AI Tool Feature Matrix Builder
Pricing
from $10.00 / 1,000 dataset items
AI Tool Feature Matrix Builder
Build compact AI product feature rows for vendor comparison matrices.
Create clean AI-tool feature rows for comparison matrices, vendor research, and sales battlecards.
Who Uses It
AI consultants, product marketers, procurement teams, founders, and analysts comparing AI software.
Why It Is Useful
This actor does not save a generic web-scrape dump. It saves a compact dataset shaped around one business job, so the output is easier to review, export, and plug into research workflows.
Input
startUrls- Public pages to inspect.logLines- Pasted snippets for fast tests or sources that do not need crawling.maxPagesPerStartUrl- Same-domain pages to inspect from each URL.maxResults- Maximum clean rows to save, capped at 20.proxyConfiguration- Optional Apify proxy settings.
Output Fields
productName- Detected AI product or vendor name.capability- Feature or capability being compared.capabilityType- Feature category such as API, agent workflow, security, or AI workflow.apiSupport- Whether API access is mentioned.teamControls- Whether team, admin, security, or enterprise controls are mentioned.enterpriseSignal- Enterprise-readiness phrase found in the content.monetizationSignal- Pricing or monetization mention tied to the feature.featurePage- Source feature page.
Example Output
{"productName": "exampleai","capability": "Agent workflow automation","capabilityType": "Agent workflow","apiSupport": "yes","teamControls": "yes","enterpriseSignal": "security controls mentioned","monetizationSignal": "$49/month","featurePage": "https://example.com/features"}
Use Cases
- Compare AI agents, APIs, workflow, team, and security capabilities.
- Build feature matrices for buyer research.
- Track enterprise-readiness signals across tools.
Limits
The actor works best with public pages or focused pasted snippets. Login walls, CAPTCHA, heavy anti-bot protection, and very noisy pages can reduce result quality.
Pricing
Recommended pricing is paid per saved dataset item, so users pay for useful rows instead of empty page visits.