AscendQuery GenAI Natural Language SQL Assistant

## The Challenge

Organizations struggle with delayed reporting cycles, growing analytics backlogs, and heavy dependence on technical teams for routine data requests. Business users in finance, sales, operations, customer service, and leadership often wait days for answers that should take seconds.

## How It Works

AscendQuery transforms natural language questions into accurate, schema-aware SQL queries executed against your existing AWS data infrastructure:

• **Supported databases:** Amazon RDS, Amazon Aurora, and Amazon Redshift
• **AI engine:** Amazon Bedrock powers context-aware query generation
• **Interface:** Intuitive conversational experience for ad hoc data exploration

Users simply type business questions in plain language, and AscendQuery generates optimized SQL, executes it securely, and returns results in real time.

## Industry Use Case: Retail Finance

A retail finance team can ask AscendQuery questions like "What were our top 10 products by gross margin last quarter?" against their Aurora database. Instead of submitting a ticket to the data team and waiting days for a dashboard update, the finance analyst receives an immediate answer - enabling faster pricing decisions and margin optimization.

## Engagement Structure

This professional services engagement follows a structured delivery model:

• **Phase 1 - Discovery and Scoping (1-2 weeks):** Assessment of existing data infrastructure, schema review, identification of priority use cases, and definition of access control requirements.
• **Phase 2 - Configuration and Deployment (2-4 weeks):** Schema mapping, AI model configuration, integration with target databases (RDS, Aurora, or Redshift), IAM policy setup, and deployment of the serverless architecture within the buyer's AWS account.
• **Phase 3 - Validation and Training (1-2 weeks):** Query accuracy testing, user acceptance testing, end-user training sessions, and documentation delivery.

**Deliverables include:** Deployed AscendQuery instance, configured schema mappings, access control policies, query audit logging, user training materials, and administration documentation.

## Prerequisites and Scope

• Active AWS account with Amazon RDS, Aurora, or Redshift databases
• Database schemas must be accessible via standard SQL metadata
• Buyer provides IAM permissions and network access to target databases
• Buyer designates a technical point of contact (DBA or data engineer) for schema access

## Enterprise Governance and Security

• Role-based access controls via AWS IAM
• Query auditing and logging for compliance visibility
• Data remains within the buyer's AWS account
• Encryption in transit via TLS and at rest via AWS-managed encryption
• No customer data is stored outside the buyer's environment

## Key Benefits

• **Reduce analytics backlogs:** Enable business users to self-serve routine data questions
• **Lower BI licensing costs:** Reduce dependency on additional analytics platforms
• **Accelerate decisions:** Shorten the path from question to insight from days to seconds
• **Maintain governance:** Enterprise-grade access controls and audit trails

To get started, request a guided demo or schedule a discovery call to assess your environment and define a proof-of-concept scope.