Daniel Saks
Chief Executive Officer
In B2B sales and marketing, finding the right buyers has traditionally been a time-consuming grind of filtering databases, scraping lists, and manual research. However, a new class of AI-powered buyer discovery platforms is changing the game. These tools leverage natural language processing and vast data to let go-to-market teams simply describe their ideal customers in plain English – and instantly receive highly targeted prospect lists in return. The result is a faster, smarter way to build pipeline. In fact, high-performing sales teams that adopt AI for prospecting report 15–20% faster pipeline growth on average(1). Buyers today expect personalized, timely outreach, and AI-driven platforms are stepping up to meet that need by turning intent into data and action in one step. Below, we explore the top AI buyer discovery platforms using natural language interfaces, highlighting their capabilities and key stats. Each section also features a notable statistic showing how these platforms drive efficiency and results.
Landbase is the first agentic AI platform purpose-built for fully autonomous audience discovery and qualification. Powered by its second-generation model (GTM-2 Omni), Landbase allows any business to find its next customer in seconds — simply by describing their ideal market in natural language. This agentic AI doesn’t just search static data; it reasons over 1,500 real-time business signals to identify best-fit accounts and contacts, much like a skilled researcher working at superhuman speed.
Landbase compresses what once took weeks of manual list-building into a single AI-driven interaction. Early users report it is 4–7× faster at audience creation than traditional data vendors, with up to 80% reduction in manual list-building costs (while maintaining over 90% accuracy through AI + human validation). In pilot campaigns, teams saw 2–4× higher lead conversion rates using Landbase-qualified leads, compared to their old targeting methods. Users say Landbase essentially eliminated weeks of research, enabling them to start outreach in hours instead of months.
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Landbase’s impact has been significant, especially for sales and marketing teams aiming to do more with less. The platform is reported to be 4–7× faster at building targeted lists than traditional methods. One B2B SaaS customer added $400K in new MRR in a single quarter by leveraging Landbase’s AI-qualified lists during what is normally a slow season. Another client reduced their list-building time by 93% (from weeks to hours) while improving outbound reply rates by 40% – because Landbase’s data was more accurate and intent-driven. Overall, users see Landbase as not just a tool, but a “GTM system of action” that moves their targeting process from manual drudgery to automated precision. By unifying deep data infrastructure, a conversational UX, and autonomous AI execution, Landbase has effectively redefined how go-to-market teams approach pipeline generation. For any sales, marketing, or RevOps leader seeking speed and accuracy in buyer discovery, Landbase’s agentic AI platform stands out as a definitive advantage in this new era of autonomous GTM.
ZoomInfo is widely known as the market leader in B2B contact data and sales intelligence. For years, ZoomInfo’s massive database has been a go-to resource for prospecting, boasting between 180–220 million contacts and tens of millions of company profiles in its system(2). This extensive coverage (aggregated from web scraping, partnerships, and user contributions) gives sales teams a broad universe to search for potential buyers. However, traditionally users have had to build lists through manual filters and boolean logic – selecting titles, industries, company size, etc., which can be time-consuming and requires skill in using the platform.
ZoomInfo is now adding AI-driven features to modernize its interface. While it does not yet offer a pure natural language search for building prospect lists, it has introduced tools like ZoomInfo Chat and an AI “Go-to-Market Copilot”. For example, the Chat with Data feature lets users ask questions in natural language to analyze their CRM or campaign performance. And the Copilot can generate explanations for account prioritization in plain English. These moves signal ZoomInfo’s recognition that users want more conversational, intelligent ways to interact with data. We may soon see ZoomInfo enabling natural language queries to directly find contacts (leveraging LLMs to translate prompts into its many filters), given research prototypes in that direction.
Despite new AI features, ZoomInfo’s core value remains its extensive, up-to-date data. It covers a claimed 130+ million professional contacts and 95+ million companies globally, with millions of updates made daily. ZoomInfo also provides advanced company insights – like org charts, tech stack information, and buyer intent signals (e.g. surge in searches for certain keywords) – which help sales teams prioritize who to reach out to. Its data quality is high, but like any large database it can have stale entries; studies have noted that static databases often hover around 70% accuracy at any given time, due to contact churn. ZoomInfo combats this with a combination of automated web crawling and a community of contributors installing its plugins to feed back updated info.
