Predict consumer intent before your competitors do.
AI-powered predictive intelligence infrastructure delivering real-time behavioral signals, conversion probability modeling, and enterprise-grade acquisition intelligence at institutional scale.
Institutional scale, measured in signals — not promises.
The platform processes behavioral signals at institutional scale and reports against calibrated ground-truth. These numbers refresh daily.
Most companies buy stale lists. Elite companies buy probability.
Lead quality decays non-linearly from the moment a behavioral signal is generated. Acquisition strategies that ignore decay over-spend on prospects who are already gone. The math is not subtle.
Hours since first intent signal
The predictive cohort retains usable probability past 48 hours because the model decays signal weight rather than discarding it. Industry-average leads (stale list vendors) lose ~80% of usable signal in the same window.
Days from first contact
Probability-targeted cohorts hit 70%+ of their lifetime conversion value within 14 days. Cold list-based outreach takes 90+ days to reach the same level — at multiples of the spend.
Signals are not lists
Lists describe who exists. Signals describe who is acting. Probability ranks signals by what will happen next.
Decay is the moat
Most vendors sell records. We sell probability-weighted records, decay-aware, with confidence intervals. That is a different product.
Identity unifies it
An identity graph resolves signals from every channel to the same individual or household. Without it, the math degrades into noise.
Price your lead pull in real time.
Tune freshness, demographic depth, and quantity. The model returns your per-lead price, total order, and where the volume floor kicks in. Premium real-time pulls top out at $25/lead; archive bulk floors at $0.44/lead.
Richer demographics carry higher market value — pricing reflects the cumulative cost of appended data.
Mid-aged records at scale. Optimized for nurture sequences and look-alike modeling.
Pulling 500 leads costs us the same operational effort as pulling thousands. Orders below $3,000 are billed at the minimum — increase quantity to lower your effective per-lead price.
Lock in this configuration as a formal quote, or talk to the team about enterprise volume and recurring delivery.
Predictive intelligence, mapped to every vertical.
Each vertical has its own decay curve, identity-resolution profile, and conversion psychology. Models are tuned per industry — not flattened into a single one-size product.
Patient intent at predictive scale
Migration intelligence for HNW relocation
Behavioral risk + intent scoring
Predictive policy switching signals
High-intent case acquisition
Destination intent + booking probability
Voter sentiment & behavioral targeting
Software switching intent acceleration
In-market buyer probability
Operational intelligence for portfolio cos.
Predictive patient acquisition for premium care
Geo-temporal demand prediction
Operators using predictive intelligence at institutional scale.
Five engagements across regenerative medicine, luxury real estate, consumer finance, luxury travel, and statewide political. Every metric below is third-party verified.
Regenerative medicine network reduces CAC 58% with predictive intent targeting
A multi-state regenerative medicine network replaced broad-match list buys with predictive intent cohorts. Behavioral scoring re-prioritized media on a rolling 72-hour intent window, eliminating $2.4M in low-conversion impressions while increasing qualified intake by 51%.
Luxury brokerage captures $312M GMV using HNW migration prediction
Identity-graph signals identified high-net-worth households in active relocation consideration 90+ days before MLS activity. Targeted intent campaigns produced a 4.1x lift in qualified buyer registrations and contributed to $312M in tracked GMV.
Mid-market lender lifts ROAS 3.1x with behavioral risk + intent overlay
Behavioral risk scoring integrated with intent signals produced cleaner top-of-funnel for a consumer lender. The combined model reduced underwriting waste 38% and lifted return on ad spend 3.1x within two quarters.
Luxury travel brand books 2,400 incremental high-value stays via destination intent
Destination intent signals combined with booking probability modeling shifted media toward in-market guests within a 28-day window, producing a 62% increase in upmarket bookings.
Statewide campaign realigns $4.6M media plan with behavioral sentiment intelligence
Behavioral sentiment clustering surfaced persuadable cohorts that traditional polling missed. Media plan was reallocated mid-cycle; engagement rose 38% and persuasion-target reach improved 2.3x.
Methodology is not theater. It is institutional discipline.
Predictive scoring combines four model families: behavioral propensity, identity-graph confidence, decay-aware signal weighting, and demographic + psychographic overlays. Every score ships with a calibrated confidence interval.
Traditional acquisition vs. predictive intelligence cohort
Anonymized data from a 14-clinic regenerative medicine network. Predictive cohort entered production in February; full curve steady-state by month seven. Traditional curve continued drift consistent with national category CAC inflation.
Calibration vs. ground truth panel
Higher = closer alignment between predicted probability and observed conversion rate across the panel.
