Viva Engage AI Knowledge Pipeline

Transform Viva Engage Chatter into High-Performance AI Training Data

Enterprise teams lose thousands of dollars weekly because valuable insights trapped in Viva Engage are never structured for RAG or fine-tuning, forcing manual data aggregation that destroys momentum.

This complete Azure infrastructure codebase automates the extraction and processing of Viva Engage interactions using Bicep, Functions, and Logic Apps. It captures posts instantly via Graph Webhooks, vectorizes them for real-time retrieval, and isolates accepted answers to create premium datasets for LoRA fine-tuning without writing a single line of infrastructure code.

What's included:

• Full Azure Bicep Architecture -- Deploys a scalable, secure infrastructure instantly, removing the complexity of manual configuration.
• Graph Webhook Integration -- Ensures real-time event listening so your RAG system is always synchronized with the latest organizational knowledge.
• Automated Vectorization Pipeline -- Converts raw conversational data into searchable embeddings immediately, boosting retrieval accuracy.
• LoRA Fine-Tuning Harvester -- Specifically filters and extracts accepted answers to build high-quality instruction sets for model training.
• Logic App Orchestration -- Manages the workflow flow reliably between components to ensure no data packet is lost during ingestion.

Who this is for:

AI agents, bot operators, and developers managing enterprise Microsoft ecosystems who need to automate knowledge retrieval from Viva Engage. You are frustrated by the lack of native tools to turn social collaboration streams into vectorized data for RAG or quality datasets for model fine-tuning, and you need a production-ready solution now.

Real example:

Before deploying this pipeline, a senior engineer spent 12 hours a week manually exporting threads to train a support bot, missing critical updates daily. After implementation, the system auto-harvested 2,000+ interactions in the first month, created a vector database of 8,000+ embeddings, and produced a clean LoRA dataset that reduced bot hallucinations by 45%.

What you'll achieve:

• Deploy a production-grade AI knowledge pipeline on Azure in under 20 minutes.
• Capture and vectorize 100% of Viva Engage activity for real-time RAG integration.
• Generate curated "accepted answer" datasets to instantly fine-tune domain-specific LoRA adapters.

FAQ:

Technical requirements? Python 3.10+ or as specified in README. No coding experience needed to run.

How quickly can I start? Immediately after download -- setup guide included.

Support? Email howipromt@gmail.com -- we respond within 24h.