Where ZoomInfo truly excels is as a comprehensive sales intelligence suite. Beyond list building, it offers workflow integrations (e.g. CRM sync, sales engagement tools), intent data feeds, and even conversation intelligence through acquisitions like Chorus.ai. It’s an end-to-end platform suited for enterprises that want data plus the tools to act on that data. The trade-off is cost and complexity – ZoomInfo is one of the pricier solutions and often requires annual contracts and training to get the most value. For smaller teams or quick one-off searches, it can feel heavyweight.
ZoomInfo’s sheer scale is its standout metric. As of 2025, the platform claims over 220 million active contacts (with ~150 million emails and 50 million direct dials) in its database(3). This makes it one of the largest B2B contact repositories in the industry. All that data comes at a price: ZoomInfo is generally aimed at larger organizations with the budget to invest in data-driven prospecting. Its focus is on breadth and depth of data, whereas newer AI-native tools focus on smarter automation. Still, for companies that need a vast net and are willing to configure complex targeting filters, ZoomInfo remains a dominant player – now evolving to incorporate AI assistance into its workflow.
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ZoomInfo’s evolution shows how even incumbents are adapting to the demand for easier, AI-driven targeting. Its strength is trusted data at scale, now gradually layered with AI for insights. For teams that already have a clear ICP and just need data volume, ZoomInfo is a proven (if expensive) choice. But for those who want a more automated or natural language experience, it may feel less intuitive compared to newer entrants. In practice, many organizations use ZoomInfo’s data in conjunction with AI tools – for instance, exporting ZoomInfo lists and then using an AI assistant to refine or research them further. ZoomInfo appears to be heading toward offering that refinement internally, so it will be interesting to watch how quickly they enable true natural language prospect searching within their platform.
Apollo.io is another popular sales intelligence platform, known for combining a broad contact database with built-in engagement tools. Apollo began as a more affordable alternative to ZoomInfo and has grown rapidly; it now touts an extensive dataset of over 265 million contacts across 70 million companies(3). These numbers (sourced from Apollo’s own homepage) indicate Apollo’s data coverage is actually slightly larger than ZoomInfo’s in raw contact count, though the depth and accuracy can vary. Apollo leverages a community-updated model (users contribute data via a browser extension) and multiple data partners to keep its information fresh.
What sets Apollo apart is its integrated approach: it doesn’t just give you contacts, it helps you engage them too. The platform includes a sales engagement module for designing and sending email sequences, a power dialer for calls, and analytics to track responses. This makes Apollo a one-stop-shop for smaller sales teams or startups that want both data and outreach in one tool. However, historically Apollo’s interface for building lists has been similar to ZoomInfo’s – using filters and checkboxes.
Recently, Apollo has dipped into AI features as well, though not as deeply on the natural language query side as some competitors. They introduced an “AI Research” assistant that provides additional insights on people and companies (to help craft personalized messages). Apollo also launched Apollo Labs in 2023–24, which hinted at AI-driven prospecting, possibly integrating with tools like Clay (Apollo has an integration where you can feed Apollo data into Clay workflows). There’s mention of an “ApolloGPT” by users, which suggests Apollo may be experimenting with GPT-based list building, but it’s not a core product feature yet from official sources.
Nonetheless, Apollo’s ease of use and pricing have made it very popular. It offers a free plan and affordable paid tiers, which is attractive to SMBs. The data quality is generally good (it claims a high percentage of verified emails and numbers) but, like any crowd-augmented database, one should expect to do a bit of cleaning. Apollo’s advantage of scale comes from its user base: every time an Apollo user finds a new email or contact, that data can flow back into Apollo’s system (with permission), keeping it ever-expanding.
Apollo.io boasts 265 million+ contacts and 165 million verified emails in its database(3). It also has 120 million phone numbers. This massive trove is combined with outreach tools, which Apollo claims can increase a team’s output significantly. According to one comparison, Apollo users benefit from the platform’s constant database updates (via user contributions), which ensures a steady flow of new leads – an important factor given that an estimated 30% of B2B data becomes outdated each year (due to job changes, etc.). Apollo’s approach of continuous enrichment helps mitigate data decay, keeping accuracy reasonable even at large scale.
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Apollo.io strikes a balance between data quantity and practical tooling. It may not have the most sophisticated AI query interface yet, but its large dataset and integrated workflow make it a top choice for many growing sales teams. In the context of natural language prospecting, Apollo could evolve by adding a chat-based search (imagine asking Apollo: “Give me VPs of Marketing at fintech companies in Europe” and getting results). Until then, users might use Apollo’s rich data export and plug it into agentic AI tools (some teams already use Apollo data in conjunction with ChatGPT or Clay to automate research). Overall, Apollo remains a cornerstone platform in AI-enabled prospecting due to its scale and all-in-one design – a testament to how the industry is blending data + action in one place.
