See how customers
before you ship
Instinct sends customer-calibrated agents through your product to predict behavior, uncover friction, and show you what to change before customers experience it.
INTEGRATIONS
Every click, screen, and outcome tracked from first visit to checkout
01
Model
02
Simulate
03
Decide
04
Automate
Build behavioral models of your customers
Simulate complete customer journeys
Know what to fix and what will move the business
Turn approved recommendations into tested improvements
Turn your existing research, analytics, and customer data into calibrated models of how different buyers decide, hesitate, and convert.
Built from real behavioral signals, not just demographic stereotypes
Captures differences in price sensitivity, trust, intent, attention, and patience
Calibrated against how your customers actually behave
Reusable across journeys, experiments, and product decisions
01 Model
Build behavioral models of your customers
Turn your existing research, analytics, and customer data into calibrated models of how different buyers decide, hesitate, and convert.
Built from real behavioral signals, not just demographic stereotypes
Captures differences in price sensitivity, trust, intent, attention, and patience
Calibrated against how your customers actually behave
Reusable across journeys, experiments, and product decisions
02 Simulate
Simulate complete customer journeys
Run behavioral models through your live web and mobile experiences to see how different customers navigate, decide, and respond from entry to outcome.
Simulate real journeys across web and mobile
Compare behavior across customer segments
Capture every click, screen, decision, and outcome
Run thousands of journeys across paths and scenarios
Replay every journey from start to finish
03 Decide
Know what to fix and what will move the business
Turn simulated journeys into prioritized findings that show where customers struggle, why it happens, and what changes are most likely to improve outcomes.
Identify friction, abandonment, and conversion risk
See the behavioral evidence behind every finding
Prioritize issues by impact and customer segment
Get specific recommendations for what to change
Compare proposed fixes before committing resources
04 Automate
Turn approved recommendations into tested improvements
Let Instinct agents implement approved changes, rerun the affected journeys, and verify whether customer outcomes improve.
Generate production-ready fixes for approved findings
Push changes into your existing development workflow
Automatically rerun affected journeys after each change
Verify improvements against the original finding
Continuously detect new issues as your experience evolves
Purpose-built for high-stakes consumer experiences.
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STUDIES
Assessments
Experiments
EVIDENCE
Session Replay
Heatmaps
Analytics
DESIGN
Workspaces
UI Chat
AUDIENCE
Personas
Cohorts
E-Commerce & Retail
Improve product discovery, merchandising, promotion performance, checkout conversion, average order value, and post-purchase experiences.
Media & Entertainment
Improve content discovery, engagement, subscription conversion, playback, advertising experiences, and retention.
Consumer Technology
Improve activation, feature adoption, engagement, account recovery, trust and safety, and cross-device experiences.
E-Commerce & Retail
Improve product discovery, merchandising, promotion performance, checkout conversion, average order value, and post-purchase experiences.
Media & Entertainment
Improve content discovery, engagement, subscription conversion, playback, advertising experiences, and retention.
Consumer Technology
Improve activation, feature adoption, engagement, account recovery, trust and safety, and cross-device experiences.
by
New
Home
STUDIES
Assessments
Experiments
EVIDENCE
Session Replay
Heatmaps
Analytics
DESIGN
Workspaces
UI Chat
AUDIENCE
Personas
Cohorts
E-Commerce & Retail
Improve product discovery, merchandising, promotion performance, checkout conversion, average order value, and post-purchase experiences.
Media & Entertainment
Improve content discovery, engagement, subscription conversion, playback, advertising experiences, and retention.
Consumer Technology
Improve activation, feature adoption, engagement, account recovery, trust and safety, and cross-device experiences.
Model Training System
A continuous architecture that turns enterprise behavior data into models, agents, and measurable outcomes.
Enterprise Behavior Data
Data Engineering
Enterprise Behavior Store
Continuous Model Training
System of Models
Agent Harness
Continuous Behavioral Feedback
Telemetry
Sessions
CRM
Transactions
Support
Experiments
Behavior data
The raw behavioral signals captured across the enterprise, every source feeding one continuous record of how customers actually act.
Telemetry
Experiments
Sessions
CRM
Transactions
Support
Behavior data
The raw behavioral signals captured across the enterprise — every source feeding one continuous record of how customers actually act.
