Python Demo for Validating Customer-Export JSON Before Import

Python Demo for Validating Customer-Export JSON Before ImportPython Demo for Validating Customer-Export JSON Before Import

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

Back to feedPost

Andrei Bogomolov

• 13h

JSON export validation before import

AI-assisted, self-initiated Python demonstration on synthetic data. Validates a small customer-export schema: nonempty string IDs, booleans and real YYYY-MM-DD dates. Reports duplicate IDs and malformed values by record and field without modifying the source.

Verified example: five records, six issues across three records. IDs 0010 and 10 remain different; 2024-02-29 passes, 2026-02-30 is flagged.

Included in a three-tool demo suite with six automated tests passing. Source, test data and result JSON available on request. This is a fixed demo schema, not a production deployment or a past client project.

Data Analysis Python

Jaydutt Dave

• Oct 4

Where do you think AI agents can make the biggest impact in a real business? 🤖

I'm exploring AI agents and automation through projects with n8n, Python, RAG and APIs.

I'm curious to hear from other builders and founders:

What would you automate first if you could remove one repetitive task from your business?

Lead management? Customer support? Internal operations? Something else?

Would love to hear your ideas 👇

#AIAgents #AIAutomation #n8n #AI #Automation

Johnson Afolabi

• Oct 5

I saw how linking n8n to a CRM can pull new leads straight into a queue, wiping out the copy paste step.

Shamroz Ali

• Oct 4

What if every new lead could be handled before you even open your inbox? ⚡

This AI-powered workflow turns a new email inquiry into a structured, ready-to-handle lead — automatically.

A new inquiry arrives → AI extracts the important details → Airtable/CRM is updated → the response goes through optional human approval → a personalized email is sent → Notion is updated → the team gets notified in Slack → performance data is collected for reporting.

The goal isn't to remove people from the process. It's to remove the repetitive work around them.

This type of workflow can be customized for B2B companies, SaaS businesses, agencies, real estate, e-commerce, recruitment, consulting, customer support, healthcare, finance, education, and other service businesses.

Already know what you want to automate? → Send me your current workflow and I’ll map out how we can automate it.

Still doing repetitive work manually? → Tell me the task that consumes your team’s time, and I’ll help identify what can be automated.

#AIAutomation #AIWorkflow #AIWorkflows #AIAgents #Automation #WorkflowAutomation #BusinessAutomation #ProcessAutomation #n8n #n8nAutomation #ArtificialIntelligence #GenerativeAI #B2B #B2BAutomation #SaaS #SaaSAutomation #SalesAutomation #LeadGeneration #LeadAutomation #LeadManagement #EmailAutomation #CRMAutomation #CustomerSupport #CustomerServiceAutomation #MarketingAutomation #AgencyAutomation #RealEstateAutomation #EcommerceAutomation #RecruitmentAutomation #HRautomation #FinTech #HealthcareAutomation #EdTech #BusinessGrowth #BusinessEfficiency #DigitalTransformation #NoCode #LowCode #OpenAI #AIAgent #CRM #Airtable #Notion #Slack #Productivity #FutureOfWork

AI Automation AI Agent Engineer Business Workflow Automation Gmail N8N OpenAI

Kapil Chauhan

• Oct 4

I'd use the human approval step to capture corrections, not just a yes/no decision. If a reviewer changes the extracted lead details, does the workflow update Airtable and the reporting record before sending the email?

Atique Ahmed

• Oct 4

I built an AI agent that handles customer operations — refunds, order lookups, and support tickets — with a human approval gate built into the workflow. The agent proposes the action, pauses, and waits for a human to approve before anything executes. The LLM never makes the final call. Role-based access and a full audit trail are enforced in code, not prompts. Deployed live on Azure Container Apps.

Completed work