Selected research
- Behavioral economicsKahneman & Tversky · prospect theory under uncertainty in purchase decisions.
- Decay modelingHawkes processes for self-exciting behavioral signals (Liniger, 2009).
- Identity resolutionProbabilistic linkage at scale (Fellegi-Sunter framework, panel-calibrated).
- CalibrationBrier-score evaluation + isotonic regression for predictive probability outputs.
Cumulative ROAS — predictive vs. traditional baseline
Cumulative return on ad spend across a 24-week predictive cohort versus baseline cold-list acquisition. The compounding gap is the value of decay-aware scoring + identity-graph resolution.
Predictive intelligence, explained.
The questions enterprise buyers, analysts, and operators ask most often. Schema-marked for AI search and answer engines.
What is predictive intelligence and how is it different from intent data?+
Predictive intelligence is the modeling layer that sits on top of intent data. Intent data observes that a person has shown interest; predictive intelligence forecasts the probability they will convert, when, and at what value. It combines behavioral signals, identity graphing, demographic enrichment, and psychographic overlays to produce a probability score rather than a binary flag.
How fresh is the data and how does decay work?+
Behavioral signals decay non-linearly. The half-life of meaningful intent varies by category — for B2B SaaS purchase intent it can be 14 days; for high-velocity consumer categories (travel, automotive) it can be 48–72 hours. Our scoring model adjusts in real time and stale signals are weighted down rather than discarded, preserving them for retrospective modeling.
What is identity graphing?+
An identity graph is a probabilistic mapping of devices, accounts, addresses, and behaviors to unified individuals or households. It allows a signal from one touchpoint (a mobile session, an email open) to be associated with the right person across every other channel. Accuracy is measured against panel ground truth.
How do you handle compliance?+
We operate under SOC 2 Type II controls. Consumer data is sourced from consented, opt-in panels and licensed publisher networks; identity resolution is hashed-first by default. We support GDPR, CCPA, and TCPA workflows including verifiable consent provenance per record.
Why is conversion probability more valuable than a lead list?+
A list is a snapshot of who exists in a category. Probability is a forecast of who is going to act. Lists generate volume; probability concentrates spend on the people most likely to convert within a defined window. Customers replacing lists with probability typically see 30–60% CAC reduction in the first quarter.
How does AI scoring work?+
Scoring combines several models: a behavioral propensity model trained on millions of historical conversion events, identity-graph confidence (how sure we are this is the right person), demographic and psychographic overlays, and decay-aware signal weighting. The output is a single 0–100 score with a confidence interval.
Can you integrate with my existing stack?+
Yes. Native exports to Salesforce, HubSpot, Marketo, Snowflake, BigQuery, and Segment; webhook delivery for real-time scoring; and a REST API for custom pipelines. Audience exports support CSV, Parquet, and direct push to ad platforms (Meta, Google, LinkedIn).
What is psychographic targeting?+
Psychographics describe attitudes, values, motivations, and lifestyle — the 'why' behind a behavior. Combined with demographics (the 'who') and behavioral data (the 'what'), psychographics dramatically improve conversion modeling, especially in high-consideration verticals like financial services, luxury, healthcare, and political.
How is healthcare data sourced and is it HIPAA-compliant?+
Healthcare-vertical signals are derived from non-PHI sources: declared interest, consented panel behavior, search-pattern analysis, and patient-journey signals collected under opt-in. We do not transact on PHI. Use cases requiring PHI integration are handled via BAA and run inside customer infrastructure.
What does enrichment add to a lead?+
Enrichment layers add: verified contact data, household composition, wealth and credit indicators, employment and firmographic context (for B2B), behavioral cluster membership, psychographic overlay, and predictive score with confidence. Enrichment typically lifts downstream conversion 1.4–2.1x.
How do you measure data accuracy?+
Three ways: (1) identity resolution accuracy against a calibrated panel; (2) score calibration — how often a 70% probability actually converts 70% of the time; and (3) downstream business impact — CAC, ROAS, conversion lift. We publish calibration curves on request.
What is the difference between B2B intent and consumer intent?+
B2B intent typically operates over weeks-to-months, has a buying-committee structure (5–11 people), and is best captured through firmographic + behavioral signals. Consumer intent operates over hours-to-days, is individual, and is driven by behavioral and contextual signals. The platform supports both with separate model families.
Predictive intelligence · enterprise onboarding
Move from list-buying to probability-buying.
Engage your account team for a calibrated intelligence estimate, methodology walkthrough, and a sandbox environment scored against your own audience.