Where Azquo Helps
Where accumulating AI knowledge and provenance make a real difference.
A worked example — auto-rating in insurance
Underwriting data — rules, factors, exceptions, decisions taken over years of cases — imported into Azquo and used by AI agents during premium calculation. Each new edge case an underwriter resolves is captured in the database, immediately available to the next agent that handles a case on the same line of business.
Every premium produced can be traced back to the inputs that fed it, the rules that derived it, and the human or agent that decided each rule belonged.
AI with external data, anchored to your business
When an AI agent reaches outside for context — weather feeds, commodity prices, regulator updates, news, market data — the question becomes how to attach those signals to the entities the business already uses. Azquo’s hierarchies become the join point: weather attaches to regions and sites, indices attach to product categories, regulator updates attach to affected business lines.
External data lands in the same auditable store as the internal numbers. An AI agent prowling an existing database can pick up where the data ended and reason across both.
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Other shapes Azquo fits well
- Regulated reporting (financial, ESG, pharma) where every figure must be defensible.
- Pricing and tariff engines with exceptions that accumulate over time.
- Multi-source forecasting that combines internal data with external indicators.
- Customer-specific configurations where the reasoning behind each variation should be preserved.
- Acquisitions and consolidations where new data must integrate without schema migration.
A common thread: the answer needs to be both accumulating (today’s discoveries inform tomorrow’s) and defensible (every figure traceable to who, when, and how).