Network Operations
This page explains how OptimAI Network moves from user activity to validated intelligence and rewards. It is written for readers who want the mechanics without the pitch.
Operational Model
OptimAI has four operational loops:
- Task demand: A product, user, builder, or campaign requests search, extraction, validation, compute, or monitoring.
- Node execution: Eligible nodes receive tasks based on capability, reputation, availability, and permissions.
- Reinforcement validation: Results are checked for provenance, freshness, accuracy, duplication, and usefulness.
- Settlement: Contributions update node reputation and reward status.
Data Flow
Node Roles
| Role | Typical work |
|---|---|
| Lite Node | Lightweight validation, bandwidth contribution, referrals, and simple data tasks. |
| Core Node | Browser-native execution, Claw runtime, extraction, compute, storage, and campaign jobs. |
| Edge Node | Mobile participation, local context, edge preprocessing, and future IoT workflows. |
| Validators | Review samples, label data, confirm relevance, and provide human feedback. |
Task Types
- Search refresh: gather or refresh sources for a query.
- Claw extraction: extract structured data from approved sources.
- Validation: review data quality, source relevance, or extracted records.
- Annotation: label text, images, entities, sentiment, or categories.
- Compute: run preprocessing, embeddings, inference support, or dataset processing.
- Monitoring: repeat a query or extraction job over time.
Privacy And Security Principles
OptimAI should be clear about user control. The strongest documentation does not hide behind vague words like “secure”; it explains the model.
- Permission first: authenticated or personal sources require user approval.
- Local processing where possible: sensitive raw context should stay on the device when workflows allow.
- Anonymization: network-submitted outputs should avoid personal identifiers unless the user explicitly chooses otherwise.
- Encryption: node communication should be encrypted in transit.
- Resource controls: users should be able to set limits for bandwidth, compute, storage, and task types.
- Auditability: valuable outputs should include source metadata and task history.
Quality Controls
OptimAI’s data quality model should combine automated checks and human judgment:
- duplicate detection
- source reputation
- freshness scoring
- validator agreement
- node reputation
- anomaly detection
- user feedback
- downstream product usefulness
Reward Inputs
Reward calculations should be quality-weighted. Useful inputs include:
- task difficulty
- result quality
- validation accuracy
- uptime and reliability
- compute or bandwidth contributed
- campaign demand
- reputation history
What To Verify Before Launching A Task
- Does the task require user permission?
- Is the expected output schema clear?
- Does the task need validation?
- What data can be shared with the network?
- What reward budget or fee applies?
- How will the result be used by Search, Claw, Persona, or an API?