You will get I'll Build You a Multi-Source AI Feedback Routing & Escalation System
Rising Talent
Rising Talent
Project details
Most "AI feedback tools" just slap a sentiment label on text and call it done. I build systems that check their own work. Every classification gets reviewed by a second, independent AI pass before anything happens with it, so a miscategorized complaint gets caught instead of silently routed wrong.
This isn't theoretical. While building this exact system, my validator caught a real mistake the first agent made, it flagged a mislabeled category and explained why. I also hit and fixed two real bugs during development: a workflow that hung when only one feedback source was active, and a tagging error that mislabeled every entry's origin. I document what breaks and how I fixed it, not just the finished result.
Works well for customer support tickets, product reviews, app store feedback, or post-purchase survey responses, anywhere you need to separate urgent issues from routine noise without reading everything by hand.
You get a working system that separates urgent issues from routine ones automatically, keeps a full audit trail, and flags recurring complaint patterns before they become a bigger problem.
This isn't theoretical. While building this exact system, my validator caught a real mistake the first agent made, it flagged a mislabeled category and explained why. I also hit and fixed two real bugs during development: a workflow that hung when only one feedback source was active, and a tagging error that mislabeled every entry's origin. I document what breaks and how I fixed it, not just the finished result.
Works well for customer support tickets, product reviews, app store feedback, or post-purchase survey responses, anywhere you need to separate urgent issues from routine noise without reading everything by hand.
You get a working system that separates urgent issues from routine ones automatically, keeps a full audit trail, and flags recurring complaint patterns before they become a bigger problem.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Sentiment AnalysisAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
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WO
Will O.
Jun 12, 2026
Prompt Editor for AI-Generated Planning Documents
About Eric
AI Engineer | AI Workflow Systems Builder
Princeton, United States - 8:51 pm local time
Every system I build includes honest documentation of what broke and how I fixed it, because that's the standard a business actually needs before trusting AI with real decisions.
Workflows I build:
Confidence-based routing pipelines:
→ Example: strong matches move forward, weak ones get rejected, borderline cases go to a person.
Classification and escalation routing:
→ Example: incoming email extracted, acknowledged, and flagged if urgent or overdue.
Document and data extraction pipelines:
→ Example: claims data pulled automatically. Low-confidence results go to review.
Invoice and document processing:
→ Example: invoices extracted on a schedule into a live spreadsheet.
Onboarding pipelines with duplicate detection and audit logging:
→ Example: new client intake that checks for existing records first.
Lead scoring and outreach with eval layers:
→ Example: emails checked for company name, length, and CTA before anything sends.
Multi-agent orchestration:
→ Example: one resume scored by three agents at once, then merged into one result.
Risk and fraud triage pipelines:
→ Example: claims flagged for human review. The system never decides fraud on its own.
RAG chatbots and knowledge-base Q&A:
→ Example: policy Q&A with source citations.
API integrations with error handling and dedupe:
→ Example: Monday integration integration that catches failed writes hidden behind HTTP 200.
MCP servers exposing internal tools to AI clients:
→ Example: fraud scoring callable from Claude in plain English.
CI/CD pipelines for workflow deployment:
→ Example: GitHub Actions validates the workflow, then deploys it without creating duplicates.
AI observability and monitoring dashboards:
→ Example: tracks token cost, confidence drift, and error spikes.
Steps for completing your project
After purchasing the project, send requirements so Eric can start the project.
Delivery time starts when Eric receives requirements from you.
Eric works on your project following the steps below.
Revisions may occur after the delivery date.
Map feedback sources and rules
Define your feedback sources and urgency criteria.
Build the workflow
Build the classification, validation, and routing logic in n8n.