AI-powered platform that scores every website visitor with real-time buyer probability using behavioral micro-signals, enabling smart routing to sales reps, chatbots, or self-service — no cookies or PII required.
Lift AI is a machine-learning platform that assigns real-time buyer intent scores (0–100) to every website visitor, including the roughly 70% of traffic that remains completely anonymous. Unlike traditional lead scoring tools that depend on form fills, CRM matches, or third-party cookie data, Lift AI uses a proprietary engine called Micro-Behavioral Analysis™ to evaluate thousands of in-session behavioral signals — scroll patterns, navigation velocity, page dwell sequences, click cadence, and content engagement depth — and compare them against a model trained on what the company reports as over one billion visitor profiles across 14+ industries.
The platform outputs a Website Buyer Probability™ score that updates in real time as the visitor browses. These scores integrate natively with major go-to-market tools including Drift, Intercom, Qualified, Salesforce, HubSpot, Marketo, Segment, and Google Analytics, enabling automated routing: high-intent visitors are connected to live sales reps or premium chat experiences, while lower-scoring visitors are directed to self-service resources or nurture flows.
Lift AI is designed primarily for B2B SaaS and enterprise marketing teams with meaningful website traffic (typically 10,000+ monthly visitors) who want to maximize pipeline from existing traffic without additional ad spend. Published case studies from customers such as Loopio, PointClickCare, and Chronus report outcomes including 9x–19x increases in chat conversions, 67% reductions in cost per lead, and 3.2x conversion gaps between high- and low-scored visitor segments.
The platform requires no personally identifiable information and is SOC 2 compliant, operating within a privacy-first architecture that avoids third-party cookies and data enrichment dependencies. Deployment is handled through a single JavaScript snippet that Lift AI states can be installed in roughly five minutes with no changes to existing website code.
Lift AI offers a free buyer probability scan that provides a one-time snapshot of scored visitor distribution, plus a 30-day free trial with full real-time scoring capabilities. Paid plans are available through a sales consultation process, which is standard for enterprise-focused intent platforms in this category.
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Every visitor receives a real-time score from 0 to 100 representing their purchase likelihood, updated continuously as they browse. The model evaluates behavioral patterns against a training set of over one billion visitor profiles across 14+ industries, producing scores that segment visitors into actionable tiers (e.g., high-intent 65–100, medium 30–64, low 0–29) for routing and prioritization.
Lift AI's proprietary analysis engine examines granular in-session behavioral signals including scroll depth, navigation speed, click patterns, page dwell sequences, content interaction depth, and mouse movement trajectories. These micro-behaviors are compared against the trained model to infer purchase intent without requiring any personally identifiable information or third-party data sources.
Buyer probability scores feed directly into 8+ GTM tools including Drift, Intercom, Qualified, Salesforce, HubSpot, Marketo, Segment, and Google Analytics. Teams configure routing rules based on score thresholds — for example, visitors scoring above 65 trigger a live sales rep connection in Drift, while those scoring below 30 receive an automated self-service chatbot experience.
Lift AI scores can be layered on top of existing ABM platform data (e.g., 6sense account-level buying stage, Demandbase account identification) to add individual visitor-level behavioral intent as a second dimension. This enables teams to distinguish between high-intent and low-intent visitors within the same target account, improving prioritization accuracy for outreach and engagement strategies.
The platform provides analytics for monitoring visitor score distributions, conversion rates by score segment, pipeline contribution, and revenue attribution across routing rules. Teams can track the measurable impact of score-based routing on chat conversions, lead quality, and cost per acquisition over time, enabling data-driven optimization of threshold settings and routing logic.
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In early 2026, Lift AI expanded its integration ecosystem with additional GTM platform connectors and enhanced its scoring model with updated training data. The platform also introduced improved score distribution analytics in its dashboard, giving marketing teams more granular visibility into visitor segment performance and revenue attribution across routing rules.
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