Structured Data Capture via a Chat Agent
Turn natural conversations into organized, trackable data — no forms, no manual entry, no context switching. A custom chat agent that captures structured information and writes it directly to a SharePoint Excel workbook, with built-in duplicate detection to surface high-priority items.
Introduction
Across every team — Sales, Product, Operations, Customer Success — rich, qualitative information surfaces in conversations every day. A customer describes a pain point on a call, a teammate flags a recurring issue in a standup, a stakeholder articulates a requirement during discovery. This is some of the most actionable information a team can have — but it's also some of the hardest to capture in a structured, usable way at scale.
The challenge is that documenting it in a unified format places an outsized burden on the reporter: switching to a spreadsheet, choosing the right fields, manually entering data hours or days later — if it happens at all. And even when people do log it, the same issue gets expressed differently by different people — "dashboard is slow," "report rendering takes too long," "performance issues on the analytics page" — with no way to recognize these as the same problem or aggregate them into a clear signal.
The result: critical information quietly disappears. Recurring problems get reported in isolation but never connected. Teams operate on incomplete pictures, leading to longer learning cycles, repeated issues, and silent failures that only surface when it's too late.
The 'Quick' Solution
The Structured Data Capture Chat Agent is a custom chat agent built in Amazon Quick that eliminates this friction. Instead of filling out forms or navigating spreadsheets, users simply describe what they need to log in natural language — the agent infers structured fields like category, type, priority, and impact from the conversation, confirms the data, and writes it directly to a SharePoint Excel workbook. Optional duplicate detection identifies when multiple users report the same issue and aggregates their submissions, giving teams a clear signal on what matters most.
This pattern works across any team and any use case where unstructured conversations need to become organized, actionable data.
Who This Guide Is For
- Sales teams logging customer call details and use case information
- Product managers collecting internal feedback and feature requests
- Operations teams documenting meeting action items and stakeholder requirements
- Customer Success teams capturing support interactions and renewal risks
- Any team that needs to turn unstructured conversations into organized, trackable data
What You'll Build
A custom chat agent in Amazon Quick that:
- Conducts natural conversations and infers structured fields from user input — no form-like responses required
- Writes captured data to a pre-configured SharePoint Excel workbook automatically
- Detects duplicate submissions using keyword similarity matching (>70% threshold) and offers to aggregate them
- Increments influence counts on duplicate entries to surface high-priority items
- Asks clarifying questions when input is vague, and splits multiple distinct issues into separate entries
- Confirms all captured data with the user before writing to SharePoint
- Provides a Reference ID after each successful submission
Prerequisites
- Amazon Quick Access — installed and accessible from your environment
- Professional user license (minimum) — required for custom chat agents and action connectors
- SharePoint action connector — configured by your Amazon Quick administrator (verify under Integrations)
- SharePoint team site — must be a team site (not a personal site) so that multiple users on your team can access and collaborate on the shared workbook
- Edit permissions on the SharePoint team site where your Excel workbook lives
Solution Details
The Structured Data Capture Chat Agent leverages Amazon Quick's custom agent capability to bridge the gap between unstructured conversations and structured data.
At its core, the agent listens to natural language input — a description of a customer issue, a feature request, a meeting action item — and infers the structured fields needed to log it: category, type, description, priority, impact, and additional context. It then presents the structured data for user confirmation before writing it to the next available row in a SharePoint Excel workbook.
The agent also includes optional duplicate detection: before writing a new entry, it searches existing rows for entries with the same category, same type, and >70% keyword similarity in the description. If a match is found, the user can choose to add their support to the existing entry — incrementing the influence count — or submit it as a new row. This ensures that popular issues naturally rise to the top, giving teams a clear, quantified signal on what matters most without requiring manual deduplication.
Implementation
Step 1: Set Up Your SharePoint Excel Workbook
- Navigate to your SharePoint team site (not a personal or communication site — a team site ensures all agent users can access the shared workbook)
- Create a new Excel workbook (e.g.,
DataCaptureTracker.xlsx) or upload one to a document library - In the first sheet, create a header row with the following recommended columns:
| Column | Type | Notes |
|---|---|---|
| Title | Text | Brief summary of the entry (required) |
| Category | Text | Area or topic (e.g., Dashboard, Reporting, API) |
| Type | Text | e.g., Bug Report, Feature Request, Enhancement |
| Description | Text | Detailed description of the entry |
| Priority | Text | Critical, High, Medium, Low |
| Impact | Text | Business impact description |
| Additional Context | Text | Examples, customer names, event IDs |
| User Influences | Text | Comma-separated list of users who submitted similar entries |
| Influence Count | Number | Count of duplicate submissions |
| Status | Text | New, In Progress, Resolved |
Save the workbook and ensure all users who will interact with the agent have Edit permissions on the file
Retrieve your Site ID and the file path for the workbook. You can ask the default Amazon Quick chat agent using a prompt like:
You: "I need the SharePoint Site ID and drive file path for my team site at [your SharePoint URL]. Can you look that up for me?"
The agent will use the SharePoint connector to retrieve the Site ID and file path, which you'll need for the agent instructions in Step 3.
Step 2: Create the Chat Agent
- Navigate to Chat Agents from the left navigation panel in Amazon Quick
- Click Create Agent
- Create the chat agent using the following prompt:****
You are [Agent Name] helping [target users] capture structured [data type] and write it to a SharePoint list. Your role is to: (1) Collect structured information through natural conversation, inferring as much as possible from user input before asking questions; (2) Detect duplicate submissions using >70% keyword similarity and offer to aggregate; (3) Write to SharePoint excel URL [URL] at List Name [List Name] and Site ID [Site ID]" by updating the next available row. Always confirm captured data with the user before writing.
