S
Stylitics Tech Stack
AI-powered outfit bundling and visual merchandising for retailers
Stylitics builds AI-driven outfit generation and recommendation systems for retail ecommerce. The stack—Java, PostgreSQL, Qdrant, Claude, Gemini, OpenAI—combined with active projects in vector search, RAG pipelines, and LLM performance monitoring reveals a company shifting from template-based styling toward real-time, generative product recommendations. Pain points around scaling LLM delivery and catalog enrichment map directly to the engineering-heavy hiring mix, signaling growth in backend infrastructure and AI operations.
Tech Stack 14 technologies
What Stylitics Is Building
◆Challenges
- High-volume api delivery
- Account renewal challenges
- Client churn risk
- Scaling ai content delivery
- Maintaining llm performance
- Transforming basic product data into rich structured content
- Enriching product catalogs at scale
- Scaling ai recommendation systems
- Scaling ai workflows
- Semantic search for product similarity
▲Active Projects
- Vector search for product similarity
- Llm performance monitoring system
- Scalable ai content delivery workflows
- Temporal workflows for ai operations
- Backend services integrating llm apis for catalog enrichment
- Rag pipelines using qdrant for semantic search
- Ai recommendation generation at scale
- Temporal workflow orchestration for ai operations
- Ai outfit recommendation system
- High-scale api delivery
Hiring Activity
10 roles · 6 in 30d
Department
Seniority
Company intelligence
