Monitor feedback data from Google Sheets, generate prompts, analyze per row, visualize results with QuickChart, and deliver a branded HTML report by email.
The AI agent analyzes unstructured feedback from Google Sheets to extract semantic insights. It runs per-row analyses using a set of prompts and organizes results into structured fields. It generates QuickChart visuals and compiles a final HTML report for email delivery.
Concrete actions the agent performs to derive insights.
Ingests feedback data from Google Sheets
Generates structured prompts for analysis
Pairs each row with all prompts and runs analysis
Merges, deduplicates, and semantically clusters results
Generates QuickChart visuals and compiles an HTML report
Emails the final HTML report to recipients
Before: Sifting through unstructured feedback manually is time-consuming, prompts vary between analysts causing inconsistent insights, and row-by-row analysis is slow. After: The AI agent provides automated, consistent row-level insights, prompts are reused for consistency, analysis is faster, and a branded HTML report with charts is delivered automatically.
A simple 3-step process anyone can follow.
Imports feedback rows from Google Sheets and prepares the first 20 rows for analysis.
AI creates 3–6 prompts and pairs each row with all prompts to form the analysis dataset.
LLM analyzes the paired data, merges results, generates QuickChart visuals, and assembles a branded HTML report for email delivery.
A realistic scenario demonstrating end-to-end use.
Scenario: A product team maintains a Google Sheet with 20 customer feedback items. The AI agent imports the data, proposes 4 prompts, and runs per-row analyses. It merges results, generates 4 charts (themes, sentiment, top requests, channel breakdown), builds a branded HTML report, and emails it to stakeholders within about 25–30 minutes.
Roles that gain from automated feedback analysis.
Identify top issues and feature requests from feedback efficiently.
Track sentiment drivers and understand drivers of customer perception.
Surface recurring pain points to reduce churn and improve onboarding.
Map feedback to user journeys and guide design decisions.
Automate text analysis and reporting workflows to scale insights.
Receive concise narratives and charts for decision-making.
Tools connected to the AI agent workflow.
Imports feedback data from Sheets and feeds the analysis pipeline.
Runs prompts, executes row-level analyses, and parses results.
Generates chart URLs from processed metrics for visualization.
Delivers the final HTML report via email to recipients.
Concrete scenarios where the AI agent shines.
Practical answers to common questions.
Currently, the workflow ingests data from Google Sheets, structured for per-row analysis. The prompts are designed to be product-agnostic, so you can reuse the same prompts across different sheets without modification. If you need to switch sources, you can adapt the integration layer to pull data from additional systems. The end-to-end pipeline remains the same: ingestion, per-row analysis, merging, visualization, and reporting.
Data security is important. The agent runs within your configured credentials and uses standard OAuth2 and API keys managed in your environment. Google Sheets access is restricted to the sheets you authorize, and Gmail access is tied to the sender account you specify. All prompts and results are handled within the workflow's execution context and are not published externally unless you explicitly share the final HTML report. For teams with strict governance, you can enable audit logging and ephemeral storage policies.
Yes. The prompts are designed to be adaptable; you can modify the set of prompts, add new ones for additional dimensions, or optimize existing prompts for your domain. The system supports prompt mutations and iterative refinement, so you can improve the quality of per-row insights over time. Changes apply to future analyses without disrupting historical results. This keeps the analysis aligned with evolving product priorities.
Custom charts and report layout are supported by adjusting QuickChart configurations and the HTML template. You can select different chart types, colors, and data series, and tailor the HTML structure to reflect your brand. The agent outputs chart URLs that are embedded in the report, so you can replace or extend visuals as needed. If you require advanced visuals, you can add new chart types or data mappings in the integration layer.
After analysis, the agent compiles an HTML report and sends it through Gmail to the specified recipients. The email includes embedded charts and a readable narrative of the insights. You can customize the recipient list and subject line for automation workflows. If you need to deliver to multiple destinations, you can extend the flow to upload to Drive, Notion, or Slack as well.
Recurring runs and event-based triggers are supported by the underlying automation platform. You can schedule executions daily or weekly, or trigger the workflow when a Google Sheet is updated. The AI agent handles incremental data and ensures idempotent results for repeated runs. For governance, you can limit triggers to specific times and set notification preferences.
Pricing and rate limits depend on the LLM and cloud resources you use to run the agent. The workflow is designed to be scalable with batch processing for large data sets. If you exhaust quotas, you can adjust batch sizes or extend compute time. For teams, you can create dedicated instances with preset budgets and alerting. This makes it practical to scale usage without surprises.
Monitor feedback data from Google Sheets, generate prompts, analyze per row, visualize results with QuickChart, and deliver a branded HTML report by email.
