Website Buyer Context: Find the Buyers Your GTM Stack Can't.
Lift AI gives your GTM systems, agents, and reps the probability that each website visitor, known or anonymous, will buy. Trained on millions of commission-audited sales, it tells them who to prioritize, how to treat them, and whether decisions created revenue.
Trusted by GTM teams at:
Intent isn't probability. Your pricing page proves it.
Your GTM stack already has plenty of context: who visitors are, what they’ve researched, what’s happened in your CRM. What it’s missing is how likely each visitor is to buy. Not pricing page views or form fills, but a revenue-trained probability to buy for every visitor.
of pricing-page visitors aren't high-probability buyers.
of high-probability buyers don't visit pricing at all.
Each panel illustrates 50 visitors at the stated ratios.
Every buyer, found in real time. Even the anonymous ones.
Lift AI reads how each visitor behaves as they browse, and scores every one of them, whether you know who they are or not. The buyers surface while they're still on your site.
Lift AI: Predict. Guide. Prove.
Buyer Probability Score
A real-time probability of buying, from 0 to 100, for every visitor. High, Mid or Low, so attention and spend can match each visitor's likelihood to buy.
Buyer Context Brief
A plain-language read on the behavior behind the score: who the visitor resembles, what they're focused on, and the recommended next step for your people and agents.
Revenue Loop
Did 0417 actually buy? The Revenue Loop checks scores against the pipeline and revenue that followed. As outcomes flow back, your agents learn from them for the next decision.
This anonymous visitor's Probability Score is 87. Here's why.
Anonymous doesn't mean unknowable. The Probability Score tells you how likely this visitor is to buy. The Buyer Context Brief tells you what they care about and what to do next.
COMPARED against ~15 years of revenue-verified buyer journeys.
Illustrative example. Real briefs are generated per visitor, in session.
70% of your website traffic is anonymous. Lift AI scores 100% of it.
Most visitors never fill out a form or match an account, so most tools have nothing to act on. Lift AI reads behavior, not identity, so every visitor has a Buyer Probability Score from their first session.
Anonymous visitor
Act on probability right away: adapt the site experience, prioritize chat and build ad audiences.
Company known
Combine visitor probability with account fit and intent to guide ABM and sales prioritization.
Person known
Bring the score, the Brief and CRM history together for targeted follow-up.
One score. Every agent, tool, and system in your stack.
Website Buyer Context changes how every system treats visitors: your website conversions, your sales prioritization, your ad spend, and your agents. There's no new dashboard to log into, and install takes five minutes.
Website Conversions
Chat routes 0417 straight to an AE instead of a bot, while they’re still on your site.
Qualified · Chili Piper · Drift · Mutiny
Sales Prioritization
If 0417 matches an account in your CRM, it jumps to the top of follow-up with the brief attached.
Salesforce · HubSpot · 6sense · Clay
Ad Optimization
0417 joins your high-probability audience, and low-probability traffic stops soaking up spend.
Google · Meta · The Trade Desk
GTM Agents
Your agents qualify and route 0417 with the score and brief as context, not page views.
Platform agents · Your own agents
Revenue Loop:
Making Lift AI's Website Buyer Context Accountable at Scale
A prediction is only worth something if it's proven. The Revenue Loop connects every score to what happened next, so you can check whether Lift AI was right, see which plays work, and make every future decision on outcomes instead of proxies.
Outcome Validation
Did High Probability visitors actually produce more revenue?
Lift AI compares Buyer Probability segments with the pipeline and revenue that followed, wherever outcomes are connected.
Performance Measurement
Which treatments worked, for which buyers?
Your platforms report pipeline, revenue and conversion by probability, treatment and segment. Comparison groups establish incremental impact.
Agent Learning Loop
What should happen next time?
Connected results guide future priorities and treatments, giving your agents the outcome feedback to improve their decisions over time.
Charts in A–C are illustrative. Your Revenue Loop reports use your own pipeline and revenue data when connected.
When GTM teams are grounded in probability, the results speak for themselves.
Lift AI customers typically see results within the first 30 days of implementation.
