B2B marketing operates under very different constraints than B2C. Purchasing decisions are higher value, involve more stakeholders, and have longer buying cycles. That means success depends less on broad reach and more on precision: engaging the right people, at the right businesses, at the right time.

 

That precision starts with firmographic data. In practice, who to market to is usually determined by a small set of signals: job title, location, industry, and company size. When this information is incomplete or missing, businesses that could be ideal targets are often overlooked, limiting campaign effectiveness and leaving opportunities on the table.

 

Much of the firmographic detail marketers need already exists in unstructured company text — website content, product descriptions, “about” pages, case studies, press releases, and public communications. Historically, extracting reliable insights from this material at scale was difficult. Rule-based approaches and keyword matching struggled with nuance, context, and the wide variation in how companies describe themselves.

 

Advances in large language models are changing that.

 

By combining LLMs with structured enrichment pipelines, it is now possible to infer firmographic attributes such as industry focus, approximate company size, operating model, and market presence directly from company-authored text. These pipelines are designed for accuracy and reliability: each step includes prompt engineering to guide the model, confidence scoring to measure certainty, validation against known data, and reconciliation to prevent duplicate or conflicting records. The outputs are not just descriptive — they are structured, verifiable, and ready for use in operational decision-making.

 

With enriched data in place, the possibilities extend beyond simple categorisation.

 

Agentic AI systems can layer campaign-specific decision logic on top of these enriched datasets. Instead of filtering broadly by industry or size, AI agents can evaluate whether a company aligns with the actual business problem a campaign aims to solve. This evaluation can incorporate multiple dimensions simultaneously — market presence, growth signals, product relevance, or strategic priorities — effectively replicating human judgment at scale. This allows marketers to target the right companies with precision, prioritising those most likely to engage and convert.