Lifecycle workflow overview
Think of this workspace as an operating rehearsal: signal intake, opportunity ranking, and campaign generation are shown as one continuous decision loop.
1) Seed users and entities
Seed creates users, entities, interest edges, and entity deltas so the demo starts with realistic graph relationships.
2) Detect change deltas
New deltas represent high-intent changes (email updates, legal records, phone changes) that can trigger outreach.
3) Match + score candidates
Each delta is matched against user interest edges and scored for priority using segment, recency, and interest strength.
4) Generate lifecycle copy
Top candidates get generated subject lines, preview text, email body, and landing copy using OpenAI.
5) Review campaign run
The run stores KPIs (matches, high-priority opportunities, estimated revenue) so you can inspect performance over time.
Business context (why this matters)
Users
People with prior intent history. Context matters because monetization depends on sending relevant outreach to the right segment at the right time.
Entities
Products/companies/listings users care about. Entity-level context is what ties external change to a commercially meaningful trigger.
Change events
Observed deltas (new phone, updated record, status change). These events create urgency windows where conversion probability is materially higher.
Metric guide
Deltas detected
Number of entity profile changes currently in the demo graph.
Campaign candidates
Total user-entity opportunities created from matching delta events to interest edges.
Generated messages
Candidates that have generated copy assets ready for review or launch.
Live lifecycle flow
Current app data
Lifecycle app data
Self-contained sample data is ready
Lifecycle can run immediately with seeded sample users, entities, interest edges, and events.
Active source: current lifecycle sample app tables. Sign in to apply imported workspace data.
App data: current lifecycle database rows
Workspace import: Not imported
Workspace source: Workspace Google Sheets import (Google Sheets)
Volume and conversion snapshotThese counts show whether the graph has enough events, matches, and messages to support a useful demo run.
KPI Snapshot
Quick visual of current funnel volume.
Event-to-message pipelineThe pipeline shows where commercial opportunity narrows from raw change events to usable message assets.
Lifecycle pipeline diagram
Signal flow from change detection to modeled commercial impact.
Entity Deltas
Campaign Candidates
Generated Messages
Projected Outcomes
Text alternative: Entity Deltas flow into Campaign Candidates, then Generated Messages, then Projected Outcomes.
Outcome mix
Lifecycle run infographic
Signals detected
Candidates scored
Messages generated
Signal-to-message conversion: 0.0%
Revenue proof foundation
warning
Lifecycle launch baseline
1 treatment · 8 control · low confidence
Expected baseline
Observed revenue
Incremental revenue
Billable lift candidate
Baseline conversion
Observed conversion
Evidence chain
Agent actions
- 0 generated lifecycle messages · pending
- 0 scored candidates · pending
Exports
- Agent audit export · audit
- Lifecycle audit · actions
Confidence flags
- Demo proof uses current workspace counts until frozen customer baselines are persisted.
- Observed revenue is missing or zero.
- Confidence is low; keep proof directional until reviewed.
Operator decision canvas
A shared frame for how inputs become governed actions and measurable learning.
Inputs
- Market/entity signal stream
- User or audience intent graph
- Budget + policy constraints
Decisions
- Priority scoring and ranking
- Intervention/playbook selection
- Budget and channel allocation
Actions
- Message/creative generation
- Campaign launch + pacing
- Override and approval checkpoints
Learning
- Outcome and efficiency metrics
- Counterfactual comparison
- Next-iteration policy updates
Workspace data operations
Reset workspace data
Clears lifecycle workspace records, then reseeds representative lifecycle baseline data.
Underlying graph topology
Pipeline map
Users and entities form relationship edges, events trigger scoring, then campaigns and messages are generated.
Users
Entities
Relationships
Events
Candidates
Messages
Graph complexity view
Edges / user
Edges / entity
Candidates / event
Messages / candidate
Estimated active relationship clusters: 10. This is a proxy to guide future multi-hop and cluster-based graph exploration.
Influence path explorer (beta)
Multi-hop relationship view with lightweight cluster and path-strength signals.
Interpretation: Discovery captures broad intent collection, Intent shows concentrated qualification pressure, and Conversion reflects where operator-ready actions are most likely to emerge.
Use the assumption controls below to preview how stronger recency emphasis or tighter priority thresholds alter link strength and cluster influence before running a full simulation.
Assumption sensitivity (preview)
Avg neighbors / user
Event pressure / entity
Path templates
Cluster influence map
Strongest current path
Discovery → Intent (w7)
Discovery
Nodes: 30
Influence: 16
Intent
Nodes: 27
Influence: 6
Conversion
Nodes: 223
Influence: 19
Discovery → Intent
Path strength: w7
Intent → Conversion
Path strength: w7
Discovery → Conversion
Path strength: w1