The AI pilot phase is over.
AI isn’t a tool GTM teams experiment with anymore. It’s doing the actual work: running outreach, routing leads, prioritizing pipeline. The teams that figured this out early are pulling ahead. Not by a little. Structurally.
And the thing separating the teams that win from the ones that fall behind isn’t the agent. It’s the data those agents run on.
That’s what EvoLusha 2026 is about.
Join us live on May 27 as Yoni (Co-Founder & CEO), Danit (Senior Data Product Manager), and Ben (Core Experience Product Director) show you what it looks like when quality data and agentic GTM actually work together.
What’s on the agenda
Workspace: Find the right people without lifting a finger
Right now, most reps spend the first 30-60 minutes of their day figuring out who to contact before they can actually start selling. They fight filter menus, export messy lists, clean out the bounces, and cross-check the CRM. By the time they pick up the phone, half the morning is gone.
Workspace changes that. Describe who you need in plain language and get back a verified list with buying signals already attached. No filter gymnastics. No dead emails to clean after export.
What this looks like in practice: An SDR working a fintech book types “Series B fintech CMOs in New York who’ve had a leadership change in the last 90 days.” Workspace returns a verified list, ranked by signal strength, ready to work. The whole thing takes two minutes. An AE targeting specific enterprise accounts pulls verified contacts with funding rounds and hiring activity already surfaced, so they know which accounts are in motion before they make the first call. A RevOps team hooks into the API and automates the whole flow — verified, signal-enriched contacts flowing directly into the CRM without a manual export in sight.
The leads are better. The time to first call is shorter. The data doesn’t break the workflow.
How to use it
Try this prompt in Claude with Lusha connected:
<context>
I'm prospecting for new business and want a verified contact list that matches my ICP.
</context>
<task>
Use Lusha to find 25 verified contacts that match:
- Job titles: [TITLES — e.g., VP of Revenue Operations, Head of RevOps]
- Industry: [INDUSTRY — e.g., B2B SaaS]
- Headcount: [RANGE — e.g., 500-2,500]
- Geography: [REGION — e.g., United States]
Return a table:
Name | Title | Company | Validated email | Mobile direct dial | DNC status
</task>
<constraints>
- Only include contacts with hasWorkEmail: true
- Surface email confidence grade (A+ through D) per row
- Flag any phone number with DNC = true
</constraints>
ICP Hub + AI recommendations: Your best leads, ranked and waiting every morning
Here’s a problem most sales teams don’t talk about out loud: every rep builds their own list differently. Different filters, different logic, different accounts. The result is inconsistent pipeline, missed opportunities, and new hires who take weeks to figure out who they’re even supposed to be targeting.
ICP Hub fixes the foundation. Admins define the ideal customer profile once — industry, size, seniority, location, signals that matter — and every rep on the team works from the same targeting logic from day one. Including the new hire who started Monday.
What this looks like in practice: A sales manager sets up the team ICP in the morning. Every rep opens Lusha and sees two scored tables waiting — top companies and top contacts for today, ranked by how closely they match the ICP and how strong their current buying signals are. The Signals column shows exactly why each lead surfaced: new funding, a key hire, a CRM match. A rep starting their day doesn’t ask “who should I call?” They look at the list and start calling. A new hire on their first week isn’t relying on tribal knowledge or shadowing a senior rep to learn the ICP. They open Lusha and the feed is already personalized and ready.
Up to 10,000 prioritized contacts and companies, delivered daily. The more the ICP is configured, the sharper it gets.
How to use it
Try this prompt in Claude with Lusha connected:
<context>
I want to build a scored ICP table from inbound leads with transparent component scoring —
not just YES/NO qualification but a defensible score sales can trust.
My ICP scoring model:
- Title fit (0-30 points): [e.g., 30 for VP+, 20 for Director, 10 for Manager]
- Industry fit (0-25 points): [e.g., 25 for core ICP, 15 for adjacent, 0 for off-ICP]
- Company size fit (0-20 points): [e.g., 20 for 500-2500, 10 for adjacent range]
- Geography fit (0-15 points): [e.g., 15 for US/UK, 0 for off-region]
- Signal bonus (0-10 points): [10 if a buying signal fired in last 90 days]
The leads:
[PASTE — name, email, company, optional title]
</context>
<task>
For each lead, enrich with verified data, apply the scoring model, and return a sorted table:
Tier | Total score | Name | Title | Company | Component breakdown | Reasoning
Apply routing tiers: HOT (80+), WARM (50-79), COOL (20-49), COLD (under 20).
Sort by total score descending.
</task>
Signal-based selling: Automate the follow-through on buying signals
The best reps already do this manually. A contact gets promoted. A target account raises a Series B. A company starts hiring aggressively into a role that signals budget. A sharp rep catches it, acts fast, and gets into the conversation before anyone else.
The problem is consistency. Even the best rep misses signals when they’re busy. And most reps aren’t catching them at all.
Signal-based selling automates the response. When the right trigger fires for the right account, the workflow starts automatically. No one has to catch it.
What this looks like in practice: A target account posts 15 new engineering jobs. A workflow fires, flags the account, pulls the verified decision-makers, and queues them for outreach — all before a rep even opens their laptop. A contact at a key account gets promoted to VP of Sales. An automated sequence kicks off with messaging tailored to someone new in the role who’s likely reassessing vendors. A company raises a Series C. The account gets surfaced immediately to the AE who owns it, with a contact list attached and a suggested next step. Signals that used to slip through the cracks become systematic pipeline.
How to use it
Try this prompt in Claude with Lusha connected:
<context>
I want to scan target accounts for buying signals in the last 90 days and rank them
by signal intensity for outreach priority.
My target accounts:
[PASTE — domains or company names]
</context>
<task>
For each account, use Lusha's signals layer to retrieve activity in the window:
- Financial events (funding rounds, IPO, M&A, strategic investments)
- Leadership events (executive hires, promotions, departures)
- Hiring surges by department
- Product launches and partnerships
For each account return:
Account | Signal types fired | Specific signals with dates | Signal intensity | Recommended action
Apply intensity scoring:
- HOT: 3+ signal types fired, including one strong signal (leadership, funding, M&A)
- WARM: 1-2 signal types fired
- QUIET: no signals in window
Sort by intensity. Surface HOT accounts for priority outreach this week.
</task>Connect Lusha to Claude in two minutes →
Why this matters right now
These aren’t features for a future state. They’re built for the way GTM already works — where agents are running more of the workflow and data quality is the variable that determines whether those agents do useful work or waste everyone’s time.
Lusha’s data operates in two layers. The first is universal: billions of verified data points on contacts, companies, and real-time signals across the business world. The second is specific to your business — your customers, your patterns, your best deals. The AI learns your context and uses it to score every opportunity against what actually works for you. Every morning, instead of a list of hundreds, you get the ones most likely to buy from you today. And it gets sharper every time you use it.
Working together, those two layers don’t just power agents. They make every rep more productive, every campaign more precise, and every dollar of pipeline more likely to close.
Join us live
Everything we’re showing at EvoLusha 2026 is live: in the platform, the extension, via API and MCP, and through tools like Claude, ChatGPT, n8n, Make, and more. We’re showing you what’s available right now and how to use it.
If your team is spending time building lists before they can prospect, missing buying signals because there’s no system to catch them, or waiting on data to be clean enough to trust, this session is for you.
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