Persana AI is an emerging platform that takes a very direct approach to AI-driven lead generation: it offers a natural language lead list builder called SalesGPT. Persana positions itself as an AI Sales Assistant that unifies data from 75+ sources and automates prospecting tasks. In May 2025, Persana launched SalesGPT 2.0, a feature that lets users “generate precise lead lists using natural language — no filters, no clunky interfaces”(4). This is very much aligned with Landbase’s philosophy, indicating Persana is a close competitor pushing the envelope on conversational prospecting.
With SalesGPT, a user can type something like “Founders of fintech startups in California” or “Marketing managers at e-commerce brands with >50 employees” and the system will interpret the intent and return a list of leads that fit(4). Behind the scenes, Persana still uses filters – it translates the plain English into specific criteria – but it spares the user from doing that manually. Persana even allows the user to review and refine the AI-generated filters, providing a bit of transparency and control(4).
Under the hood, Persana boasts a huge database and signal engine as well. According to the company, Persana’s database includes 800+ million contacts and 200 million companies globally(4). It tracks 75+ buyer intent signals (such as job changes, funding rounds, tech stack usage, web traffic spikes) to time outreach when prospects are most likely to respond(4). For example, Persana monitors if a target account just raised a round or if a key executive was hired, and can surface those “triggers” as part of the lead qualification. Persana claims this signal-driven approach can cut sales cycle time by 65% and boost conversion rates by 30% by focusing reps on high-potential prospects(4).
Beyond list building, Persana automates other steps: it can generate personalized ice-breaker messages, and it includes a Chrome extension for LinkedIn to pull data while browsing profiles(4). Persana emphasizes automation end-to-end– even including an integrated email sender so you can email prospects from the platform once a list is built (saving the CSV export/import step)(4). It’s aiming to be a hands-off system where an SDR could conceivably say “find leads like this, and send them this sequence,” and the AI handles a lot of it.
Persana AI highlights that it leverages 75+ signals to qualify prospects and that using these, their users saw sales cycles 65% shorter and conversion rates 30% higher compared to traditional methods(4). Additionally, Persana’s dual-step enrichment process boasts a 95% email find rate, outperforming many single-source tools(4). The sheer scale of Persana’s data (800M contacts) is also noteworthy – it rivals or exceeds the largest incumbents by aggregating numerous sources. These stats underline Persana’s focus on quality and timing of outreach, not just quantity of contacts.
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Persana AI is a strong example of the agentic prospecting trend. It appeals to teams who want to maximize automation: it not only finds who to contact, but helps you with how and when to contact them. One could say Persana’s approach is “prompt to pipeline.” You describe your audience, and Persana’s AI tries to deliver meeting-ready prospects. Of course, with such automation, users should ensure oversight on quality and compliance (Persana emphasizes it is compliant with data privacy and has measures to avoid spam). In summary, Persana AI is pushing the frontier of natural language prospecting by combining a huge dataset with intelligent automation – making the process of discovering buyers feel as easy as chatting with an assistant.
Clay is a slightly different entry on this list. Rather than a giant proprietary database, Clay is an automation platform that connects to dozens of data sources and uses AI agents to gather and enrich leads. Think of Clay as a Swiss army knife for prospecting: you feed it some starter info (like a list of companies or a search query), and it will pull data from various services (Apollo, Clearbit, Hunter, Google Maps, LinkedIn, etc.) to build out a targeted list. Recently, Clay introduced Clay AI Agents (e.g. “Claygent”) that allow natural language prompts to orchestrate these web automations(4).
For example, a user could prompt Claygent with: “Find SaaS companies in London that use Stripe and have <100 employees, then get me the CEO’s email.” Clay will then do a series of actions: search a database for SaaS in London, cross-check technographic data for Stripe usage, filter by size, then find CEO names and emails via enrichment APIs. It’s like chaining multiple tools together, which used to require coding or complex setup, now done via an AI instruction. Clay’s strength is flexibility – it can tap into 50+ premium data vendors and public web info, so you’re not limited to one database(4). In fact, companies have used Clay to triple their data enrichment rates by combining sources(4).
Clay also excels at web scraping and research. The Clay AI agent can visit company websites, scour staff pages or press releases, and extract custom info. If you need something niche like “companies that mention remote work on their about page” or “startups whose CEOs have written a book” – Clay’s agent can attempt that, which a static database might not capture. The trade-off is Clay might require a bit more tinkering and validation; it’s a power tool for growth hackers and ops teams who are comfortable building workflows. It’s less of a plug-and-play UI for a sales rep (compared to others on this list) and more of a “prospecting automation toolkit.”
