<span>AI-Powered Customer</span> Insight Agents | SQUAD Tech

AI-Powered Customer Insight Agents

SQUAD unified 13+ siloed customer feedback data sources into three AI agents that surface product issues in minutes instead of days of manual analysis.

3 AI agents delivered

from concept to production in 3 months

13+ data sources unified

now accessible through a single interface

Time to insight reduced to minutes

replacing days and weeks of manual analysis

Client at a Glance

Service Type

AI agents for product insights and customer feedback analysis

Industry

Smart home devices and consumer electronics

Engagement

AI agent development for product management and customer insight workflows

Region

Global

The client is a Devices Product Management team responsible for product strategy, customer experience, and quality oversight across a smart home device portfolio.

Challenge

The client had no scalable way to understand what customers were saying about their products. Customer feedback was scattered across
13+ disconnected sources, including store reviews, app store ratings, social media platforms, community forums, customer support call summaries, return comments, and in-app feedback. Each source lived in a different system, was owned by a different team, and used a different format.

This created several issues:

Product issues took days or weeks to surface, so teams often reacted only after many customers had already been affected.

There was no cross-source analysis, which meant related signals from reviews, forums, and support channels were never connected.

Teams could not link customer complaints to device metrics, so investigations relied on anecdotal feedback instead of measurable evidence.

Manual analysis did not scale across thousands of new records arriving daily.

The client needed a way to unify customer feedback, enrich it with structured analysis, connect it to device data, and surface product issues in a format product managers could use immediately.

Solution

SQUAD built and deployed three AI agents that continuously ingest, enrich, and analyze customer feedback, delivering insights that previously required days of manual work in seconds.

The main elements of the solution were:

Development of a Customer Feedback and Insight Agent that surfaces top issues across all sources and delivers investigation recommendations through collaboration tools.

Development of a Star-Rating and Reviews Agent that enables conversational trend analysis without manual dashboard exploration.

Development of a Reviews and Device Metrics Agent that links customer complaints with device-level metrics, allowing teams to connect reviews to measurable device conditions.

Creation of a shared architecture with automated ingestion, a pre-processing pipeline for normalization and sentiment analysis, an S3 data lake, Redshift for structured analytics, and integration into an internal platform with chat and workflow connectors.

The delivery approach was staged

to keep progress moving while the solution was being defined and expanded:

Prototype with public data to validate the technical architecture end to end and create a working demo that helped define requirements.

Data foundation build-out to create ingestion pipelines, the Redshift data model, and the pre-processing layer for sentiment analysis and keyword extraction.

Production data onboarding and refinement to handle real-world complexity, including millions of rows, multiple sources, regional variations, and interactive query performance.

Technologies and frameworks

The solution relied on the following tools and platforms:

Agent development

Python, Strands Agents SDK, Amazon Bedrock AgentCore

AI and ML

Amazon Bedrock with Claude, Bedrock Knowledge Bases

Data storage

Amazon S3 data lake, Amazon Redshift Serverless

Data processing

AWS Glue ETL, Amazon Comprehend, Amazon Bedrock for sentiment analysis

Ingestion and orchestration

AWS Lambda, AWS Step Functions

Infrastructure and security

Infrastructure as Code, Amazon VPC, AWS IAM, AWS KMS

Results & Impact

technical outcomes

3 production AI agents delivered

SQUAD delivered three production AI agents from concept to deployment within three months. The first working demo was delivered within one sprint, which helped validate the architecture early and accelerate stakeholder alignment.

Unified data foundation across 13+ sources

More than 13 customer feedback data sources were brought into a single pipeline and made accessible through one interface. Every ingested record is enriched with sentiment scoring and keyword tagging.

business outcomes

Faster issue discovery and investigation

Time to insight was reduced from days or weeks of manual analysis to minutes, allowing the product team to identify product issues much earlier and act on them faster.

Scalable monitoring across launches

AI agents are now used for daily tracking of insights across more than 20 device launches, replacing fragmented manual review workflows with a repeatable operating model.

customer outcomes

More evidence-based product decisions

By linking customer feedback to device metrics, the team gained the ability to investigate issues using measurable evidence instead of anecdotal signals.