Artificial intelligence is transforming enterprise workflows at an unprecedented pace. Yet despite the rapid rise of AI agents and large language models (LLMs), one persistent problem continues to limit their effectiveness: unreliable, unstructured web data.
Enter Nimble Way, a New York–based web search startup that has raised $47 million in Series B funding to solve precisely this issue. The round was led by Norwest Venture Partners and brings Nimble Way’s total funding to $75 million.
The company’s mission is straightforward yet ambitious: give AI agents access to real-time, validated, structured web data that enterprises can trust and use at scale.
In an era where AI-driven decision-making increasingly relies on external information, Nimble Way aims to bridge the gap between live web content and production-grade enterprise data systems.
Why AI Agents Struggle With Web Data
Large language models are powerful. They can:
- Crawl and summarize information
- Connect disparate sources
- Generate insights
- Answer complex queries
However, most AI systems return results in plain text. For enterprises, this presents several challenges:
- Unstructured Output – Text summaries are difficult to integrate into analytics tools.
- Hallucinations – Models may generate inaccurate or unverifiable data.
- Source Reliability Issues – AI may pull from questionable websites.
- Limited Governance Controls – Enterprises need strict oversight on data usage.
In production environments — especially in finance, retail, or compliance-heavy industries — these weaknesses can become critical failures.
As Nimble Way CEO and co-founder Uri Knorovich explains, the issue often isn’t the model itself. It’s the data layer supporting it.
Nimble Way’s Core Innovation: Structured, Governed Web Search
Nimble Way deploys AI agents that:
- Search the web in real time
- Verify and validate results
- Cross-reference multiple sources
- Structure outputs into clean, database-ready tables
Instead of delivering paragraphs of text, the platform returns neatly formatted data that enterprises can query like an internal database.
This distinction is crucial.
Enterprises operate on structured data — tables, columns, metrics, and relationships. By converting web data into structured formats, Nimble Way makes live web intelligence usable within modern data stacks.
Turning the Open Web Into a Queryable Database
The true power of Nimble Way’s platform lies in its integration capabilities.
The company integrates with major enterprise data platforms, including:
- Databricks
- Snowflake
- Amazon Web Services
- Microsoft
These integrations allow enterprises to plug Nimble Way’s AI search agents directly into their existing:
- Data warehouses
- Data lakes
- Analytics pipelines
In practice, this means:
- AI agents can reference internal business data to add context.
- Web results are structured according to company-specific rules.
- Search constraints and compliance requirements are preserved.
For example, if a retailer wants to monitor competitor pricing, Nimble Way’s agents can:
- Search relevant websites in real time.
- Extract verified pricing information.
- Structure it into standardized tables.
- Feed it directly into internal dashboards.
The web effectively becomes an extension of the company’s internal database.
Why Structured Web Data Is a Competitive Advantage
In today’s AI-driven enterprise environment, access to reliable, live web data can unlock several strategic use cases:
1. Competitor Intelligence
Track pricing, product launches, and promotions across markets.
2. Brand Monitoring
Analyze public sentiment, customer feedback, and media coverage.
3. Financial Research
Gather real-time market indicators, company announcements, and regulatory updates.
4. Know-Your-Customer (KYC)
Validate business information across public registries and digital footprints.
5. Hedge Fund Analysis
Aggregate alternative data sources for trading insights.
In each scenario, raw text summaries are insufficient. Enterprises need structured, validated datasets that plug seamlessly into analytics tools.
Governance and Data Security at the Core
One of the biggest concerns enterprises have when deploying AI agents is data governance.
Questions include:
- Where is customer data stored?
- Who controls retention policies?
- Can AI agents access restricted sources?
- Are search parameters enforceable?
Nimble Way addresses these concerns by ensuring that:
- Customer data remains within the enterprise’s infrastructure.
- Search constraints are configurable and enforceable.
- Data pipelines comply with retention and security policies.
This governed approach differentiates Nimble Way from basic web scraping services or generic AI research tools.
The Series B Funding and Strategic Backers
Nimble Way’s $47 million Series B round underscores growing investor confidence in the structured web data market.
