AI Agents Beat Clay and Apollo: Landbase Launches GTM-3 Omni

AI agents just won their first public head-to-head contest against the legacy sales-data industry — and it was not close. On September 29, 2026, Landbase, the Mountain View company building go-to-market infrastructure for the agentic era, announced GTM-3 Omni, its next-generation model for agentic sales discovery, qualification and outreach. Alongside the launch, the company published a benchmark pitting its AI agents directly against Clay, Apollo and ZoomInfo on list precision. The agents won 18 of 26 test prompts outright and nearly doubled the precision of their closest human-operated rival.

The result is a meaningful milestone in the broader shift toward autonomous work. Until now, most proof that AI agents outperform classic filter-and-keyword software has lived in vendor marketing decks. Landbase is releasing the full benchmark methodology and per-prompt results, with identical prompts, identical qualification criteria and an independent judge across all four vendors — the kind of transparency the agent economy will need if it wants institutional trust. The numbers tell a striking story: GTM-3 Omni hit 76.7% precision, while Clay managed 47.9%, Apollo 36.0% and ZoomInfo 23.2%.

The Benchmark: Agents Versus Filters

The setup was simple enough that anyone can audit it. Landbase's AI Lab gave all four vendors the same 26 list-building prompts — realistic go-to-market targeting tasks with defined qualification criteria — and scored every company and person returned against identical standards. According to the announcement published via Business Wire, each returned name was independently re-verified before scoring, so the only variable was the quality of what each vendor produced.

Landbase's model won 18 of the 26 prompts outright, more than triple the four wins Clay managed. It also delivered the highest first-page accept rate: 76.1% of the first 100 rows a buyer would actually work were good prospects. Apollo and ZoomInfo, two of the biggest names in B2B contact data, finished a distant third and fourth. For anyone who has burned sales credits on lists full of dead accounts, the practical math is brutal. A rep working a 77%-precise list of 1,000 companies has 767 real conversations available. The same rep on a 36%-precise list burns two out of every three touches on the wrong account — and pays for the volume.

Landbase's customers, the company says, have observed double the campaign performance or better once list precision improves. "We've now proven that AI can automate the work, from TAM mapping to list building to account research, so reps can do more of what they love: selling and cultivating human relationships," said Daniel Saks, Landbase's co-founder and CEO, in the announcement.

Precision Over Speed, By Design

One of the most interesting details in the announcement is what GTM-3 Omni refuses to do: be fast. A filter-based tool returns rows in seconds. GTM-3 takes roughly two minutes per list, because it plans the search, qualifies every candidate against the buyer's stated criteria and discards what does not hold up. That trade is the entire product. Instead of reps filtering and burning credits, one prompt does the targeting, qualification, enrichment and scoring — and customers see twice the connects and meetings.

That two-minute pause is where the agent's reasoning actually happens. GTM-3 explores, plans, qualifies and scores every candidate against the buyer's stated criteria written in natural language, rather than mapping a prompt onto a fixed set of filters. The model runs directly inside Claude Code, Codex and Gemini CLI — the agentic coding environments where go-to-market and revenue operations teams already spend their days — and Landbase is also deploying it across enterprise and private equity portfolios, with forward-deployed data engineers embedded alongside GTM leaders.

The lab behind the model carries serious weight in the data world. It is led by Hua Gao, a Stanford Ph.D. and co-founder of EverString, the predictive-marketing company acquired by ZoomInfo. "With Landbase, agents can explore and reason over data to optimize go-to-market workflows with greater efficacy than humans," Gao said in the announcement. "Landbase outperforms alternatives at identifying desired prospects by 2x in precision. When paired with targeted outreach, more precise audiences can drive 2x or greater improvement in campaign performance."

What This Signals for the Agent Economy

The Landbase launch lands on the same day that always-on AI agents dominated the industry's biggest stage — OpenAI unveiled Dots, its always-on agent fleet, at DevDay 2026 — and the timing is instructive. The first wave of agent hype was about assistants that could chat. This wave is about agents that can do measurable work, scored on outcomes, with receipts.

Reproducible benchmarks like Landbase's are how that transition happens. GTM-3 is described as the first go-to-market model released alongside a reproducible, vendor-agnostic benchmark with published methodology — a standard the company is now implicitly daring the rest of the industry to meet. In capital markets, a parallel story is playing out: Nasdaq and Robinhood have both moved agents onto live financial rails in recent weeks. The common thread is accountability. Agents that can be scored get deployed; agents that can't stay in demos.

The benchmark also has an awkward twist for one of the companies it beat. ZoomInfo acquired EverString, the company co-founded by the man now leading Landbase's AI Lab — which means ZoomInfo's own former acquisition talent is now publishing data showing agents outperforming ZoomInfo's lists. It is a neat illustration of how quickly expertise is migrating from the old data-broker world into the agent world.

There are reasons to read vendor benchmarks with some skepticism, and the honest ones acknowledge it: 26 prompts is a meaningful sample, not an exhaustive one, and every vendor benchmark rewards the scenario its author designed. But precision is a hard metric, the methodology is public, and the gap between 76.7% and the next-best 47.9% is wide enough that it can't be explained away by prompt design. If the methodology holds up under outside scrutiny, this becomes one of the first independent-feeling proof points that agentic reasoning — not better filters — is the answer to one of sales' oldest questions: who should we be talking to?

About Landbase

Landbase, founded in 2023 and headquartered in Mountain View, California, describes itself as go-to-market infrastructure for the agentic era. Its AI Lab builds domain-specific GTM models that let agents reason over business data to find, qualify and engage a company's next customer, natively inside the agentic CLIs where teams already work. The company has raised over $42.5 million from investors including Sound Ventures, Picus Capital, A*, Firstminute Capital and 8VC.

The backstory has its own color. Co-founder Daniel Saks previously co-ran AppDirect, where a cold LinkedIn message from billionaire Michael Dell taught him a lesson that now reads like Landbase's founding thesis: when people know who you are, they answer your outreach. His company's bet is that AI agents will do the harder inverse job — finding the people worth knowing in the first place. GTM-3 Omni is available today in the Landbase platform and natively in Claude Code, Codex and Gemini CLI, with the full benchmark methodology available via the company's website.

Sources: Landbase announcement via Business Wire (September 29, 2026); quotes from Daniel Saks, co-founder and CEO, and Hua Gao, head of Landbase's Applied AI Lab; background on Landbase's funding and history via TechCrunch's 2025 Series A coverage.