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What’s Your Data Quality Red Flag?
We just build the MVP of an AI Data Quality Auditor:
→ Upload CRM CSV → 5-point audit → 0-100 score
→ <85% accuracy = “AI Integration Blocked”
→ Auto-calculates estimated AI waste + generates BI PDF
But before we lock the scoring weights, we need your war stories.
🔥 DROP YOUR STORY:
What’s one data quality issue that tanked your AI/ML project?
Duplicate records skewing predictions?
Inconsistent staging/formatting?
Missing critical fields?
Outliers breaking models?
The network for creativity
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Where does your data go when you use AI tools? I checked my own meeting notetaker this morning. The answers were reassuring, and they took an hour to find, because they were spread across a privacy policy, a DPA and a help center.
Today's tarot card is The Fool: stepping off a cliff edge, eyes on the sky. That was me with the notetaker. Clicked accept, never looked down.
So I turned the method into a free guide: how to check any AI tool your team uses, with the settings and links for eight vendors and a printable worksheet.
https://contra.com/products/l3NYerft-where-does-your-data-go
If you're planning an AI project for a client, check before the first byte of real data goes in.
Taking an hour to find the answer across three documents makes the case for the guide. I'd put each vendor's retention period and deletion path on one page, so a team can check both before the first upload.
Sales teams already have the data. The problem is finding what actually matters.
We built an AI Sales Intelligence Agent that brings together CRM activity, emails, calls, meetings, Slack and WhatsApp into one intelligent layer.
It helps sales teams:
• Identify leads that need follow-up
• Detect opportunities that are going cold
• Understand common customer objections
• Summarize conversations and client history
• Highlight deals that need immediate attention
• Turn sales activity into actionable insights
Instead of spending hours going through CRM records and conversations, teams can simply ask:
"Which leads need follow-up today?"
"Which opportunities are at risk?"
"What objections are prospects raising?"
"What did this client ask for in our last 3 calls?"
The goal is simple: give sales teams the right insight at the right time.
Save hours of manual administrative work with AI automation.
I built this independent AI automation prototype to demonstrate how an administrative quality review process can be streamlined using n8n, OpenAI, Gmail, and Supabase.
Instead of manually receiving emails, checking attachments, reviewing documents for missing information, recording case details, deciding whether a follow-up is required, and writing follow-up emails, the workflow automates these repetitive steps.
The workflow:
Gmail Intake → Document Routing & Extraction → AI Quality Review → Case Record & Decision → Professional Follow-up Email
The AI analyzes the submitted information, identifies missing or inconsistent data, structures the results, and determines whether a follow-up is required. If necessary, it automatically generates a polished follow-up email for the management team.
The goal is simple: reduce hours of repetitive administrative work, speed up the review process, and allow teams to focus on work that actually requires human judgment.
Tech Stack: n8n • OpenAI • Gmail • Supabase • APIs • AI Workflow Automation
This is an independent portfolio prototype using fictional/sample data. It is not an official ISYS system, project, or client engagement, and is not affiliated with ISYS Solutions.
Watch full dem video here:
