ProjectDiscovery Neo
by ProjectDiscovery · AI security agents for pentesting, PR review and fix retesting
BenchRank score
Screenshots of ProjectDiscovery Neo
Homepage
Overview
Neo, from the makers of Nuclei, runs AI agents that pentest applications, review pull requests, triage vulnerability backlogs and retest fixes. It tests web apps, APIs, cloud assets and third-party integrations in one loop, tracing attack paths and using runtime validation to confirm exploitability. Tasks run in isolated sandboxes, and agents can be launched in fleets across teams.
- Best for
- Security teams needing continuous pentesting and vulnerability backlog triage on fast-shipping apps
- Pricing
- No prices are shown in the captured text; the site has a pricing page that was not captured and directs visitors to book a demo.
- Runs on
- WebmacOSSelf-hosted
Strengths and trade-offs
Strengths
- Tests web apps, APIs, pull requests and cloud assets in one loop
- Runtime validation used to confirm exploitability and cut noise
- Triages and deduplicates backlog, and retests fixes when shipped
- Each task runs in an isolated sandbox
Trade-offs
- No pricing figures shown in the captured pages
- Entry route is a sales demo; no self-serve trial is shown
- The benchmark against other scanners is run by the vendor itself
- No named integrations, CI systems or deployment options given
How ProjectDiscovery Neo markets itself
A structured read of the promise, proof and page design on ProjectDiscovery Neo’s captured homepage.
Homepage capture
“Stay ahead of every exploit”
- Angle: Fear / risk avoidance
- Hero: Abstract graphic
Pricing
Published plans and prices from ProjectDiscovery Neo’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The ProjectDiscovery Neo pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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Is this your product?
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