Clay’s multi-source strategy contributes to strong data quality. By chaining APIs, Clay can achieve very high coverage – one report noted companies using Clay saw a 3× increase in enrichment rates (finding 3× more emails or data points) versus using a single source(4). Clay connects to data on over 3 million companies for tracking buying signals and uses 50+ providers to fill in details(4). Essentially, if one source misses a contact, another might have it. This waterfall enrichment approach means Clay often ends up with more complete lead profiles, which can significantly improve campaign reach and results.
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Clay’s approach is extremely powerful for those who want ultimate control. It’s like having an AI-enabled research assistant that you can customize. For pure natural language list building, Clay might require a bit more user effort than Persana or Landbase, but it can accomplish some things they cannot, especially when it comes to more complex or bespoke data needs. In practice, some companies use Clay in combination with a database like Apollo or ZoomInfo – e.g., use Clay to refine or enrich a list pulled from elsewhere, or to scrape niche sources that the big databases haven’t captured. It may not be as straightforward as “type and get list” for the average seller, but for ops and data-savvy teams, Clay is a top platform that demonstrates how AI can connect the dots across the web for buyer discovery.
Cognism is a B2B contact data provider that has gained prominence particularly in Europe and internationally for its commitment to data compliance and quality (e.g., GDPR-aligned data). In 2024–2025, Cognism introduced an AI Search feature within its Prospector tool that allows users to search for contacts and companies using natural language(5). This move signaled Cognism’s entry into the natural-language prospecting arena. For example, in Cognism you could type a query like “IT directors at fintech companies in Germany” and the AI Search will interpret that and return matching contacts, without the user manually setting filters. It essentially provides a text-to-filter interface, similar to what ZoomInfo has been exploring and what Landbase/Persana offer out of the box.
Cognism backs this interface with its solid data platform. Cognism’s database is a bit smaller than ZoomInfo’s in raw numbers, but it emphasizes accuracy and compliance. It has a specialty data subset called Diamond Data – phone-verified contacts that are highly accurate (Cognism actually has humans call to verify phone numbers, resulting in 3× higher connection rates on those contacts)(5). Cognism’s total contact count is not always advertised, but it likely ranges in the hundreds of millions as well. For instance, Cognism’s website notes “equipping 4000+ revenue teams” and highlights case studies: one client saw a 33% increase in pipeline in 3 months, another doubled their leads using Cognism’s data(5). These outcomes tie to Cognism’s focus on data quality – more conversations with the right people.
The AI Search in Cognism’s Prospector is reported to speed up the user’s workflow significantly. Cognism advertises “74% faster prospecting” with AI Search(5). Essentially, tasks that took, say, an hour to build a list might take 15 minutes when you can just describe what you want and let the system assemble it. This speed claim is likely based on user testing or case studies after launching the feature. In addition, Cognism has AI baked into other parts of their suite: their Chrome extension (Sales Companion) can do things like automatically suggest contacts at a company (using AI to guess who might be relevant)(5), and even do research for cold call prep (summarizing news about a company)(5).
Cognism’s introduction of natural language search led to a 74% increase in prospecting speed for users(5). In other words, sales reps can build lead lists nearly 3× faster than before, which translates to more time engaging leads instead of building lists. Additionally, Cognism’s focus on data quality yields tangible results: for example, a case study showed a 200% increase in leads contacted after switching to Cognism, and another showed phone connect rates rising significantly due to their Diamond verified data(5). These stats underscore that speed means little without quality – Cognism aims to deliver both.
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Cognism’s trajectory shows an incumbent data provider adopting AI to improve user experience. They have one foot in the “traditional” camp (big database, sales tools) and one in the “modern AI” camp (conversational search, intelligent insights). This hybrid can be powerful: a rep using Cognism can trust the data quality and also enjoy some of the time-saving magic of AI. Cognism might not market itself as aggressively “agentic” as Landbase or Persana, but in practice its AI search achieves a very similar outcome: faster, easier list building with natural input. For teams that value compliance and quality, Cognism provides that peace of mind while still innovating on the UX front.
