LOW-CODE AI ANALYTICS ASSISTANT IN AMAZON QUICK SUITE
OVERVIEW
An internal analytics team needed a faster, more consistent way to generate SQL queries, interpret data, and surface metric definitions from existing dashboards. Using Amazon Quick Suite’s low-code chat-agent framework, we developed an AI assistant capable of answering natural-language questions, referencing organizational knowledge, and producing high-quality SQL with detailed explanations. The agent reduced manual query-development time by roughly 50 percent and introduced a scalable path toward broader AI-enabled analytics workflows.
THE CHALLENGE
Our data analysts routinely received ad-hoc requests for new metrics or data pulls not represented in any dashboard. Producing these queries required knowledge of Salesforce data structures, table relationships, and logic conventions—expertise concentrated among a small number of team members. Existing workflows were time-consuming, difficult to scale, and inconsistent across analysts. The team needed a repeatable, self-service mechanism for generating accurate queries while maintaining shared definitions and standards.
OUR SOLUTION
We created a Quick Suite chat agent—a low-code AI assistant—that allowed users to ask full-sentence questions and receive structured SQL, metric definitions, and contextual explanations in return. The agent leveraged several key Quick Suite capabilities:
• Agent Persona: Defined identity, instructions, and communication style to ensure consistent, domain-appropriate responses.
• Knowledge Base (“Memory Bank”): A repository containing the team’s complete metric-definition wiki, sample SQL queries, terminology references, and supporting documentation.
• Natural-Language Interface: Allowed users to phrase questions conversationally, similar to modern AI assistants.
The knowledge base acted as the agent’s contextual foundation, teaching it the team’s specific terminology, how metrics are defined, and how SQL should be structured. Sample queries guided the agent to match the team’s preferred coding format and logic structure.
During testing, the agent handled an urgent new-data request—requiring a custom query not defined anywhere in dashboards—cutting development time approximately in half (from over an hour to about 30 minutes). The generated SQL included:
• A structured header comment describing the purpose of the query
• Code formatted to the analyst’s preferred styling
• A clause-by-clause explanation, plus guidance on modifying filters and date logic
The result was a reusable, self-service pattern for common analytics tasks.
KEY FEATURES
- • Low-code AI chat agent capable of SQL generation, metric definition retrieval, and partial dashboard interpretation.
• Knowledge base containing domain documentation, enabling consistent responses across analysts.
• Structured SQL output with explanatory notes, join logic, and modification instructions.
• Natural-language queries and full-sentence responses similar to broader AI assistant patterns.
• Output accuracy validated against manually written SQL to ensure reliability.
GLOBAL IMPACT/RESULTS
- • ~50% reduction in SQL development time for urgent data requests.
• Improved consistency through centralized metric definitions and reusable knowledge sources.
• Reduced reliance on specialized analysts for routine query creation.
• Established an internal model for operationalizing analytics tasks using low-code AI agents.
TECHNOLOGIES & SERVICES
Amazon Quick Suite — low-code chat agent creation and deployment.
Quick Suite Knowledge Sources (Spaces) — repository for metric definitions, sample queries, and domain documents.
Analytical dashboards (QuickSight/QuickSuite) — data source for partial dashboard-reading capabilities.
CONCLUSION
By converting existing documentation into a structured knowledge base and leveraging Quick Suite’s low-code agent builder, the team deployed a practical AI assistant that accelerated analytics workflows and improved data accessibility. The project demonstrated how low-code AI agents can transform repeatable analytical tasks while laying the groundwork for more advanced, customizable agent architectures in the future.