Lusha Data Framework
Where every GTM team loses time, money, and API calls
Your GTM stack has more tools than ever and is probably delivering less than it did two years ago. Not because the tools are bad. Because every tool in the stack is running on data that was never verified, hasn't been refreshed, and doesn't connect to the AI layer sitting on top of it. Every enrichment call, every AI-generated brief, every outbound sequence is working from a foundation that's quietly degrading. You're not losing because you lack effort. You're losing because the foundation is wrong.
Source: B2B Sales Intelligence Benchmarks 2026
Source: Lusha Contact Change Report Q2 2026
Source: Salesforce State of Sales 2024
Sources: B2B Sales Intelligence Benchmarks 2026 · B2B Contact Change Report Q2 2026 · Salesforce State of Sales 2024
The fix isn't another tool. It's a verified data foundation that every tool and every AI model runs on — one that's current, connected, and shaped by your specific business.
Start with truth
AI doesn't fix bad data. It scales it. Every hallucinated contact, every stale account brief, every outreach sent to someone who left the company six months ago — these aren't AI failures, they're data failures that AI made faster and more expensive. The teams winning with AI GTM didn't start with better prompts. They started by making sure the data underneath the prompt was worth trusting.
"In the AI era, trust and data accuracy are what separate the teams pulling ahead from the ones generating expensive noise. Start with verified data. Everything else follows."
Yoni Tserruya, CEO, Lusha
Language models don't know when they're working with bad data — they produce output either way, and they produce it with equal confidence whether the underlying contact is still at the company or left eight months ago. What changes the quality of the output isn't the model itself, it's the accuracy and freshness of everything you feed into it. A verified account picture, a named buying signal, a contact confirmed in seat this week — these inputs produce briefs a rep can act on immediately. A stale CRM record and a guessed email produce advice that sounds reasonable and leads nowhere.
Claude, GPT, Gemini — every company runs on the same foundation of models, available at the same price per token. What separates the teams producing useful AI output from the ones producing noise isn't which model they chose, it's what they built underneath it. A team running verified contacts, named buying signals, and ICP scoring trained on their own closed-won deals will consistently produce better outputs than a team running the same model on a stale CRM export — and that advantage compounds with every deal cycle, because better outputs drive better decisions, which sharpen the data further.
A named signal is a measured change at a specific account with a date attached: a budget line moved, a job count rose, a leader was appointed. That is more than anonymous intent gives you. It is also less than "know the moment." Signals read on cycles. Hiring signals read weekly. Budget and headcount signals read monthly, and the newest reading can be five or six weeks old. News signals carry the date the article ran, which is not always the date the thing happened. Headcount counts LinkedIn profiles, not payroll, so a marketplace with thousands of agents can show a fall that never touched its staff. Every play in the Campus library reports the reading date and flags these cases, because a signal trusted blindly is worse than no signal: it puts a confident rep on the phone with the wrong sentence. Five checks before you trust a buying signal →
The same list, two different readings
Not a hypothetical. On September 9, 2026 we ran the IT spend increase signal on a list of 25 B2B software companies through the Lusha plugin for Claude. Same model, same signal, same 17 credits. 14 accounts came back with a budget increase. Here is what the result looks like read two ways.
Five accounts with technology budgets between $170M and $1.25B, each showing that budget more than tripled in a single month. A rep calls the top one and opens with the number. The prospect says nothing has changed. The rep stops trusting the feed.
Two accounts raised technology budget on two consecutive readings. That is a plan, not a blip. Two more show a single plausible reading. The five outliers go to the data owner, not to a rep. Same 17 credits; the top accounts are the ones a rep can call with a true sentence.
Company names masked to initials. Confirmed live via Lusha connector, September 9, 2026. Full run: Find accounts increasing IT spend and work them first →
30% of B2B contact data decays annually. The gap between verified and unverified widens every quarter, and AI amplifies it. Source: Lusha Contact Change Report Q2 2026. Role change among US sales leaders specifically runs at 12.6% a year, and 1.73 times that in the UK: measured against 148,000 records. The two figures measure different things: decay counts any field going wrong; role change counts one person moving.
Verified data at the foundation. Intelligence built on top.
Search the market. Get back intelligence tuned to your business, shaped by your ICP, your CRM, your contacts, your searches and your closed-won deals. Lusha is built on two data layers that work together. The first — the Search layer — is universal. It covers everything that happens in the business world: contacts, companies, live signals, verified across independent sources so every agent and every rep always has the best data available. The second is Deep Intelligence, and this one is unique to your business. It learns your ICP, your customers, and your best deals, then uses that context to rank every opportunity and surface the accounts likely to generate your next pipeline. Together they make every rep more productive, every campaign more precise, and every dollar of pipeline more likely to close.
Data sourced from Lusha's verified B2B database. Read our data methodology →
Deep intelligence is what separates a data vendor from a GTM platform
Every B2B data tool gives you a contact: a verified email, a direct dial, a company profile. That's the starting point, not the finish line.
What changes the output, for AI and for your team, is intelligence shaped by your specific business. Not a generic ICP model built from industry averages. Not intent data scored against anonymous topic clusters. Intelligence built from your closed-won deals, your target personas, and your actual buying patterns. That's what Lusha Deep Intelligence does, and it's why two teams using the same AI model and the same verified contact data will still produce completely different outputs if one has the intelligence layer configured and the other doesn't.
And it's not a black box. You can see its reasoning, guide it to fit your specific needs, and it gets sharper every time you use it. Over time it becomes your own personal intelligence layer, not a generic model that treats your business the same as every other company in your category.
Lusha Deep Intel starts from your closed-won data, finds the patterns in the accounts you've already won — the signals that showed up consistently before those deals closed — and surfaces new accounts that match those patterns. The model learns from what actually converted, not from what looks good on paper, which means it gets more specific and more accurate the longer you use it.
Anonymous intent data tells you a company is researching a topic category. Named signals tell you something specific happened — a VP joined six weeks ago, a Series B closed last month, 12 SDR roles posted this week across EMEA and North America. Each signal is verifiable, dated, and tied to a real person at a real account. That's the difference between a directional indicator and an actual reason to reach out today.
Deep Intel maps the full buying group across every target account — Champion, Economic Buyer, Technical Evaluator, Influencer, Blocker — with each contact verified via Lusha, classified by role, and audited for coverage gaps. A deal that reaches Stage 3 without a verified Economic Buyer isn't a pipeline problem, it's a data problem that the intelligence layer surfaces before it becomes a lost deal.
Give Lusha your best customers and Deep Intel finds companies that look like them — not based on a generic similarity model, but on the specific firmographic, technographic, and signal patterns that appear in your own closed-won data. The result is a prospecting list that starts from what actually converted, not from what fits a broad category filter that applies equally to your competitors.
"The teams pulling ahead aren't just using better AI. They're feeding it better intelligence — intelligence shaped by their specific business, their specific wins, and their specific buyers."
Yoni Tserruya, CEO, Lusha
Everywhere
Ask it in Claude or ChatGPT. Run it inside your CRM. Pipe it into anything you build.
Lusha data lives wherever your team and AI agents work: the Lusha plugin for Claude, the ChatGPT app, the Codex plugin, your CRM marketplace, the Chrome extension, API, or MCP. Both data layers are immediately available in the tools your team already uses every day. Nothing to rip out. No new workflows to adopt. No asking your team to change how they work.
Four forces, sequential, non negotiable
There's a sequence to GTM that doesn't change regardless of what tools you add. Data tells you who exists. Signals tell you who's ready. Targeting narrows that to who's worth reaching right now. Outreach delivers the message. Each layer depends on the one below it. When teams skip a layer, usually in the rush to deploy AI generated outreach at scale, they discover that sophisticated messages sent to the wrong accounts at the wrong time don't convert. They just cost more to produce.
"Instead of one big cold list, you build a series of small, precise workflows. Each one answers a single question: why is right now the right moment to reach this person?"
Ben Harten-Beilis, Core Experience Product Director, Lusha
Where are you today
Locate your organization in the model, then take your next move. The CRM looks populated. The enrichment tool is connected. The intent platform is sending signals. And yet the pipeline doesn't reflect it. That gap almost always traces back to the same place: a data foundation that was set up but never properly verified, connected, or shaped around the business it's supposed to serve.
Three teams that built the foundation
"Lusha's direct contact information is worth more than gold. Data is our bread and butter. Lusha is focused on data, not bling and bells and whistles."
Jeremy Levine, Director of Business Development, WalkMe
"With Lusha, SDRs spend 87 additional hours prospecting each month. We've tripled the number of outbound meetings generated."
Florence Broderick, VP Marketing, CARTO
"Lusha has directly contributed to our revenue growth, helping us generate £1.4M in 2024 through better data and smarter outreach."
Chris Coghlan, Performance and Development Manager, Empiric
Remove the friction, keep the judgment
The goal isn't to replace sellers with AI — it's to remove the hours they spend each week on work that isn't selling. Research before a call that should take two minutes but takes twenty. Enrichment runs that a rep does manually because the CRM wasn't updated. Switching between tools to find a direct dial, verify a title, check whether a contact is still at the company. When those hours go back to the seller, the same team with the same headcount produces dramatically different results — not because the AI is doing their job, but because they finally have time to do it themselves.
"Traditional sales tools force you to search and filter endlessly. Sales streaming is the opposite — the right accounts, contacts, and signals come to you."
Yoni Tserruya, CEO, Lusha
Five questions. Answer them straight
The CRM looks populated. The enrichment tool is connected. The intent platform is sending signals. And yet the pipeline doesn't reflect it. That gap — between what the stack looks like and what it actually produces — almost always traces back to the same place: a data foundation that was never properly built. These five questions cut through the surface and show you exactly where yours stands.
Every rep calling from a stale list, every AI brief that needs fact-checking, every deal that slipped because the champion left and nobody knew — that's a foundation problem. The companies that fix it will close more deals, grow faster, and open a gap their competitors won't be able to close. Not a quarterly advantage. A permanent one.
Start with a play · Read the docs · Want the data behind the framework? Read the B2B Sales Intelligence Benchmarks 2026 →
