Nimble $47M addresses AI agent web data reliability crisis
Nimble raised $47 million in Series B funding led by Norwest to solve a critical bottleneck in enterprise AI agent deployment: reliable access to real-time, structured web data that agents can actually trust for business decisions.
The New York-based company addresses what CEO Uri Knorovich calls the core enterprise AI failure mode: “Most production AI fails aren’t because the models are not good enough — it’s because of a data failure.” While AI agents excel at web search and analysis, they typically return unstructured text prone to hallucinations and unreliable sourcing, creating an insurmountable trust gap for enterprise deployment.
Problem: Web Data Trust Crisis
Enterprise AI agents face a fundamental reliability problem when accessing web data. Current approaches return search results as unstructured text, making it difficult to validate sources, detect hallucinations, or integrate findings with existing enterprise data systems.
This data quality crisis becomes particularly acute for use cases like competitor analysis, pricing research, KYC processes, and financial analysis—scenarios where data accuracy directly impacts business outcomes. Without structured, validated web intelligence, enterprises cannot confidently deploy agents for mission-critical workflows.
Solution: Multi-Agent Data Validation Architecture
Nimble employs AI agents to search the web in real time, then validates and structures the results into queryable database tables. This multi-layer approach combines web intelligence gathering with data quality assurance, delivering enterprise-grade reliability.
The platform integrates directly with existing data infrastructure including Databricks and Snowflake data warehouses, allowing validated web data to appear alongside internal enterprise data. Agents can remember constraints like preferred data sources and search parameters, enabling consistent, governed data collection processes.
Crucially, Nimble maintains customer data within customer environments, addressing data sovereignty requirements that often block enterprise AI adoption. The system supports custom validation rules and source whitelisting, giving enterprises fine-grained control over agent web access.
Evidence of Enterprise Adoption
Nimble serves over 100 customers, with the majority being Fortune 500 companies including major retailers, hedge funds, banks, and consumer packaged goods companies. The platform has also attracted AI-native startups building agent-powered products.
The company has established partnerships with major enterprise data platforms including Databricks, Snowflake, AWS, and Microsoft, streamlining deployment for enterprises already using these systems. Databricks participated in the Series B funding round, indicating strategic validation of the approach.
Target Global, Square Peg, Hetz Ventures, Slow Ventures, and other existing investors joined the round, bringing total funding to $75 million.
Implications: Structured Web Intelligence Standard
Nimble’s approach suggests the emergence of “web intelligence as a service” as a distinct infrastructure category. Rather than expecting enterprises to build reliable web data pipelines internally, specialized providers are creating governed, enterprise-ready web intelligence layers.
This represents a shift from ad hoc web scraping toward systematic, validated web data integration. As Norwest partner Assaf Harel noted, “Trusted live web data is increasingly becoming a prerequisite for AI agents performing critical business decisions.”
The multi-agent validation approach also establishes a template for addressing AI reliability concerns: rather than relying on single model outputs, deploy multiple AI systems to cross-validate and structure results before presenting them to enterprise users.
Looking Forward: Governed Agent Internet Access
Over the next 12-18 months, expect structured web intelligence to become standard infrastructure for enterprise AI agent deployments. Companies will increasingly require validated, traceable web data rather than accepting unstructured agent search results.
The success of Nimble’s validation-first approach may accelerate development of similar governance layers for other agent data sources, establishing reliability as a key differentiator in enterprise AI infrastructure rather than raw capability alone.
This infrastructure evolution reflects the broader maturation of enterprise AI deployment, where operational reliability increasingly matters more than pure model performance. As agents handle higher-stakes business functions, the quality of their data sources becomes as critical as their reasoning capabilities.
Platforms like Overclock complement this trend by providing the orchestration layer that coordinates validated data sources with agent reasoning workflows, enabling enterprises to deploy trustworthy AI agents at scale.