Enterprise Behavior Data
page_view
lifecycle_stage
refund
nav_path
chat_message
variant_exposure
refund
click
dwell
add_to_cart
ticket
conversion
email_open
checkout
PURCHASE
scroll
session_start
profile_update
ADD_TO_CART
csat
Structured records
Timestamp
User
Event
Source
Value
14:22:03.114
u_8841
page_view
telemetry
/pdp/4471
14:22:05.902
u_8841
add_to_cart
transactions
sku_4471 ×1
14:22:06.550
u_2207
email_open
crm
camp_win14
14:22:09.318
u_8841
checkout
transactions
$58.00
Raw, heterogeneous behavior is parsed, de-duplicated and normalized — resolved into clean, structured records.
Data Engineering
Behavior store · Profiles
Entity
Sessions
Purchases
Preferences
Support
u_8841
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Structured records are organized into a persistent, queryable profile — behavior accumulating across dimensions, per customer.
Enterprise Behavior Store
continuous refinement
target fit · 0.80
Behavioral models train on the store, then evaluate and tune — continuously — improving fit with every iteration as outcomes feed back.
Continuous Model Training
Layer 1
Population
Layer 2
Individual
Layer 3
Outcome
A layered system of models — population, individual and outcome — turns structured behavior into predicted actions for each consumer agent.
System of Models
The harness runs each model as a live agent — observing, acting and verifying — then deploys it as a versioned consumer.
Agent Harness
The Models
store · training ·system of models
In Production
store · training ·system of models
Behavior
Outcomes
Evaluation
Corrections
Deployment isn't the end of the pipeline — it's the start of the loop. Agents in production surface real behavior and outcomes; those signals are evaluated, corrections feed back into the models, and the system retrains.
Continuous Behavioral Feedback
Telemetry
Experiments
Sessions
CRM
Transactions
Support
Behavior data
The raw behavioral signals captured across the enterprise — every source feeding one continuous record of how customers actually act.
Enterprise Behavior Data
page_view
lifecycle_stage
refund
nav_path
chat_message
variant_exposure
refund
click
dwell
add_to_cart
ticket
conversion
email_open
checkout
PURCHASE
scroll
session_start
profile_update
ADD_TO_CART
csat
Structured records
Timestamp
User
Event
Source
Value
14:22:03.114
u_8841
page_view
telemetry
/pdp/4471
14:22:05.902
u_8841
add_to_cart
transactions
sku_4471 ×1
14:22:06.550
u_2207
email_open
crm
camp_win14
14:22:09.318
u_8841
checkout
transactions
$58.00
Raw, heterogeneous behavior is parsed, de-duplicated and normalized — resolved into clean, structured records.
Data Engineering
Behavior store · Profiles
Entity
Sessions
Purchases
Preferences
Support
u_8841
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
■ □ □ □
u_2207
■ ■ ■ ■
■ ■ ■ ■
■ ■ □ □
■ □ □ □
u_5093
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
u_3310
■ ■ ■ □
□ □ □ □
■ □ □ □
■ ■ ■ □
u_7742
■ ■ ■ □
■ ■ ■ ■
■ ■ □ □
■ ■ ■ ■
u_9008
■ □ □ □
■ □ □ □
■ ■ □ □
■ ■ □ □
u_1156
■ ■ ■ ■
■ ■ □ □
■ ■ ■ □
□ □ □ □
u_6420
■ ■ □ □
■ ■ ■ ■
■ □ □ □
■ ■ ■ □
Structured records are organized into a persistent, queryable profile — behavior accumulating across dimensions, per customer.
Enterprise Behavior Store
continuous refinement
target fit · 0.80
Behavioral models train on the store, then evaluate and tune — continuously — improving fit with every iteration as outcomes feed back.
Continuous Model Training
Layer 1
Population
Layer 2
Individual
Layer 3
Outcome
A layered system of models — population, individual and outcome — turns structured behavior into predicted actions for each consumer agent.
System of Models
The harness runs each model as a live agent — observing, acting and verifying — then deploys it as a versioned consumer.
Agent Harness
The Models
store · training ·system of models
In Production
store · training ·system of models
Behavior
Outcomes
Evaluation
Corrections
Deployment isn't the end of the pipeline — it's the start of the loop. Agents in production surface real behavior and outcomes; those signals are evaluated, corrections feed back into the models, and the system retrains.
