Case Study: Improving SME Market Intelligence Through Transparent AI and Web Data.
For decades, the Small and Medium-sized Enterprise (SME) sector has represented a significant growth opportunity for the financial services and insurance industries. However, despite making up the vast majority of the business population, SMEs have remained one of the most difficult segments to track and understand. Traditionally, the lack of structured data has forced organisations to rely on fragmented ‘signals' or outdated company databases, leading to inefficient outreach and higher risk profiles.
Recent breakthroughs in AI, Large Language Models (LLMs), and machine reading are fundamentally changing this landscape. At Glass.AI, our AI technology is designed to bridge this information gap by mapping the digital footprint and activities of millions of SMEs in real-time.
The Challenge of “Invisible” Data About Businesses
Unlike large public corporations that file quarterly reports and maintain high-profile media presences, the activities of SMEs are often “hidden” within the open web. Their most critical growth and risk signals are scattered across disparate sources:
Company websites and news mentions.
Job boards and hiring announcements.
Social media activity and e-commerce platforms.
Product launches and geographical expansion signals.
Historically, this data was too unstructured and voluminous for manual discovery at scale or tracking. For example, sales and marketing teams were often left “chasing ghosts”, relying on high-level company databases or sales intelligence tools rather than evidence-based insights on SMEs.
Beyond LLMs and Company Databases: A Structural Approach to SME Research
While many organisations are exploring generative AI, at Glass.AI, we apply a structural, transparent approach to business research. By combining deep web crawling with business ontologies, our AI moves beyond simple keyword matching to understand context and intent.
This allows for the continuous monitoring of the SME ecosystem to:
Detect Real-World Events: Identify office openings, new product launches, international expansion, key customer wins or leadership changes as they happen.
Normalise Unstructured Data: Transform messy web content into structured, usable intelligence for CRM and risk models.
Ensure Verifiable Insights: Unlike “black box” AI models, our approach provides a clear path back to the source data, ensuring compliance and data provenance.
From Guesswork to Precision
The application of AI-driven signal monitoring has shown material results in the financial sector. As an example, Capital One in the United States was able to engage with SMEs at the precise moment of a “trigger event” (such as a new product launch or office opening), and this led to a 160% increase in booking rates for proactive sales channels.
By shifting from static databases to a living company data ecosystem, financial services organisations can achieve:
Enhanced Risk Modelling: Identifying healthier businesses earlier to lower delinquency rates.
Operational Efficiency: Reducing costs of acquisition by focusing resources on high-intent prospects.
Personalisation at Scale: Engaging business owners with messages that reflect their current operational reality.
The Shift Toward Evidence-Led Decision Making
The SME market has not changed, but our capacity to observe and track it has. The transition from “guessing” to “knowing” allows financial institutions like Capital One and others to see SMEs as the dynamic, evolving entities they truly are.
As we continue to map and track the SME ecosystems across countries, the organisations that succeed will be those that leverage AI to gain a clearer understanding of the sectors they serve. At Glass.AI, we remain committed to providing the data infrastructure that anchors these strategic decisions.