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AI-Assisted Chat QA | From Review to Agent Coaching
Turning customer conversations into actionable service improvements.
THE BUSINESS PROBLEM
Manual chat reviews can limit quality coverage and leave managers with scores that don’t clearly explain what agents need to improve.
THE CONCEPT
An AI-assisted review workflow that evaluates conversations against a defined scorecard covering accuracy, empathy, ownership, and resolution completeness. It would highlight supporting conversation excerpts, flag uncertain assessments for human review, and group recurring issues into coaching themes.
BUSINESS QUESTIONS
• Are repeat contacts linked to unclear explanations or incomplete resolutions?
• Which issues require agent coaching—and which require better processes?
• Are coaching actions improving conversation quality over time?
HOW SUCCESS WOULD BE MEASURED
Review coverage, agreement with human reviewers, recurring quality issues, and repeat-contact rate.
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In TechFoundry IMS the agents do two jobs: read incoming purchase orders (messy PDFs, emails, the occasional photo) into structured records, and flag anomalies — a supplier's price jumping 30%, a quantity that doesn't match history, a duplicate PO with a different number.
The agent never changes stock levels. It writes a suggestion with its reasoning attached, and a person clicks approve. Same pattern as my review console, because it's the only pattern I've seen survive real operations.
If you run inventory or ops and you've been pitched "fully autonomous" — ask what happens on the day it's wrong. Case study: https://contra.com/p/eemeBeMW-tech-foundry-ims-ai-automated-inventory-and-order-operations
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🚀 My First AI Automation Project with n8n
I recently built my first automation project — an AI-Powered Invoice Extraction & Tracking System.
The problem I wanted to solve was simple:
Imagine a business receiving dozens of invoices or cash memos every week. Someone has to open each file, read the information, and manually enter the vendor name, date, items, total amount, and category into a spreadsheet.
That’s repetitive, time-consuming, and easy to get wrong.
So I built a workflow where:
📂 An invoice is uploaded to Google Drive
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→ ⚙️ n8n processes and validates the information
→ 📊 The data is automatically added to Google Sheets
The best part?
No manual data entry. Just upload the invoice and the rest happens automatically.
While building it, I also worked with things like workflow triggers, loop control, API errors/retries, and handling multiple files.
For me, this project was less about simply “building an n8n workflow” and more about learning how AI + automation + data workflows can solve real business problems.
This is just my first step into AI automation, and I’m excited to build more practical systems like this.
If your business has a repetitive process that could be automated with AI, n8n, or data workflows, feel free to DM me. I’d be happy to discuss it.
#AIAutomation #n8n #Automation #ArtificialIntelligence #DataAnalytics #DataAnalyst #WorkflowAutomation #GenerativeAI #BusinessAutomation #AI
Smart pipeline! Have you added strict JSON schema validation after Gemini extracts the invoice fields? Found that it stops edge case hallucinations before the data ever hits Sheets.
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