Norynta Data License and Commercialization Notes

Purpose

This document outlines a practical commercialization and usage framework for Norynta data products.

Canonical posture

Norynta's canonical commercialization posture is to sell privacy-safe market intelligence products and derived analytics, not raw personal-data brokerage.

Recommended commercialization model

The recommended approach is to commercialize:

• market-level analytics
• public/on-chain activity enriched with Norynta metadata
• aggregated liquidity, spread, activity, and ranking signals
• historical exports and recurring API access

The recommended approach is not to commercialize raw personal data. Norynta should not silently collect device fingerprints or raw location profiles for resale.

Permitted use examples

• research and analytics
• market making and quantitative modeling
• dashboards and monitoring
• editorial and intelligence products
• agent and bot decision support

Prohibited use examples

• re-identifying natural persons
• combining Norynta data with external private datasets to deanonymize users
• discriminatory profiling
• selling onward access in violation of Norynta license terms
• using non-public Norynta data without authorization

Restricted data categories

These categories should not be part of the sellable default data surface unless separately reviewed by counsel and platform policy owners:

• emails
• phone numbers
• IP addresses
• device fingerprints
• private KYC or AML records
• any identity-linked account recovery or support data
• precise location profiles

Product design guidelines

• prefer aggregated outputs over user-level outputs
• apply minimum cohort thresholds where applicable
• separate sellable datasets from internal raw data pipelines
• keep explicit audit logs for exports and enterprise deliveries
• document freshness, schema stability, and allowed usage clearly
• require policy/compliance review before introducing new sellable field classes

Operational governance requirements

• classify fields into public, derived, and restricted categories
• restrict default commercial products to public + derived
• maintain export/delivery audit trails (customer, dataset, purpose, volume)
• enforce retention schedules by dataset class
• require explicit review for any release with re-identification risk

Go-to-market notes

The highest-probability first offering is usually:

1. market intelligence API
2. premium signal feed
3. historical trade export
4. enterprise delivery / bulk exports

Recommended packaging ladder

Starter

• ranked market intelligence only
• suitable for dashboards, research, and evaluation
• lower request budgets and optional delayed freshness
• clean monetization lever via enforced delayed data access

Pro

• ranked market intelligence
• derived signal feed (/api/data/signals)
• normalized trade export
• normalized public wallet position export
• designed for active agents, quants, and market makers

Enterprise

• everything in Pro
• premium historical market-intelligence history
• bulk exports, recurring delivery, and custom schemas
• partner SLA / support expectations

Why the signal layer matters

The strongest monetization story is usually not raw public data alone. It is the combination of:

• public/on-chain facts,
• Norynta normalization,
• Norynta ranking logic,
• Norynta-derived signals that reduce downstream analysis work.

That is what turns a dataset into an intelligence product.

Operational monetization note

Where appropriate, commercial API keys can enforce freshness delays directly in the runtime using per-key delaySeconds. This gives Norynta a concrete way to sell delayed Starter access while preserving real-time or near-real-time value for Pro and Enterprise customers.

Related docs

• docs/public/DATA_API.md
• docs/BLOCKCHAIN_COMPLIANCE_CONTROLS.md
• docs/ARCHITECTURE.md