Continuous Behavioral Feedback
Telemetry
Experiments
Sessions
CRM
Transactions
Support
Behavior data
The raw behavioral signals captured across the enterprise — every source feeding one continuous record of how customers actually act.
Enterprise Behavior Data
page_view
lifecycle_stage
refund
nav_path
chat_message
variant_exposure
refund
click
dwell
add_to_cart
ticket
conversion
email_open
checkout
PURCHASE
scroll
session_start
profile_update
ADD_TO_CART
csat
Structured records
Timestamp
User
Event
Source
Value
14:22:03.114
u_8841
page_view
telemetry
/pdp/4471
14:22:05.902
u_8841
add_to_cart
transactions
sku_4471 ×1
14:22:06.550
u_2207
email_open
crm
camp_win14
14:22:09.318
u_8841
checkout
transactions
$58.00
Raw, heterogeneous behavior is parsed, de-duplicated and normalized — resolved into clean, structured records.
Data Engineering
Behavior store · Profiles
Entity
Sessions
Purchases
Preferences
Support
u_8841
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
■ □ □ □
u_2207
■ ■ ■ ■
■ ■ ■ ■
■ ■ □ □
■ □ □ □
u_5093
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
■ ■ ■ ■
u_3310
■ ■ ■ □
□ □ □ □
■ □ □ □
■ ■ ■ □
u_7742
■ ■ ■ □
■ ■ ■ ■
■ ■ □ □
■ ■ ■ ■
u_9008
■ □ □ □
■ □ □ □
■ ■ □ □
■ ■ □ □
u_1156
■ ■ ■ ■
■ ■ □ □
■ ■ ■ □
□ □ □ □
u_6420
■ ■ □ □
■ ■ ■ ■
■ □ □ □
■ ■ ■ □
Structured records are organized into a persistent, queryable profile — behavior accumulating across dimensions, per customer.
Enterprise Behavior Store
continuous refinement
target fit · 0.80
Behavioral models train on the store, then evaluate and tune — continuously — improving fit with every iteration as outcomes feed back.
Continuous Model Training
Layer 1
Population
Layer 2
Individual
Layer 3
Outcome
A layered system of models — population, individual and outcome — turns structured behavior into predicted actions for each consumer agent.
System of Models
The harness runs each model as a live agent — observing, acting and verifying — then deploys it as a versioned consumer.
Agent Harness
The Models
store · training ·system of models
In Production
store · training ·system of models
Behavior
Outcomes
Evaluation
Corrections
Deployment isn't the end of the pipeline — it's the start of the loop. Agents in production surface real behavior and outcomes; those signals are evaluated, corrections feed back into the models, and the system retrains.
Continuous Behavioral Feedback
Choose a review, get findings, evidence, and the next action, all in one run, so you know what to fix before release
Behavioral Journey Research
Where do customers hesitate, change course, abandon, or succeed?
Decision-Making Research
What drives customers toward one choice over another?
Friction & Persistence
What makes customers continue, hesitate, switch paths, or give up?
Attention & Comprehension
What gets noticed, understood, overlooked, or misinterpreted?
Behavioral Segmentation
How does behavior differ across motivations, preferences, and constraints?
Counterfactual Simulation
How would customer behavior change if the experience changed?
Behavioral Journey Research
Where do customers hesitate, change course, abandon, or succeed?
Decision-Making Research
What drives customers toward one choice over another?
Friction & Persistence
What makes customers continue, hesitate, switch paths, or give up?
Attention & Comprehension
What gets noticed, understood, overlooked, or misinterpreted?
Session Replay
Capture every customer journey across web and mobile, then turn those interactions into behavioral evidence you can analyze, compare, and act on.
Full analytics
Funnels, paths, cohorts, conversion, friction, and behavioral trends
A/B testing
Measure how product variants change customer behavior and outcomes.
Enterprise ready
Privacy controls, secure capture, governance, and role-based access.
Web + mobile
High-fidelity capture across browser and native applications.
Bring the journey your team cannot afford to get wrong
We will run it with Instinct and show you the decision evidence.
Bring the journey your team cannot afford to get wrong
We will run it with Instinct and show you the decision evidence.
Bring the journey your team cannot afford to get wrong
We will run it with Instinct and show you the decision evidence.