unitQ processes customer feedback—from support tickets, analytics, social media, and surveys—through AI agents to surface product issues and user needs in real-time. The stack is LangChain + RAG + Python + SQL on AWS/GCP, with Kubernetes orchestration and Spring Boot microservices, signaling a mature, distributed system built to handle high-volume unstructured data ingestion. Active projects span feedback analytics, RAG pipelines, and data processing layers, while hiring remains minimal and senior-weighted—typical of a post-product-market-fit company focused on deepening existing capabilities rather than rapid scaling.
Tech Stack 20 technologies
Core StackRAG Python Docker AWS LangChain Java Kubernetes Hibernate Zendesk Jira Salesforce SQL GCP LangGraph Spring Boot Git Data Studio
AdoptingRAG
What unitQ Is Building
◆Challenges
- Extracting value from unstructured data
- Improving product quality at scale
- Data ingestion and processing
- Customer account activity monitoring
▲Active Projects
- Ai-powered feedback analytics
- Large-scale user feedback processing
- Retrieval-augmented generation (rag) pipelines
- Ai-driven features and services
- Microservice architecture
- Scalable data pipelines
- Robust data ingestion, cleaning, and processing systems
- Client taxonomy management across journey
Hiring Activity
Minimal5 roles · 0 in 30d
Department
Seniority
Company intelligence
