System walkthrough

Watch cognition and agency operate as one intelligence system.

See Brain Engine reconstruct customer state and choose the intervention, then watch Atlas coordinate the response and return the result to the system.

Setup simulator

Connect OrthancIQ to the systems where customer value is created.

Start with a guided import, a read-only Atlas Data Agent, or API and webhooks. Atlas maintains approved live context; Brain Engine turns it into customer-state intelligence.

Fastest way to start

Build the first Customer Signal Graph with a guided import.

Use CSV or JSON exports, product documentation, billing snapshots, CRM context, and support history. Atlas maps the first customer context before Brain Engine initializes the state model.

InputsExports, docs, billing, support
Connection motionGuided signal mapping
First useful output48 hours after data boundary
Best fitRevenue Intelligence Audit
Brain coverage58%
Automation levelManual review
Trust boundaryExplicit exports only
Private rollout path

Install a read-only Atlas Data Agent for recurring customer monitoring.

Atlas can sit beside approved systems, maintain customer context, and send only the signals your team has authorized for risk diagnosis and action.

InputsWarehouse, product DB, support, CRM
Connection motionRead-only connector review
First useful output24 hours after install
Best fitGrowth
Brain coverage72%
Automation levelSchema-aware
Trust boundaryApproved fields only
Operational inbox path

Stream customer events into a living Customer Signal Graph.

API events and webhooks keep Atlas and Brain Engine current across usage, activation milestones, billing movement, support pressure, and ownership changes.

InputsEvents, webhooks, CRM, billing
Connection motionEvent contract + validation
First useful outputDaily refresh cycle
Best fitScale / Enterprise
Brain coverage84%
Automation levelContinuous
Trust boundaryValidated event contract

Governed intelligence. Brain Engine reasons only over approved context, and Atlas acts only within the permissions you define. Sensitive interventions can remain approval-gated, with evidence attached throughout.

Live walkthrough

Follow one customer through the Revenue Continuity Loop.

Watch customer context become cognition, cognition become intervention, and intervention become new intelligence. Click any stage, or let the sequence run.

Sources · Bring your evidence
Upload the evidence your dashboard does not understandCSV, PDF, Markdown, JSON · or connect a source
DOCProduct docs12 files
EVTEvent dictionaryparsed
CRMCRM notes238 rows
SUPSupport / churnsynced
BILBillingconnected
USGUsage events87%…
Customer Value Map · Value mechanics
User value moments inferred from your product model
Connected first data source
Activation
Ran core workflow 3+ / week
Habit
Invited a second teammate
Expansion
Exported a result downstream
Stickiness
Reached reporting milestone
Proof
Evidence blueprint · Custom signal catalog
SignalTypeReliability
core_workflow_runs_7dbehavioral
0.91
champion_last_activerelational
0.84
seats_active_ratioadoption
0.72
support_sentiment_30dsignal
0.61
invoice_eventsbilling
0.55
+ 24 more signals compiled, weighted, and tied back to the product model
Brain Engine model · Maya Chen
Relationship state
ChurnValue
90-day churn hazard
31%
▲ rising · was 19%
Recovery potential
68%
likely movable with action
Expected saved revenue
$29k
of $42k value at risk
Revenue Continuity Queue · Today
01
M
Maya Chen
Workflow owner · 14 seats
Recoverable
$42kValue
Core workflow drop + owner inactive
Book call
02
N
Noah Patel
Ops lead · renewal owner
Critical
$18kValue
Renewal in 14 days + value gap
Check-in
03
H
Hannah Lee
Research lead · dormant
Low leverage
$80kValue
Cancel intent · no usage 31d
Park for now
User evidence · Maya Chen
Maya Chen
$42k value · confidence 0.88 · priority 01
Recoverable risk
Dominant cause
Core workflow runs down 61% over 3 weeks; Maya last active 19 days ago.
Supporting signals
9 signals firing · seats_active_ratio ↓ · support_sentiment flat
Recovery potential68%
Atlas intervention
Book a workflow-recovery call; re-onboard Maya around exports.
Outcome feedback · Close the loop
After your call with Maya Chen, what happened?
Saved — workflow recovered
Still at risk — needs follow-up
Churned anyway
Wrong call — not actually at risk
OrthancIQ calibrates from outcomes. Every logged result updates signal reliability and recovery-potential estimates, making the next intervention sharper and more operational.
Decision lab

Change the customer state. Watch the entire system adapt.

The same activity drop can mean different things for different customers. Brain Engine changes the inferred state and intervention leverage; Atlas changes the operating plan.

Selected user

Maya Chen

Workflow owner · 14 seats · export workflow adopted

Recoverable
90-day churn pressure +18 pts over baseline
risk gap

The vertical gap is the signal: how far this user's risk has moved beyond normal behavior for the same product stage.

Risk pressure 31%
Saveability 68%
Expected saved revenue $29k risk × saveability × value
Dominant signal Core workflow drop Owner inactive 19d
Risk case evidence
  • Workflow runs down 61% over 3 weeks.
  • Maya was last seen 19 days ago.
  • Support sentiment is neutral, not hostile.
  • Export milestone was previously completed.
Intervention test

Book a workflow-recovery call and re-onboard Maya around the export workflow she originally adopted.

Projected saveability after action 74%

Run OrthancIQ on real customer signals.

Connect the first signal path, initialize the customer-state model, and generate your first Revenue Continuity Queue from real context.

Join private rollout Connect OrthancIQ