12M+
active customers worldwide
1600+
natural health and wellness products
57+
years in operation
The client needed faster, self-service access to performance data spread across eight disconnected source systems, without requiring SQL expertise or compromising data privacy. Inferenz built a natural-language analytics agent on Snowflake Cortex Agent and Cortex Analyst, backed by a semantic model covering 40+ business metrics across 12+ marketing channels, an automated evaluation framework, and weekly quality monitoring, entirely within the client’s own Snowflake environment. Performance insights that once required SQL queries and dashboard-hopping are now delivered in under a minute through one conversational interface, with built-in accuracy checks running automatically every week.
The client wanted faster, self-service access to performance data without compromising data privacy or requiring SQL expertise. The existing approach created four core operational bottlenecks.
Business analysts without SQL expertise had no self-service path to performance data. Every insight request required an engineer or analyst to write and execute queries manually, creating constant bottlenecks across teams.
Key business metrics were spread across eight separate source systems. Assembling a unified view required complex joins and deep knowledge of stored procedure logic documented only internally.
Channel performance, customer retention, loyalty, subscription, and financial metrics existed in isolation — spread across referenced views, raw row-level records, and pre-aggregated tables simultaneously, with no interface to compare them.
ithout any way to measure or demonstrate response accuracy, gaining business user trust in an AI agent was a significant barrier. Teams were reluctant to act on AI-generated insights without independent verification.
Inferenz delivered a self-service analytics agent that turns plain-English questions into governed, accurate answers automatically, and entirely within the client's own Snowflake environment.
A Snowflake Cortex Agent backed by Cortex Analyst translates plain-English questions into optimized SQL, returning formatted answers with data tables and charts. Business users access 40+ performance metrics with zero SQL, and the agent resolves synonyms like “total revenue” or “first-time buyers” automatically.
Stakeholders previously navigated multiple platforms to piece together performance. The agent now consolidates every source into a single conversational interface and automatically surfaces 2-3 key observations with every response, highlighting trends and standout figures without requiring manual interpretation.
A purpose-built semantic model maps 40+ business metrics across 8 source systems and 28 table relationships, grounded in the client’s own data dictionary. This consolidates order data, retention, marketing channels, loyalty, subscriptions, and lifetime value into a single governed view, with synonym resolution ensuring any phrasing returns the correct result.
A 12-question evaluation dataset with ground-truth SQL scores every response across 5 metrics using an LLM-as-judge approach. A Snowflake Task runs this suite weekly and refreshes a full audit trail (questions, SQL, tokens, duration) daily, catching regressions automatically without manual oversight.
Insight generation
Performance insights that previously required SQL queries and dashboard-hopping across multiple platforms are now delivered instantly.
SQL knowledge required
Business users access 40+ performance metrics through plain-English questions, with no technical skills needed.
Data inside Snowflake
All queries run natively inside the client's warehouse, with zero data leaving the environment at any point.
Automated quality checks
The evaluation framework scores agent performance every week across five quality dimensions, catching regressions before they reach business users.
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