 61.2 on the Stunt Double Index[3], with a 39-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 recurring revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
38.8 pts
Above SaaS avg
0.0 pts
Modelled new ARR at risk
$2,638,400

Estimate assumes a $20M ARR baseline with agent-sourced expansion. Claim your domain to replace this placeholder with your reported new ARR.

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?
90
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
100
Market rankingWhere do you sit in the lineup?
35
AccuracyDo they tell the truth about you?
90
Task completionCan an agent complete a task on behalf of a user?
30
Delegated accessDo you let agents in, safely?
12
Contact & communicationCan an agent reach a human?
60

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
0.0
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.
lowAccuracy
No privacy policy linked from the homepage. Agents can’t cite policy when asked.
mediumTask completion
No visible primary call-to-action in the server-rendered HTML. Agents have nothing concrete to click on behalf of a user.
highTask completion
No reachable entry point for delegated tasks — nothing at `/pricing`, `/signup`, `/cart`, `/book`, or similar responds without auth.
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.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.