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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
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I provide clean, accurate and professional Data Entry, Data Cleaning, Data Formatting and Data Analysis services for businesses and individuals.
📩 DM me if you need reliable and efficient data services! Let’s make your data organized and easy to manage. 💻📊
An AI risk metric/score of 87 tells an approver almost nothing.
It says the model is worried. It doesn't say why, how fresh the number is, or whether the data behind it was complete. If the pipeline failed 17 times this month, the 87 looks exactly the same.
That's a design problem, not a model problem. The fix is mostly disclosure: say where the number came from, show the signals behind it, put partial runs on the decision screen, and let the approver re-run it before signing off on $248,600.
The person clicking Approve owns the decision. The interface owes them the evidence.
Chris, this is spot on. On a payments agent we built, approvers only started trusting the flags once each one showed the reason behind it and when it last ran. A bare number just made people click Approve faster, which is the opposite of what you want.
Your support team goes home at 6. Your customers don't. An online retailer we worked with had a simple problem.
Shoppers asked questions at 11pm, on weekends, on holidays.
The answers came the next morning, and by then a lot of them had bought somewhere else.
We built a 24/7 AI chat into their store. Not a FAQ bot that says "I didn't understand that" three times.
It handles the common questions on its own, recommends products inside the conversation, and drops every chat into their CRM with the full customer context.
When a person does step in, they aren't starting from zero. → 50% more conversions → 35% faster response time → 40% less support workload The part people underestimate is the CRM link.
A chat that sells but forgets who the customer is just moves the problem to your team. Support and sales turned out to be the same conversation.
Question for ecommerce founders. What share of your support questions come in outside working hours? Have you ever checked? It's usually the first number I ask for.