Alternatively, hit 'skip' and configure the agent details directly:
Enter the Agent Instructions
- Name:
[Your Agent Name](e.g., "Feedback Capture Agent" or "Use Case Tracker") - Description: A chat agent that captures structured data from conversations and writes it to a SharePoint Excel workbook.
Paste the following prompt into the agent instructions field. Replace the bracketed placeholders with your specific values:
Agent Persona:
Agent Identity:
You are the [Agent Name] helping [target users] capture structured [data type] about [topic].
SharePoint Configuration:
Site ID: [Your Site ID]. Workbook path: [Your File Path]. Sheet name: [Your Sheet Name].
Collection Process:
When a user submits [data type], infer as much as possible before asking.
(1) Greet warmly and explain the agent's purpose.
(2) Ask which [category/component] this relates to — provide specific options.
(3) Ask what type of [feedback/request/issue] this is — provide specific types.
(4) Get a detailed description with type-specific prompts.
(5) Assess priority (Critical, High, Medium, Low).
(6) Understand impact.
(7) Gather additional context.
Duplicate Detection:
Before writing a new row, search existing entries for Same [Category] + Same [Type] + >70% keyword similarity in Description. If found, display the match and ask: "Is this the same [entry] you're submitting? Add your support to it, or submit as new?" If confirmed duplicate: update existing entry, add user to User Influences, increment Influence Count. If new: write to the next empty row, set Status to "New", provide Reference ID.
Inference Rules:
Infer [category], [type], priority (Critical/High for "blocks," "broken," "urgent"; Medium/Low for "nice to have," "could improve"), and impact from user language. Split multiple distinct issues into separate entries. Ask clarifying questions if input is vague. Use the user's alias for the user column.
Tone:
Friendly and conversational. Ask one question at a time. Always confirm before writing. Provide clear confirmation with Reference ID and next steps.
Customization:
Welcome message:
Hi, I can help you capture structured data from conversations and writes it to your SharePoint Excel.
Suggested prompts
Log product feedback from raw notes
Step 4: Connect SharePoint Actions
- In the agent configuration, navigate to Actions
- Click Add Actions and select the SharePoint connector — this gives the agent the ability to read from and write to your Excel workbook
Step 5: Test the Agent
Click Preview to open a test conversation
Try a sample interaction like:
You: "Hey, I just wrapped up a customer call. They're frustrated because every time they try to export a report to PDF, it times out after about two minutes. They said it's been happening for the last week and it's impacting their ability to share reports with their leadership team.
"Agent: "Thanks for sharing that feedback. I appreciate you reporting this right after your customer call. I checked the existing feedback entries and didn't find any duplicate matching a PDF export timeout issue..."
Submit the data capture after review:
Step 6: Publish and Share the Agent
- Click Publish to make the agent available
- Use the Share settings to grant access to your team
- Team members can access the agent from the Agents section in Amazon Quick
How Duplicate Detection Works
Duplicate detection is one of the most powerful features of this pattern. It prevents redundant entries, reduces noise, and gives teams a quantified signal on which issues matter most — all without requiring manual deduplication.
Here's how it works:
- Before writing a new row, the agent reads existing entries from the workbook
- It compares the new submission against existing entries looking for matches on:
- Same Category (e.g., both are "Reporting")
- Same Type (e.g., both are "Bug Report")
- >70% keyword similarity in the Description field
- If a match is found, the agent displays the existing entry and asks: "This looks similar to an existing entry — is this the same issue? You can add your support to it, or submit as new."
- If the user confirms it's a duplicate:
- The agent updates the existing row's User Influences field by appending the current user's alias
- The Influence Count is incremented by 1
- No new row is created
- If the user says it's new, the agent writes a new row as normal
Why this matters: A single entry with an Influence Count of 12 is a much stronger signal than 12 separate rows saying the same thing. Teams can sort by Influence Count to instantly see what's being reported most frequently — turning a flat spreadsheet into a prioritized backlog.
Sample Use Cases
This guide covers the generic pattern for structured data capture. You can customize the agent for specific use cases by adjusting the categories, types, and prompts in the agent instructions:
- Customer Call Logging — Capture use case details, customer pain points, and next steps after every call
- Product Feedback Collection — Log feature requests, bug reports, and enhancement ideas from internal teams
- Meeting Action Items — Document decisions, owners, and deadlines from recurring meetings
Tip: Each of these use cases can be set up as its own agent with a dedicated workbook, or you can use a single agent with multiple sheets in one workbook — one per use case.
Key Constraints
- The action confirmation window requires the user to click Submit — the agent cannot bypass this step
- Authentication may require 2–3 attempts — this is normal; wait 30 seconds between retries
- For duplicate detection to work effectively, the agent needs to read the full sheet each time — performance may slow with very large workbooks (1,000+ rows). Consider archiving older entries periodically
Troubleshooting
- "Invalid Request" error: Verify the Site ID, file path, and sheet name are correct in the agent instructions
- No action window appears: Confirm the SharePoint connector is connected and the agent has all required information
- Authentication fails: Retry 2–3 times, wait 30 seconds between attempts, clear browser cache if needed
- Data written to wrong sheet: Double-check the sheet name in the agent instructions matches the actual sheet name in your workbook
Conclusion
The Structured Data Capture Chat Agent transforms how teams capture and organize information. Instead of switching between conversations and spreadsheets, users log data naturally — and the agent handles the structure, validation, and storage automatically.
By writing directly to a SharePoint Excel workbook, teams get a familiar, accessible data store that's easy to filter, sort, and share. The duplicate detection feature ensures high-priority items surface naturally through influence counts, turning a flat spreadsheet into a prioritized, signal-rich backlog.
Customize the agent for your team's specific use case, connect it to your SharePoint workbook, and start capturing structured data from day one.
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article