Cloud• 2025

OmniInsight: AI Customer Intelligence Engine

Built a scalable vector search & LLM processing pipeline summarizing customer sentiment across millions of support tickets.

120 hrs

Time Saved / Week

15x

Analysis Speedup

5M+

Tickets Processed

OmniInsight: AI Customer Intelligence Engine

Project Overview

OmniCloud wanted to extract actionable product feedback from massive volumes of unorganized customer service transcripts. We architected an AI pipeline using vector embeddings and large language models (LLMs) to automatically categorize, rank, and summarize customer friction points.

The Challenge

Processing thousands of raw incoming tickets per minute without exceeding API rate limits or creating high compute infrastructure costs.

Our Solution

We built a async Python FastAPI service coupled with Qdrant vector database for semantic search. Incoming text is embedded using OpenAI text-embedding models, clustered into topic vectors, and summarized into executive dashboards using fine-tuned Llama models.

Key Features & Capabilities

Semantic topic clustering automatically discovering emerging bug reports

Executive natural-language query interface ('What are users complaining about in v2.4?')

Automated daily digest generated & posted directly to Slack channels

Role-based data masking ensuring customer PII is scrubbed before LLM embedding

Engineering & Architecture Highlights

Asynchronous worker pool processing vector embeddings via AWS SQS queue
Cost-optimized hybrid LLM router directing simple tasks to smaller models
Qdrant vector cluster with HNSW indexing for sub-10ms similarity queries

The Impact & Results

Saved 120+ hours per week of manual product feedback analysis, reduced issue identification turnaround from weeks to minutes, and uncovered critical feature requests.

"OmniInsight gave our product team superpowers. We catch critical user feedback instantly instead of discovering issues weeks later."

David Sterling

VP of Product, OmniCloud