Your agents can act on every visitor. Lift AI tells them which ones will buy.

Give the agents in your platforms, and the ones you build yourself, Website Buyer Context: a revenue-trained Buyer Probability Score and Buyer Context Brief for every visitor, through MCP, API or the browser.

See Your Buyers Talk to us

Fig.01 — Every agent, one source of Website Buyer Context

MCP · API · Browser · GTM

Lift AI · Website Buyer Context

Score + Brief · every visitor, known or anonymous

Agent.01

Chat agents

Qualified · Chili Piper · Intercom

ScoreBrief

Agent.02

SDR agents

11x · Artisan · Unify

ScoreBrief

Agent.03

Ads agents

The Trade Desk · Google · Meta

ScoreBrief

Agent.04

Your own agents

In your environment, via MCP

ScoreBrief

Agents don't have a reasoning problem. They have a context problem.

Agents can research, route, message and retarget at machine speed. But they decide based on the inputs they're given, and without a revenue-trained prediction they fall back on proxies: pricing-page views, form fills, meetings booked. Worse, agents optimize what they can measure. A booking agent left alone chases whoever is easiest to book. Give it Buyer Probability, and it chases whoever is most likely to buy.

The proxy agents fall back onPricing-page views

88%

of high-probability buyers never visit pricing.

Your agents won't know that unless something tells them.

50 high-probability buyers

Never visit pricingVisit pricing

An agent triggered by pricing-page views reaches 6 of these 50.

Predict. Guide. Prove. Three inputs no model can generate on its own.

01 · Predict

0–100 · Real time

Buyer Probability Score

Who to prioritize. A 0–100 probability for every visitor, known or anonymous, so agents know how much attention, budget and automation each deserves.

Low

Mid

High

02 · Guide

Plain language · Per visitor

Buyer Context Brief

How to engage. Buyer Profile, Areas of Focus, Behavioral Trace and Recommended Action, so the agent knows what to say, offer and route.

Buyer Profile

Areas of Focus

Behavioral Trace

Recommended Action

03 · Prove

Pipeline · Revenue

Revenue Loop

Whether it worked. Outcomes graded against pipeline and revenue, so agents learn from results instead of activity.

Outcome Validation

Performance Measurement

Agent Learning Loop

The missing ingredient isn't reasoning power. It's labelled data.

Where agents use it.

05 kinds of agents · One source of Website Buyer Context

Agent.01 / 05

Fig.A1 — Proxy vs probability · Illustrative

Website & chat agents

Which visitor gets sales, AI qualification, booking or self-serve.

Examples

Qualified

Chili Piper

Intercom

Without Lift AI · decides on a proxy

Pricing-page viewThe same bot for everyone who views pricing

With Lift AI · decides on probability

0417 · 87 · HighHands 0417 to a live AE

Agent.02 / 05

Fig.A2 — Proxy vs probability · Illustrative

CRM & platform agents

Visitor-level probability and context before agents, workflows and records act.

Examples

Salesforce

HubSpot

Without Lift AI · decides on a proxy

Form fillEvery new lead treated as hot

With Lift AI · decides on probability

Probability on the recordWorkflows fire for High first

Agent.03 / 05

Fig.A3 — Proxy vs probability · Illustrative

Revenue agents

Which visitors and accounts to research, sequence and prospect first.

Examples

11x

Artisan

Unify

AiSDR

Rox

Without Lift AI · decides on a proxy

Account intentSequences the whole researching account

With Lift AI · decides on probability

Visitor 82 · HighResearches this buyer first

Agent.04 / 05

Fig.A4 — Proxy vs probability · Illustrative

Advertising agents

Probability-based audiences, suppression and spend.

Examples

The Trade Desk

Google

Your own agents

The same Score, Brief and Revenue Loop your platforms get.

Examples

Agents in your environment, via MCP

Without Lift AI · decides on a proxy

Proxies you supplyOptimizes for activity

With Lift AI · decides on probability

Score + Brief via MCPOptimizes for revenue

Four ways in. Built for how agents already work.

Way in · 01

MCP

Claude and other MCP-capable agents and assistants call Website Buyer Context directly.

Way in · 02

API

Platform agents use the scores already on your records: HubSpot and Marketo through native apps, other tools through the API.

Way in · 03

Browser cookie

On-site agents read the Score and Brief in the same session.

Way in · 04

Google Tag Manager triggers

Fire agent workflows when a visitor crosses into a probability band.

When outcomes flow back, agents get better.

In an early production deployment, an enterprise AI chat agent was given each visitor's Buyer Probability and guidance on how to adapt the conversation. Whether each offered meeting was actually booked was fed back to refine that guidance. Once the adaptive conversation went live, the booked rate climbed every month, well past its target. A one-off fix steps up once and levels off. This kept climbing, which is what a learning loop looks like.

Fig.04 — Booked-meeting rate, enterprise AI chat agent

Shape only · No axis values

Score-only baselineAdaptive · Month 1Month 2Month 3

Adaptive conversation, outcomes fed backA one-off fix, for comparisonTarget

Early production deployment. Shape shown without axis values until figures are approved.

Where Lift AI fits in your stack.

Lift AI doesn't replace your tools or your agents. It gives them the one input they can't produce on their own, then proves whether it was right.

Fig.04 — Where Lift AI fits

Lift AI

Your stack

01 · Data

Your website

Every visit, every visitor, known or anonymous. First-party behavior, captured in real time.

→

02 · ContextLift AI

Lift AI turns that behavior into Website Buyer Context: a Buyer Probability Score and Buyer Context Brief for every visitor. Combined with the identity, signal and relationship context you already have, it becomes Connected Context.

Connected Context

IdentitySignalRelationship

Context Graph

Website Buyer Context

Score + Brief

→

03 · Decisions