Expert in e-commerce strategy and operations management, Zainab Hussain, provides a deep dive into the evolution of web data intelligence. With the rise of agentic search and real-time data processing, she explains how enterprises are moving away from fragile web scraping toward governed, multi-agent systems that treat the live internet as a structured database. This shift is redefining how organizations handle high-stakes decisions in retail, finance, and beyond.
The following discussion explores the recent $47 million funding milestone for agentic search, the transition from manual research to automated workflows, and the technical frameworks required to integrate live web data into production-ready AI systems.
With $47 million recently secured to scale agentic search, how will these funds specifically accelerate the development of multi-agent systems? What technical milestones should enterprises look for as this governed data layer expands into production environments?
The injection of $47 million, bringing total funding to $75 million, is primarily earmarked for moving beyond simple text summaries toward a sophisticated, multi-agent architecture. This funding allows us to refine a loop where one agent specializes in searching, a second focuses on verifying the integrity of the web data, and a third executes the final business action. For enterprises, the most critical milestone is the shift from “experimental” AI to a “governed data layer” that can handle millions of automated actions per day. Businesses should look for the ability to process, clean, and deduplicate data at scale, moving away from high-level snippets to schema-first tables that feel like querying a local database.
Traditional web scraping often requires heavy maintenance and lacks verifiability for high-stakes decisions. How does converting live internet data into structured, queryable tables solve the problem of inaccuracy in AI agents, and what specific protocols ensure this data remains auditable for financial or legal due diligence?
Traditional scraping is fragile because it breaks whenever a website’s UI changes, but structured tables provide a stable, schema-first foundation that AI agents can actually “understand” and rely on. By turning the live web into curated tables, we eliminate the guesswork and hallucinations common in frontier models that try to summarize messy HTML. To ensure this data is bank-grade for legal or financial due diligence, we implement a governed layer that cross-checks results step-by-step, ensuring every data point is reproducible and verifiable. This creates an audit trail where a financial analyst can see exactly where a piece of intelligence originated, transforming the web into a reliable, enterprise-grade knowledge base.
Retailers have historically taken weeks to adjust strategies based on competitor pricing. When pricing intelligence is delivered in minutes rather than weeks, how does that fundamentally change the internal decision-making loop, and can you share an anecdote regarding the organizational impact of this increased speed?
The compression of the decision loop from weeks to minutes fundamentally shifts a retailer’s posture from reactive to proactive. In the past, by the time a pricing report reached a director’s desk, the market had already moved, rendering the data a historical artifact rather than a strategic tool. I recall how industry leaders like those at Lululemon have noted that this speed puts control directly into the hands of the business units rather than the IT department. When a competitor drops a price on a Tuesday morning, an automated agentic system can alert the team and suggest an optimized response by lunch, potentially saving the company tens of millions of dollars in lost sales or margin erosion annually.
Integrating live web data with internal datasets through platforms like Databricks and Microsoft is a significant hurdle. What are the technical requirements for streaming this data directly into existing business apps, and how do you manage the “engineering tax” typically associated with these complex workflows?
The primary technical requirement is an interoperable data layer, which is why we utilize integrations like Delta Sharing to bridge the gap between the live web and internal intelligence platforms. To eliminate the “engineering tax”—those thousands of hours developers spend maintaining brittle scrapers—we provide no-code AI workflow builders and robust SDKs. This allows teams to connect web data directly to their day-to-day business apps via API, removing the need for custom-built extraction infrastructure. By operationalizing these models from OpenAI, Anthropic, and Meta, we ensure that the “plumbing” of data collection is handled automatically, letting engineers focus on building features rather than fixing broken crawlers.
While many systems use frontier models for summarization, using them to control real browsers is a different challenge. How do multimodal capabilities improve the navigation of dynamic website layouts, and how do you ensure coordinated agents cross-check results effectively when a site’s structure changes?
Multimodal capabilities allow our agents to “see” and interact with a website much like a human would, which is essential for navigating dynamic layouts that rely heavily on JavaScript or complex visual elements. Instead of just reading text, these agents use reasoning to understand button placements, pop-ups, and nested menus, ensuring they don’t get stuck when a site updates its CSS. To maintain accuracy, we deploy coordinated agents that work in parallel; if one agent encounters a structural change, the governing layer detects the anomaly and triggers a cross-check against other sources. This redundant verification ensures that even if a site’s structure is overhauled, the output remains consistent and trustworthy.
The shift toward agentic search allows non-technical teams to build web workflows without deep coding knowledge. What are the practical steps for a business to transition from manual research to an automated multi-agent system, and what common pitfalls should they avoid during the initial implementation phase?
The transition begins with identifying a high-frequency, manual task—such as daily competitor tracking or social listening—and mapping it into a no-code workflow builder. Once the “search” and “extract” steps are defined, the team should implement a verification agent to gate the data before it hits their CRM or ERP. A common pitfall is trying to automate the most complex, subjective research tasks first; instead, businesses should start with data-heavy, objective workflows where the ROI is easily measurable in hours saved. Another mistake is ignoring the governance aspect, so it is vital to ensure that any automated system includes a layer for cleaning and aggregating data to avoid flooding internal systems with “dirty” or redundant information.
What is your forecast for the future of AI-driven web data intelligence?
I predict that within the next three to five years, the concept of “searching the web” will become entirely invisible for the modern enterprise, replaced by a continuous, self-healing data stream that feeds directly into every decision-making tool. We will move away from consumers clicking links and toward a reality where “agentic search” is the standard plumbing for all business intelligence, making real-time web data as accessible as a local Excel file. As these multi-agent systems become more autonomous, the gap between market events and organizational responses will shrink to near zero, creating a hyper-efficient global marketplace where information asymmetry is virtually eliminated for those using these tools.
