Messy lead sources
Partner lists, ERP records, and outreach context are scattered across disconnected sources.
FeaturedMVP / commercial workflow prototype
Balkan TV Lead& ERP Agent
An AI-assisted sales and ERP workflow prototype for OTT/IPTV providers targeting Balkan diaspora users in Turkey. The system connects partner lead sources, ERP/customer records, lead scoring, outreach draft preparation, and human approval into one reviewable workflow.
QUICK SUMMARY
What this project covers
Prioritized sales workflow
The system reviews customer data, scores lead opportunities, and prepares next-step outreach drafts.
Faster human-approved follow-up
Sales work becomes clearer, easier to prioritize, and safer to review before contact.
WORKFLOW
How the system works
Load ERP data
Bring customer records, partner leads, and follow-up context into one reviewable workflow.
Clean and segment
Identify useful lead information, customer state, and obvious data issues before scoring.
Score opportunities
Rank leads and customers by practical outreach priority instead of treating every record equally.
Recommendations
Prepare follow-up messages and sales next steps that a person can inspect.
Approve and follow up
Keep final outreach decisions human-approved before messages or actions leave the system.
CASE STUDY NOTES
Case study notes
This project started from a practical sales problem: an OTT/IPTV provider may receive leads from partner lists, social pages, reseller activity, existing ERP records, and informal conversations — but those signals are usually scattered, duplicated, or hard to prioritize.
Instead of building a full autopilot, I designed the system as a human-approved workflow. The AI helps prepare the work: it reviews lead/customer context, identifies useful signals, scores opportunities, suggests next-step outreach, and keeps the final decision visible to a person before any contact happens.
The goal of the prototype is not to replace sales judgment. The goal is to reduce manual sorting, make follow-up more consistent, and create a clearer review queue for people who already understand the market.
Check full case study reportSCREENSHOTS
Screenshots / product preview
SYSTEM DETAILS
Tools, concepts, and architecture
Core workflow
Lead scoringERP/customer contextReview queues
AI layer
Structured outputsEvaluator logicOutreach drafts
Safety
Human approvalLocal prototype
These concepts keep the prototype focused on reviewable sales work instead of uncontrolled automation.
The workflow is designed as a sequence of small AI-assisted steps rather than one large autonomous agent.
- Collect lead and ERP/customer context.
- Normalize useful fields and detect missing or duplicate information.
- Score opportunities based on practical sales signals.
- Generate suggested outreach drafts.
- Send outputs to a human review queue before any action is taken.
DETAILED REPORT
Detailed case study report
For visitors who want to understand how the project was shaped, how I tested it, and how I would improve it.
Why I built it
I wanted to model a realistic sales workflow where lead information is scattered across partner sources, ERP/customer records, and informal outreach context. The project is built around the idea that AI is most useful when it helps organize and prepare decisions, not when it blindly replaces them.
Problem framing
The core problem is not only finding more leads. It is knowing which leads deserve attention, what context already exists, what follow-up should happen next, and how to avoid treating every record as equally important.
Workflow design
I shaped the workflow as a review queue: ingest the available context, clean and segment useful information, score opportunities, draft recommendations, and keep the final outreach decision under human approval.
AI role
The AI role is limited and practical: summarize context, identify signals, prepare next-step recommendations, and support consistency. The system is intentionally not positioned as a fully autonomous sales agent.
Testing approach
I tested the prototype around whether the workflow remains understandable: can a human inspect the reason behind a recommendation, review the prepared message, and decide what should happen next?
Future Layer
The next layer would be cleaner CRM/ERP import and export handling, stronger review history, better duplicate detection, and analytics around which approved actions lead to replies or conversions.
DESIGN TAKEAWAYS
What this proves
Messy data into action
The workflow turns demo CRM/ERP records and partner lead lists into priority scoring, risk segments, and a structured follow-up queue.
Reduced manual review surface
Instead of scanning every record equally, a human reviews sorted customer and lead priorities with visible reasons, outreach drafts, and approval checkpoints.
Demo-ready human-approved flow
The prototype uses synthetic data to show lead scoring, high-risk customer prioritization, CRM/ERP cleanup, and approved follow-up tasks without claiming live business results.
Estimated monthly impact
10-25 hrs/monthmanual CRM/ERP review time could be reduced
40-70%less manual lead triage in a demo workflow
EUR 500-EUR 3,000/monthpotential revenue protected from overdue and at-risk customers
Same-day queuepriority follow-ups prepared for sales/admin review
This project turns messy CRM/ERP and partner lead data into a prioritized follow-up queue.
It helps surface overdue, expiring, inactive, duplicate, and weak-quality records earlier.
Human review remains part of the workflow.
Projected portfolio estimates, not claimed production results.
NEXT STEP
Want to discuss a workflow like this?
Send me a message with your current sales, CRM/ERP, or operations workflow and I'll tell you where an AI-assisted system could realistically help.