Crossing Minds

Crossing Minds is an enterprise AI operations and personalization platform designed to build, deploy, and scale AI-powered applications using a composable stack. It provides end-to-end capabilities for real-time information retrieval, personalization, data enrichment, and model fine-tuning across diverse industries. The platform emphasizes low-latency, scalable infrastructure, rapid integration with existing systems, and cost-efficient deployment of large language models (LLMs) and RAG-based workflows.

What it does

• Offers a unified platform for advanced embeddings, large-scale model training, and RAG (Retrieval-Augmented Generation) workflows to generate precise and context-aware outputs.
• Provides high-performance infrastructure for real-time retrieval across massive datasets with sub-200ms latency at scale.
• Enables real-time knowledge updates and continuous learning to keep AI applications aligned with the latest data.
• Delivers a composable stack with APIs for integrating proprietary algorithms, AI-enhanced catalog, and a managed data pipeline.
• Supports fine-tuning across LLMs, with an emphasis on retrieval-augmented approaches rather than frequent full retraining to reduce costs.
• Targets personalization at scale across multiple use cases, including recommendations, conversational search, and omnichannel experiences.

How it works

1. Ingest data and transform it into AI-ready datasets using the Data Layer.
2. Build embeddings, configure RAG systems, and fine-tune models in the ML Layer.
3. Deploy real-time retrieval and serving in the Infra Layer with sub-200ms latency.
4. Use the Applications layer to implement personalization, enrichment, and conversational experiences.
5. Continuously enrich data and knowledge to keep AI outputs current.

Use cases and industries

• Personalization Engines (ecommerce, streaming, marketplaces)
• Financial institutions and B2B applications
• Email, SMS, and omnichannel personalization
• Data enrichment, conversational search, and recommendations

Core Features

• Real-time retrieval and scalable serving at terabyte scale
• Advanced embeddings and RAG-Sys for context-aware outputs
• Large model training and LLM fine-tuning
• Data processing and enrichment to produce AI-ready datasets
• Composable stack with API access for customization
• AI-powered personalization across ecommerce, streaming, and B2B apps
• Proactive knowledge updates for real-time learning
• Integration-friendly platform tailored for enterprise deployments
• Trustworthy, enterprise-grade security and governance capabilities