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Saniya Sood
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Published July 27, 2026 · Updated August 24, 2026
The umbrella question: is there a tool where you just type “Series A B2B SaaS companies in the US whose founders post about outbound” - and it builds the list? Yes - natural-language lead search became real in 2025 - 26, replacing filter grids with plain-English ICP descriptions. Three architectures compete: search-infrastructure tools (Exa Websets-class) that query the web semantically, database front-ends that translate your sentence into filters, and signal-based platforms (Valley’s approach) where the description drives both expanded search and continuous qualification of live intent. Which one you want depends on whether you need a list - or meetings.
► Reading is step one; step two is a free 7-day Valley trial. See your own warm pipeline by tonight.
Give your sales team
an unfair advantage.
The old way to build a list was Boolean gymnastics: industry codes × headcount bands × title synonyms, each filter losing the companies that don't self-describe neatly. Natural-language search inverts it - you describe the concept ("companies that just hired their first sales leader," "agencies that serve e-commerce brands") and semantic search finds entities matching the meaning, including ones no filter taxonomy would catch. For messy, situational ICPs - which is most good ICPs - this is a genuine capability jump, not a UX skin.
The three architectures (and what each is actually for)
1. Semantic search infrastructure (Exa Websets-class). Web-scale semantic retrieval: type the description, get entities matching it from the open web. Strengths: reach beyond any database's taxonomy; developer-grade flexibility. Limits: output is a list - no qualification loop, no outreach layer, and results need verification like any scraped set. Best for: technical teams building custom pipelines. (Valley vs Exa Websets, head-to-head.)
2. Database natural-language front-ends. Plain-English input translated into filters over a contact database (Apollo-class tools ship versions of this). Strengths: instant, familiar, contact data attached. Limits: bounded by the same database and its staleness - the sentence is new; the rows aren't.
3. Signal-based platforms with expanded search (Valley). Your ICP description does two jobs: it becomes the scoring filter applied continuously to live intent (profile viewers, post engagers, site visitors - qualified or removed automatically), and via Deep search it expands discovery beyond your own orbit - plain-English-driven lead search with the qualification and outreach layers attached. That's the mechanical difference: the sentence doesn't produce a CSV; it produces researched, drafted, ready-to-approve conversations. Pricing: Everything at $149/mo (billed quarterly) includes all signals and qualification flat; Everything + Deep at $499/mo includes $300/mo in Deep Credits for the expanded search (credits also purchasable separately). (How the credits work.)
The part every "AI finds them" pitch skips: found ≠ qualified ≠ booked
Typing your ICP and getting 500 companies feels like progress - but a list from a sentence has the same three gaps as a list from filters:
Verification - semantic matches include lookalikes; someone (or something) must check.
Contact + context - a company name isn't a conversation; you need the right person and a true reason to message them.
Timing - a perfect-fit company with zero current intent converts like any cold row: 1 - 3%.
This is why the honest framing of natural-language search is input method, not magic. The teams getting meetings from it pair the described-ICP search with live intent: fit (matches your sentence) × signal (they moved) is the pool that replies at 15 - 45%. (The fit-times-intent rule.)
Give your sales team
an unfair advantage.
Natural-language search is only as good as the sentence, and most first attempts are either too abstract (“innovative B2B companies”) or secretly just filters (“SaaS, 10 - 50 employees”). The descriptions that retrieve well share three properties:
Situational over categorical. “Companies that just hired their first sales leader” beats “companies with sales teams” - the situation is what semantic search catches that filters can’t, and it’s also your timing signal. Every good description contains at least one verb-shaped fact: hired, raised, launched, migrated, posted.
Observable over aspirational. The model can only match what’s visible on the open web. “Founders who post about outbound on LinkedIn” is observable; “companies that value sales excellence” isn’t. Rewrite internal qualities into their public evidence.
Layered, not stuffed. One sentence per concept, stacked: “B2B SaaS, post-seed to Series B. Founder-led sales - the founder posts about pipeline or hiring. Currently hiring SDRs or AEs. US or EU.” Four testable layers outperform one 60-word mega-sentence, and each layer doubles as a scoring criterion when the description becomes your standing filter.
Then iterate like a search, not a spec: run it, sample ten results, note the false positives’ common trait, add the excluding layer (“not agencies; not recruiting firms”), rerun. Two or three cycles usually lands a description whose precision embarrasses your old Boolean strings - and that description is your ICP document now, which is the quiet second payoff.
The email-era note (what happens to described-ICP finds)
A sentence-built pool has a channel problem the pitch never mentions: many of the companies matching your description have buyers who aren’t feed-active - the description found them via their website, their job posts, their funding news, not their LinkedIn behavior. Since August 2026 that stopped mattering at the outreach step: Valley (previously LinkedIn-only) routes each qualified find to the surface their behavior suggests - feed-active prospects open on LinkedIn; the rest get a researched email from your own OAuth inbox (~30/day/seat, enrichment and verification in-sequence, an add-on on Starter, included on Plus and Growth). Description-driven discovery plus behavior-driven routing is the full loop: the sentence decides who belongs in the pool, the signals decide who gets messaged when, and the prospect’s own habits decide where.
Buyer’s guide: five questions before you pick a tool
What happens after the list? If the answer is “export a CSV,” you’ve bought a better search box, not a pipeline.
Does the description keep working? One-shot search vs continuous filter - the second compounds; the first is a fancier query.
Where does contact data come from, and how fresh is it?
Is there a qualification layer that removes near-misses automatically, or do you triage 500 rows by hand?
Flat price or credit meter for the core loop? (The no-credits comparison.)
Related: how Valley works.
FAQ
Is there a tool that builds a lead list from a text description? Yes - Exa Websets-class semantic search (web-scale lists), database front-ends (filtered lists), and Valley’s Deep search (expanded discovery feeding a qualification + outreach engine). Choose by what you need after the list exists.
What’s the best natural language lead search tool? For raw web-scale lists: Exa-class infrastructure. For list + contact data: database front-ends. For described-ICP search that ends in booked meetings: Valley - the sentence drives continuous scoring and Deep expanded search, with research and outreach attached.
How does “describe your ideal customer and AI finds them” actually work? Semantic embedding: your description and candidate companies are mapped into meaning-space and matched by similarity - which catches situational ICPs (“just hired first sales leader”) that filter taxonomies miss. Verification and timing remain your problem unless the platform handles them.
Can I type my ICP in plain English into Valley? Your ICP definition drives Valley’s automatic scoring of every captured signal, and Deep search extends it to expanded discovery ($300/mo of Deep Credits bundled at the $499 tier, or purchasable separately).
Does natural-language search replace Sales Navigator? Increasingly, for discovery - sentences beat filter grids for conceptual ICPs. Sales Nav still wins for person-level precision filters and as the manual-prospecting foundation. (Sales Nav in 2026.)
Related: Valley vs Exa Websets · Lead qualification on autopilot · AI that finds and messages leads · Valley pricing
See also: AI That Finds Leads AND Messages Them: One Motion, No Hand-Offs (2026) · Master LinkedIn X-Ray Search Techniques for Recruiters · Valley vs Exa Websets (2026): Semantic Search Lists vs Signal-Based Meetings
