Plain-English queries for B2B contact search — replace filter UIs

Revenue and demand-generation teams at SMBs and mid-market companies struggle daily with brittle filter-driven UIs in sales intelligence tools: building a usable contact list often requires Boolean expertise, repeated refinement, and manual verification, which wastes time and lowers outreach success. The total addressable market is roughly 5.0M target companies at an average potential spend of $2,400 ARR (a $12.0B annualized market), so the pain is widespread and monetizable if the product meaningfully reduces time-to-list and improves contact quality. A solution would be a plain-English query interface that translates intent into deterministic, auditable search filters, backed by LLMs for natural language understanding and fast, real-time data pipelines for verification and freshness; the product should return explainable results, suggested refinements, and exportable workflows that replace multi-step filter UIs. Market timing is favorable: LLM-driven interfaces mean users increasingly expect conversational, intent-first workflows, and the market scores highly (market score 90/100, revenue potential 92/100) for rapid adoption if integration and accuracy are solved. To stand out you must combine robust semantic-to-filter translation with rigorous data provenance, permissioned contact verification, built-in compliance controls, and tight integrations into CRM and outreach stacks; that approach targets higher deliverability and trust, not just convenience. The challenges are real: competition is medium with incumbent databases and search products, data licensing and pipeline costs will be significant, and buyers will require measurable uplifts in conversion or time savings before switching, so execution and proof-of-value are critical.