Turning retail data into decisions
Peramal helps retail and CPG organizations transform complex data into intelligent, accessible and actionable insights through AI, analytics and modern technology.
Retail generates data everywhere. Finding the right answer shouldn't require a data team.
Retail business teams rely on data across sales, orders, invoices, collections, customers and products. Yet answering everyday business questions can require technical teams to write SQL, interpret complex schemas and prepare reports.
Dependency on BI teams
Business users often depend on technical teams for routine data questions.
Complex data structures
Complex Snowflake schemas can make self-service analytics difficult.
Inconsistent business definitions
Metrics such as revenue, open orders and collection rate require precise definitions.
Data across multiple schemas
Business information can exist across different schemas and levels of detail.
Changing data structures
Frequent schema changes can make AI-generated SQL difficult to maintain.
Trust & transparency
Users need confidence in AI-generated answers and visibility into how results are produced.
From fragmented data to a shared business language
Technology that helps retail teams move from data to action
Conversational analytics
Enable business users to interact with enterprise data using natural language.
Data & analytics
Transform complex enterprise data into trusted, actionable business intelligence.
AI & machine learning
Apply generative AI and intelligent workflows to enterprise decision-making.
Semantic layer design
Create governed business definitions, metrics, relationships and SQL guidance.
Enterprise data platforms
Build secure data solutions around modern platforms such as Snowflake.
Cloud & application engineering
Build secure, scalable analytics applications and cloud-native services.
Transforming retail data into answers with conversational AI
- industry
- Retail & CPG
- domain
- Strategic Merchandising & Analytics
- service
- Conversational Analytics & Semantic-Layer Design
- technology
- Snowflake, Claude, Python, Flask, Azure
When business questions become technical requests
Business users depended on BI teams for routine data questions.
Complex Snowflake schemas made self-service analytics difficult.
Business metrics such as revenue, open orders and collection rate required precise definitions.
Data was distributed across multiple schemas and different levels of detail.
Frequent schema changes created challenges for AI-generated SQL.
Users needed transparency and confidence in AI-generated answers.
Connecting generative AI to production data required strong security controls.
Business definitions needed to evolve without requiring application redeployment.
Ask business questions in plain English. Get governed, auditable answers.
Peramal designed and deployed a self-service conversational analytics application that enables business users to ask questions in plain English and receive governed, auditable answers directly from Snowflake.
User
Business question
Ask AI
Natural-language interface
Claude
AI interpretation
Semantic layer
Business context
Live Snowflake metadata
Schema awareness
Generated SQL
Business-rule compliant
Read-only Snowflake
Governed execution
Result
Insight + chart
Ask. Understand. Decide.
What was our revenue last quarter?
SELECT SUM(revenue) AS total_revenue FROM sales.fact_sales WHERE quarter = 'Q4' AND year = 2024;
Revenue result
Revenue is calculated using the governed business definition from the semantic layer, ensuring consistency across all analytics.
Platform capabilities
Give AI a governed understanding of the business
A YAML-based semantic layer defines business metrics, rules, relationships and SQL guidance so business logic remains explicit, consistent and maintainable.
metric:
name: revenue
definition: Total invoiced sales amount
table: sales.fact_sales
column: revenue_amount
guidance: >
Use approved sales data only.
Exclude returns and adjustments.
metric:
name: collection_rate
definition: Percentage of invoiced amount collected
tables:
- sales.fact_invoices
- sales.fact_collections
guidance: >
Calculate as collected / invoiced.
Filter by fiscal period.Representative example — not actual production configuration.
Designed for accuracy, transparency and secure enterprise data access
Application flow
Schema editor
A schema editor allows data teams to update business definitions without redeploying the application.
Making enterprise analytics faster, more transparent and accessible
Answers in seconds
Enabled business users to get answers to routine data questions in seconds.
Reduced BI dependency
Reduced dependency on BI teams for ad-hoc analytics requests.
Consistent definitions
Established consistent definitions for key business metrics.
Transparent results
Improved trust through transparent, inspectable SQL and result sets.
No-deploy updates
Enabled data teams to manage business logic without engineering releases.
Secure AI architecture
Created a secure architecture for using generative AI with production enterprise data.
Natural-language access
Improved accessibility of Snowflake data through a natural-language interface.
Reusable pattern
Created a reusable conversational analytics pattern that can be extended to other business domains.
Powered by modern data, AI and cloud technologies
Snowflake
Enterprise data warehouseCentralized enterprise data warehouse providing governed access to sales, orders, invoices, customers, products and operational data.
Anthropic Claude
Generative AIGenerative AI translating natural-language business questions into structured, business-rule-compliant SQL and actionable insights.
Python + Flask
Application layerPython-based Flask application providing the application layer and analytics workflows.
Azure
Cloud platformAzure App Service providing the cloud deployment platform.
Ready to make retail data easier to understand?
Explore how AI, analytics and governed data platforms can help your teams make faster, more informed decisions.