Nimble raises $47M to scale agentic web search platform for enterprise AI

2026-02-24 · Source: AI – SiliconANGLE · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Nimble, founded in 2021, has secured an additional $47 million in Series B funding, bringing its total raised to $75 million. This capital will accelerate the development of its agentic web search platform, expand multi-agent research, and scale its governed real-time web data infrastructure for enterprise AI deployments. The platform addresses the challenge of obtaining structured, verifiable data from the live public internet, which is crucial for AI systems that often rely on static or unstructured datasets. Nimble's technology coordinates AI-driven agents to browse live websites, extract information, and convert dynamic web content into structured, schema-first datasets. It features a no-code workflow builder and an SDK, supporting applications like financial due diligence and market research, and integrates with platforms from Databricks and Microsoft. Fortune 500 companies, including Uber, Coca-Cola, and Deloitte, are among its customers.

Key takeaway

For CTOs and AI Architects deploying enterprise AI, Nimble's platform offers a solution for integrating verifiable, real-time web data into your systems. Relying on static or unstructured data introduces significant risks; consider adopting agentic web search to ensure your AI applications operate with current, auditable, and structured external intelligence, especially for high-stakes decisions.

Key insights

Nimble's agentic platform provides verifiable, structured, real-time web data for enterprise AI systems.

Principles

• AI agents can control full web browsers.
• Governed data layers ensure data quality.
• Live web data is critical for AI agents.

Method

AI models control web browsers to navigate, interact, and extract data from live sites. A governed data layer then cleans, deduplicates, and aggregates this information into structured tables for enterprise AI integration.

In practice

• Automate web data tasks with no-code workflows.
• Integrate structured web data into BI tools.
• Use for financial due diligence or pricing analysis.

Topics

• Agentic Web Search
• Enterprise AI
• Real-time Data
• Multi-Agent Systems
• Data Governance

Best for: CTO, VP of Engineering/Data, AI Architect, Director of AI/ML, AI Product Manager, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.