The round was led by Norwest Venture Partners, with participation from:
- Target Global
- Square Peg
- Hetz Ventures
- Slow Ventures
- R-Squared Ventures
- J-Ventures
- InvestInData
Notably, Databricks also participated in the round, reinforcing the strategic alignment between structured web search and modern data platforms.
According to Assaf Harel, partner at Norwest, trusted live web data is becoming a prerequisite for AI agents making critical business decisions.
A Growing Enterprise Customer Base
Nimble Way already serves more than 100 customers, with a majority of revenue coming from:
- Fortune 500 companies
- Fortune 10 corporations
- Major retailers
- Hedge funds
- Banks
- Consumer packaged goods companies
- AI-native startups
This broad customer base signals strong demand for governed web search infrastructure.
For enterprises operating at scale, unreliable web data isn’t just inconvenient — it’s a business risk.
Multi-Agent Web Search: The Next Frontier
The company plans to use the new funding to expand research and development in:
- Multi-agent web search
- Governed data layers
- Automated validation systems
Multi-agent systems represent the next evolution of AI search.
Rather than a single AI agent performing tasks sequentially, multiple specialized agents can:
- Divide research responsibilities
- Cross-validate findings
- Monitor data consistency
- Escalate anomalies
This architecture improves both accuracy and scalability.
The Real Problem: Data Failure, Not Model Failure
Knorovich makes a compelling argument: most production AI failures stem from data weaknesses, not model inadequacies.
Even the most advanced AI systems struggle if:
- Inputs are inconsistent
- Sources are unreliable
- Constraints are unclear
- Data lacks structure
Enterprises don’t necessarily need “more AI.” They need AI that can trust its data.
Nimble Way’s strategy focuses on building a reliable, structured, and governed web data layer — the missing link between LLM capability and enterprise deployment.
Differentiation From Traditional Data Brokers
The web data space is not new. Data brokers and scraping services have operated for years.
However, Nimble Way differentiates itself by:
- Enabling real-time web search at scale
- Structuring data automatically into enterprise-ready formats
- Integrating directly with major data warehouses
- Enforcing search governance controls
- Validating and cross-referencing results
Traditional scraping tools often deliver raw data dumps, leaving enterprises to clean and structure information themselves.
Nimble Way aims to eliminate that friction.
AI Agents in Production: The Trust Tipping Point
One of the most important ideas emerging from Nimble Way’s approach is the concept of controlled autonomy.
When enterprises can:
- Define where AI agents may search
- Specify which sources are allowed
- Structure output formats
- Retain full governance control
AI shifts from experimental to operational.
Knorovich suggests that once enterprises can restrict and guide agent behavior, AI adoption accelerates significantly.
Trust becomes the tipping point.
The Broader Implications for Enterprise AI
The rise of AI agents in business has created new infrastructure demands.
Companies now require:
- Real-time external intelligence
- Structured integration pipelines
- Auditability and compliance controls
- Secure data handling
Nimble Way’s funding round highlights a broader trend: AI infrastructure companies may prove just as important as AI model developers.
Without reliable data pipelines, even advanced AI systems remain underutilized.
Scaling Toward the Future
With $75 million raised to date, Nimble Way is positioned to expand aggressively.
Key growth priorities include:
- Enhancing validation algorithms
- Expanding enterprise integrations
- Improving multi-agent coordination
- Strengthening governed data frameworks
As enterprises embed AI agents deeper into core operations, the demand for trustworthy web intelligence will likely grow.
Industries such as finance, retail, healthcare, and manufacturing increasingly rely on dynamic external signals.
Real-time structured web data could become foundational infrastructure.
Conclusion: Making the Web Enterprise-Ready
The open web is vast, dynamic, and chaotic. AI agents are powerful but imperfect. Enterprises demand precision, governance, and reliability.
Nimble Way’s approach — combining real-time search, validation, and structured output — addresses a critical bottleneck in enterprise AI adoption.
By turning live web content into database-ready intelligence, the company is not just improving search — it is reshaping how AI agents interact with the external world.
As organizations seek to move from AI experimentation to full-scale production, structured and governed web data may be the key differentiator.
If Nimble Way succeeds, the web will no longer be just a source of information. It will function as a trusted, queryable extension of enterprise data systems — powering the next generation of AI-driven decision-making.
Photo Credit – Nimble Way