| Product | Upsolve Data Models |
| Website | upsolve.ai |
| Category | Data Analytics / Semantic Layer |
| Key Feature | Define metrics and vocabulary for AI-powered analytics |
What Is Upsolve Data Models?
Upsolve Data Models lets you teach AI your company’s specific metric definitions and business vocabulary. When someone asks “what’s our MRR?”, the AI needs to know exactly how your company calculates MRR — which subscriptions count, how trials are handled, which currencies are converted. Upsolve Data Models captures these definitions so AI analytics tools answer correctly.
The core problem: every company has its own language for business metrics, and AI tools that do not know this language give wrong answers. “Active users” means different things at different companies. “Revenue” might include or exclude certain transaction types. Upsolve creates a semantic layer that bridges your company’s language and your data.
Key Features
Metric Definitions
Define how each metric is calculated: the source tables, the filters, the aggregation logic, the time granularity. Once defined, any AI tool that queries your data uses the same calculation. No more disagreements about numbers because two dashboards calculate the same metric differently.
Business Vocabulary
Map business terms to their data equivalents. “Churned customer” might mean “subscription_status = cancelled AND cancellation_date within last 30 days AND had_payment = true.” Teach the AI this mapping once, and every query that mentions “churned customers” uses the correct definition.
AI-Ready Semantic Layer
The definitions and vocabulary form a semantic layer that AI analytics tools can consume. Instead of each AI tool guessing what your metrics mean, they query the semantic layer and get authoritative definitions. This improves accuracy for any AI-powered analytics, reporting, or natural language query tool.
Who Is This For?
- Data teams tired of different tools calculating the same metric differently
- Companies using AI analytics that need consistent, accurate answers
- Business teams who want to query data in their own vocabulary
- Analytics engineers building a single source of truth for metrics
Pros
- Single source of truth for metric definitions
- Maps business vocabulary to data logic
- Improves AI analytics accuracy
- Eliminates metric disagreements across tools
- Reusable semantic layer for any AI integration
Cons
- Requires upfront investment in defining metrics
- Maintenance burden as metrics evolve
- Value depends on AI analytics adoption
- Semantic layer is an additional abstraction to maintain
- May overlap with existing dbt or Cube.js semantic layers
Verdict
Upsolve Data Models addresses a fundamental problem in AI-powered analytics: AI tools do not know your company’s business vocabulary. Without a semantic layer, every AI analytics tool guesses at metric definitions, and guesses are wrong often enough to erode trust.
The value proposition increases as more AI tools query your data. Each tool that connects to Upsolve’s semantic layer uses the same definitions, so the upfront investment in defining metrics pays dividends across every integration.
For data teams already maintaining metric definitions in dbt or similar tools, Upsolve may overlap. For teams without a semantic layer, it is a practical starting point for making AI analytics trustworthy.
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