Another player in the AI-driven buyer discovery space is Kernel. Kernel is a newer platform that focuses on account sourcing by deploying AI agents to scour various data sources, almost like an AI-powered virtual SDR. The tagline: Kernel “replicates the behavior of an experienced sales rep” to identify promising accounts and prospects across the web. While not as widely known as others above, Kernel represents the cutting edge of agentic automation. It can run what they call AI Account Sourcing across the entire web and public data, checking niche ICP criteria that a static database might miss.
For example, Kernel’s AI might monitor forums, job boards, news sites, etc., to find signals like “a company hiring 10+ engineers in a month” or “a company’s CEO mentioned expanding to APAC in an interview” – all hinting at a growth or pain point that makes them a potential buyer. It’s akin to having a digital analyst constantly researching and updating your target list. Kernel then compiles these finds into account lists and even suggests the best contacts at those accounts to approach. Essentially, it’s trying to automate that senior sales researcher who knows where to look for the “hidden gems” of prospects.
Kernel is a bit more specialized and may integrate with CRM or other systems rather than being a standalone UI where you type a prompt (its interface is not publicly demoed as much). It’s mentioned in contexts alongside Clay and Persana as a tool for advanced teams. Kernel likely appeals to organizations that want an automated feed of new target accounts meeting evolving criteria – an AI that continuously refreshes your TAM.
While specific performance stats on Kernel are scarce publicly, one can infer its impact from related data: Companies that systematically use AI for account sourcing see significant increases in pipeline coverage. For instance, an AI-native CRM called Aurasell (with a similar data engine) maintains 800M contacts and 85M companies updated in real-time to fuel its AI agents(6). The idea is that with such breadth, an AI can find 30–40% more “hidden” opportunities that wouldn’t surface through traditional search. Kernel’s value proposition is in these incremental discoveries – catching opportunities that fall through the cracks of standard tools, thereby boosting lead funnel growth without additional human research hours.
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Kernel AI is indicative of where things are heading: fully autonomous prospecting where AI agents proactively find buyers even without being asked each time. While it may not yet have the user base of an Apollo or ZoomInfo, it’s an important platform to watch (and potentially leverage for companies with very targeted account-based strategies). It complements the others on this list by focusing on automation breadth rather than user-driven queries. In practice, some companies might use Kernel to feed their pipeline top-of-funnel, and then use a tool like Landbase or Cognism to enrich and contact those leads. The ecosystem of AI buyer discovery is rich, and Kernel adds to it by pushing toward autonomous GTM research.
The platforms above illustrate an ongoing transformation in how sales and marketing teams find their next customers. Instead of spending 60–70% of their week on painstaking research and list building, reps can now offload much of that work to AI-driven systems. Natural language interfaces have been the key innovation – by allowing users to simply tell the platform who they want to target, the complexity of filters and data mining is abstracted away. This dramatically lowers the barrier to sophisticated prospecting. It also means that targeting isn’t limited to those who know how to wrangle a database; anyone on a go-to-market team can leverage these tools to generate quality leads on the fly.
Another theme is the integration of real-time signals and quality controls. Platforms like Landbase and Persana not only pull huge volumes of data, but also ensure that data is timely (through signals like intent or hiring trends) and accurate (via verification loops). This addresses the classic problem of legacy databases where you might get a large list, but half the contacts bounce or are irrelevant. The new AI buyer discovery tools strive to deliver actionable lists – ones that convert to conversations and pipeline at a much higher rate. The stats we highlighted (e.g. 2–4× higher conversion lifts, 65% shorter cycles, 80% time saved) show that when done right, AI-driven targeting isn’t just faster, it’s better for business outcomes.
Crucially, adopting these tools doesn’t mean removing the human touch. Instead, it amplifies human productivity. Sales reps can refocus their energy on engaging and selling, rather than list prep. Marketers can quickly test hypotheses about new ICPs or markets by generating lists in seconds. Operations can maintain a cleaner, more up-to-date database continuously. The AI handles the heavy data lifting, while humans provide strategy, creativity, and personal connection.
For organizations evaluating these platforms, considerations include: the scope of your target audience (global vs regional, SMB vs enterprise), the importance of compliance, budget, and how much automation you’re ready for. Some may start with a hybrid approach – for instance, using ZoomInfo or Apollo data, but then feeding it to an AI tool for faster qualification. Others might jump straight into an agentic solution like Landbase to revolutionize their process end-to-end.
One thing is certain: buyer discovery will never revert to the old status quo. The genie is out of the bottle with AI in prospecting. Just as buyers use natural language (search engines, chatbots) to find what they need, sellers can now do the same to find buyers. Those who embrace this technology stand to gain a significant competitive edge in building pipeline.
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