What challenges did VideoAmp face before AI implementation?

Manual analysis of high-dimensional media analytics data was time-consuming. Business users needed a way to query complex datasets without SQL expertise. The team needed a low-latency, cost-effective conversational interface that could handle domain-specific analytics terms and calculations.

What was the impact of the AI solution?

The evaluation framework and human review reported high accuracy on the prototype's test samples. The solution was designed to keep latency and cost low per question. VideoAmp expects the chatbot to reduce time to business insights and make complex data exploration accessible to technical and non-technical users. The company is preparing for a near-term customer beta launch.

Source

ExploringEvidence: Low40/100

VideoAmp NL Analytics Chatbot with Amazon Bedrock text-to-SQL for media analytics

Use case typeDecision supportUpdated Jun 13, 2026

VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center to develop a prototype natural-language analytics chatbot for its media analytics data. The solution is designed to let non-technical users ask questions in natural language and receive SQL-generated answers, summaries, and retrieved data from VideoAmp's analytics warehouse.

Organization: VideoAmp
Industry: Professional Services
Location: United States
Published: June 2025

Reported outcomes

Strategic outcomes

New product / capabilityBuilt a natural-language analytics chatbotCustomer experience & trustMade complex data exploration accessibleSpeed & agilityReduced time to business insightsNew product / capabilityPrepared for customer beta launch

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: VideoAmp
Provider: AWS
Maturity: Exploring
Linked source: AWS Machine Learning Blog

An automated evaluation pipeline was created to compare generated SQL and retrieved data against ground truth and support prompt optimization

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Conversational Analytics
2Text-to-SQL
3Data Exploration

Manual analysis of high-dimensional media analytics data was time-consuming.
Business users needed a way to query complex datasets without SQL expertise.
The team needed a low-latency, cost-effective conversational interface that could handle domain-specific analytics terms and calculations.

VideoAmp built the VideoAmp Natural Language Analytics Chatbot using Amazon Bedrock and Anthropic Claude 3 models.
The workflow includes a question rewriter for conversational context, a text-to-SQL component, SQL execution against the data warehouse, and a data-to-text summarization step.
The implementation also uses Amazon Bedrock Knowledge Bases, Amazon Bedrock Prompt Management, and Amazon Bedrock Guardrails, plus AWS Secrets Manager and AWS Key Management Service for secure credential handling.
An automated evaluation pipeline was created to compare generated SQL and retrieved data against ground truth and support prompt optimization.

Technologies

Amazon Bedrock Amazon Bedrock Knowledge Bases Amazon Bedrock Prompt Management Amazon Bedrock Guardrails AWS Secrets Manager AWS Key Management Service Anthropic Claude 3

The evaluation framework and human review reported high accuracy on the prototype's test samples.
The solution was designed to keep latency and cost low per question.
VideoAmp expects the chatbot to reduce time to business insights and make complex data exploration accessible to technical and non-technical users.
The company is preparing for a near-term customer beta launch.

Architecture

A frontend conversational UI sends user questions to a question rewriter model using Claude 3 Sonnet. The rewritten question is passed to a text-to-SQL model using Claude 3 Sonnet, which generates a SQL query and explanation based on database context. The SQL query executes against VideoAmp's data warehouse through connector code. Retrieved results are then summarized by a data-to-text model using Claude 3 Haiku. The solution includes Amazon Bedrock Knowledge Bases for RAG, Amazon Bedrock Prompt Management, Amazon Bedrock Guardrails, and secure credential storage with AWS Secrets Manager and AWS Key Management Service. An evaluation pipeline compares generated SQL and retrieved data with ground truth outputs.

Implementation partners1

AWS Generative AI Innovation Center

Sources & evidence1

Evidence: Low40/100Evidence strength