Senro Inc.
Senro builds reliability infrastructure for the agentic web. Its platform evaluates WebMCP tools across models, languages, prompts and scenarios before deployment, then monitors how AI agents use those tools in production.
At a Glance
- Developers and engineering teams building WebMCP-enabled websites
- Companies making websites agent-native
- Teams that need cross-model and cross-language agent evaluation
- Organizations operating production WebMCP tools and needing observability
AI Tools by Senro Inc.
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Senro
WebMCP Testing and Observability
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Latest News
Senro became publicly available; founder Nart Madi announced the WebMCP evals and observability platform on Hacker News.
Published How to Build WebMCP Tools for Meta Ray-Ban Display, covering Meta AI tool invocation, schemas, results, testing and voice-driven workflows.
Published How WebMCP Works: A Complete Guide, with practical coverage of JavaScript and HTML tool exposure, discovery, execution, state and security.
Published WebMCP vs. MCP, positioning WebMCP as the live-browser interface and MCP as the application-to-backend protocol.
Products & Services
A SaaS platform for testing WebMCP tools before production and monitoring agent interactions after deployment. It supports goal, trajectory and page-check evaluation, simulated users, smoke tests, multi-model comparison, production tool activity, inputs, results, errors, session timelines, production context, issue tracking, and usage/error metrics.
Market Position
Senro positions itself as a specialized reliability layer for WebMCP—the browser-native interface for AI agents—rather than a general-purpose LLM observability product. Its differentiation is the combination of WebMCP-specific evaluation of tool selection, parameter accuracy, trajectories and outcomes with production monitoring; adjacent alternatives include broader agent/LLM evaluation and observability platforms such as Braintrust, LangSmith, Arize and Langfuse, which are not positioned specifically around WebMCP websites.
Leadership
Founders
Nart Madi
Entrepreneur and engineer; founder and CEO of Senro. He holds an M.S. in Data Analytics and a B.S. in Computing and Information Technologies from Rochester Institute of Technology, completed 42 School's computer-science program, and previously worked as a Tech Lead at Kubar and as an AI Engineer Intern at ZainTECH/Ijaba AI. His prior work included machine-learning cloud-cost optimization and RAG/GraphRAG systems.
Executive Team
Nart Madi
Founder & CEO
Entrepreneur and engineer with prior Tech Lead, AI/ML Engineer and Software Engineer experience at technology startups in the U.S. and UAE; previously Tech Lead at Kubar and AI Engineer Intern at ZainTECH/Ijaba AI. He studied data analytics and computing at Rochester Institute of Technology and completed 42 School's computer-science program.
Founding Story
Madi says he started Senro after learning about WebMCP and asking how developers would evaluate WebMCP tools and monitor them in production. The initial vision was to provide goal-oriented and trajectory evaluations across LLMs plus production monitoring, making the AI-agent side of websites reliable.
Business Model
Revenue Model
Subscription SaaS with free and paid monthly plans, plus custom enterprise arrangements. Model-token costs are handled through customers' own API keys (BYOK) for multi-model runs.
Pricing Tiers
5 WebMCP pages, 250 evaluations/month, 25,000 events/month, 7-day observability retention, 30-day evaluation history, goal/trajectory/page-check testing, simulated users, smoke testing, multi-model runs and comparison (BYOK), production observability, community support.
10 WebMCP pages, 5,000 evaluations/month, 1,000,000 events/month, 30-day observability retention, 365-day evaluation history, the Free testing and observability capabilities, and email support.
Everything in Pro plus team seats, custom limits and retention, custom SLA and security, guided pilot and onboarding, and priority support.
Target Markets
- Developers and engineering teams building WebMCP-enabled websites
- Companies making websites agent-native
- Teams that need cross-model and cross-language agent evaluation
- Organizations operating production WebMCP tools and needing observability
- Pre-production testing of websites exposing WebMCP tools
- Regression testing of browser-agent tool selection and arguments
- Evaluating task completion across LLMs, languages, prompts and page states
- Simulating users and agent trajectories
- Production observability and debugging of AI-agent interactions with websites
- Building agent-native websites and Web Apps, including WebMCP experiences for Meta Ray-Ban Display