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Scale GenAI Platform enables AI teams to build, evaluate, and control agentic solutions that reason over enterprise data, take action with tools, and continuously improve through human-agent interactions. The platform provides enterprise-grade hosting and orchestration framework for deploying, managing, and scaling reliable AI agents with zero operations overhead.
The platform offers advanced Retrieval Augmented Generation (RAG) tools that allow LLMs to accurately reference knowledge bases. Teams can convert knowledge base data into embeddings for long-term memory retrieval, using comprehensive toolsets including data connectors, custom embedding models, vector stores, chunk summarization, metadata extraction, advanced reranking, and RAG fine-tuning capabilities.
Agent Operations provides the telemetry and data management layer for monitoring, evaluating, and improving agents using employee behavioral data. This includes semantic layer monitoring, agent evaluation tools, and a data flywheel that turns user interactions into learning signals, enabling teams to manage, trace, and continuously improve agent performance.
The platform supports building AI applications with any model on any cloud infrastructure. Teams can connect popular data sources, implement optimized RAG pipelines, and deploy models fine-tuned for domain-specific tasks. Scale GenAI Platform securely customizes and deploys enterprise-grade applications in private VPCs including AWS, Azure, and GCP, supporting all major closed and open-source foundation models from OpenAI, Google, Meta, and others.
- Automate complex customer service interactions with AI agents providing omnichannel support
- Streamline legal document review processes with advanced reasoning agents
- Accelerate due diligence for equity investments, mergers and acquisitions, and venture capital investment
- Build and run agents using custom tools and frameworks without vendor lock-in
- Deploy long-running asynchronous agents for complex enterprise workflows
- Monitor and trace agent performance with semantic layer telemetry
- Convert knowledge base data into embeddings for optimized retrieval
- Implement advanced RAG pipelines with custom embedding models and reranking
- Scale agent fleets from one to thousands across enterprise teams
- Fine-tune models for domain-specific tasks using enterprise data
- Deploy generative AI applications securely in private cloud environments
- Continuously improve agent performance using human-agent interaction data
