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
Synthetic CSV QA demonstration - not paid client work. This 10-record example preserves the original and identifiers, checks agreed rules and logs one approved title-whitespace change. Input and output both contain 10 records. The candidate is ready for human QA, not guaranteed import-ready data. URL checks cover syntax only. For a scoped review, send headers and a few invented or redacted rows first. Rules, record cap, timing and quote are agreed before accepting work. No live-store access or confidential files in messages.
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
Related posts
Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
AI Agent for ServiceNow Automation (LangGraph + GPT-4o-mini + Slack, Jira, Email)
I built a LangGraph agent that turns natural-language requests into ServiceNow incidents, so users never have to open the ServiceNow UI. A StateGraph workflow pairs an LLM node (GPT-4o-mini) that understands the request with a tool node that runs the ServiceNow Table REST API. It supports caller, description, and assignment group fields, and it resolves group names to sys_ids by looking them up in the sys_user_group table, so users can just say “Network Team”. The agent is exposed through a Flask REST endpoint and an interactive CLI, with credentials kept in environment variables and error handling around every API call. I then extended the same pattern to Slack, Email, and Jira to automate workflows across platforms.
Understand: The LLM node interprets the request and extracts the caller, description, and assignment group.
Act: The tool node executes ServiceNow actions through the Table REST API.
Resolve: Group names are looked up in the sys_user_group table to get the sys_id, so users refer to teams by name.
Expose: A Flask REST endpoint and an interactive CLI give two ways to use the agent.
Secure and robust: Credentials are managed through environment variables, and API calls have error handling.
Extend: The same agent pattern was reused for Slack, Email, and Jira integrations.
Tools to tag
Python, LangGraph, OpenAI GPT-4o-mini, Flask, ServiceNow REST API, Slack API, Jira, Agentic AI, Automation
Your next industrial order could be sitting inside an unanswered PDF.
A buyer sends a parts list. Your sales team starts searching catalogs, checking stock, and copying prices into a quotation.
Meanwhile, another supplier responds.
For industrial spare-parts distributors, quoting gets difficult when:
→ Requests arrive across emails, PDFs, and spreadsheets.
→ Buyers use incomplete descriptions or inconsistent part numbers.
→ Stock and customer pricing live in separate systems.
→ Substitute products need technical confirmation.
→ Follow-ups depend on someone remembering.
I can build an AI-assisted RFQ workspace that brings these steps together.
It would extract requested items, suggest catalog matches, retrieve inventory and pricing, and prepare a quotation for your team to review.
Missing specifications? Flag them.
Uncertain matches? Request clarification.
Alternative products? Require approval.
Your team keeps control over technical decisions and the final quote.
The workflow:
Receive request → Extract line items → Match catalog → Check stock and pricing → Review exceptions → Approve and send.
The goal is less repetitive data entry, clearer follow-ups, and faster preparation of accurate quotations.
Already know your requirements?
Send me your RFQ process and integration requirements, and let’s scope the build.
Need help identifying the right setup?
Share an anonymized request and your current quoting steps, and I’ll help map the first useful automation.
Concept project shown with fictional sample data.
industrial RFQ automation • spare parts quotation software • industrial distributor software • AI quotation assistant • B2B quote automation • request for quotation software • industrial parts catalog search • manufacturer part number matching • spare parts inventory integration • distributor sales automation • PDF RFQ extraction • email to quotation workflow • industrial procurement software • bearing distributor software • motor parts quotation system • belt distributor automation • industrial sales dashboard • customer specific pricing software • ERP quotation integration • inventory API integration • automated quote preparation • RFQ management portal • industrial order processing • AI document extraction • quotation approval workflow • technical sales automation • catalog matching software • supplier quotation management • industrial wholesale software • custom distributor CRM • Python automation developer • FastAPI development services • React dashboard development • B2B software developer • custom RFQ software development • hire quotation automation developer • industrial workflow automation • quotation follow up automation • sales operations software • parts availability checker • industrial SKU matching • product substitution approval • quotation audit trail • PDF to structured data • spreadsheet RFQ processing • distributor digital transformation • industrial customer portal • quote turnaround improvement • human reviewed AI automation • custom procurement integrations
#IndustrialDistribution #SpareParts #IndustrialSupply #RFQAutomation #QuotationSoftware #QuoteManagement #B2BSales #SalesAutomation #DistributorSoftware #IndustrialProcurement #PartsManagement #CatalogManagement #InventoryIntegration #ERPIntegration #DocumentAutomation #PDFExtraction #AIForBusiness #AIAutomation #HumanInTheLoop #WorkflowAutomation #PythonDevelopment #FastAPI #ReactDevelopment #CustomSoftware #APIDevelopment #B2BSoftware #SalesOperations #DigitalTransformation #ProcurementAutomation #IndustrialTechnology
