Redefining the future of data and AI solutions. The strategic collaboration between Karini’s GenAI Foundation and Databricks Mosaic empowers businesses to integrate generative AI capabilities seamlessly —supporting the delivery of a GenAI portfolio strategy. Affording organizations a tailored journey to their unique goals—defending current market positions, extending existing processes, and upending industries with groundbreaking innovations. Together, Databricks and Karini AI are evolving the future of AI.
Karini AI Generative AI Foundation powered byDatabricks Mosaic AI Platform
Accelerate Time to Market
Enable beginners and seasoned professionals to rapidly prototype and deploy generative AI applications. With the ability to move from concept to production 20x faster, enterprises can innovate swiftly without needing deep expertise in GenAI or Databricks.
Eliminate Technical Debt
Leverage standardized, optimized Gen AI production ready blueprints, The approach reduces reliance on specialized talent and custom code, minimizing technical debt. The platform's efficiency ensures enterprises can focus on innovation rather than being bogged down by technical issues.
Future-Proof Your GenAI Investments
Karini AI is a GenAI foundation on Databricks that stays at the forefront of Gen AI advancements, allowing seamless migration to cutting-edge models and techniques.
Cloud Agnostic Flexibility
Integration with Databricks Model Serving ensures optimized deployment of the latest open-source models while offering secure access to third-party model hubs like Azure OpenAI, Amazon Bedrock, and Google Vertex AI.
Enterprise-Grade Features
The combined platform offers robust enterprise features, including Guardrails for safety, semantic caching for cost efficiency, prompt management and tuning, and comprehensive evaluation metrics and dashboards.
Enterprise challenges during experimenting and deployingGenerative AI (GenAI)
Optimizing Compound AI systems for Quality
Scaling GenAI applications from proof of concept to full-scale production involves managing multiple AI models and pipelines, each tailored for specific tasks. Enterprises must optimize for performance, cost, and latency, which becomes increasingly challenging as they integrate smaller, open-source models with larger systems. Without careful planning, these complexities can lead to delays and increased operational overhead, hampering the potential for widespread adoption.
Ensuring Data Quality and AI Governance
Poor data quality and insufficient AI risk controls are leading causes of GenAI project abandonment. AI models rely heavily on high-quality context data to generate accurate and reliable outputs, so any deficiencies in data quality can result in subpar performance. Furthermore, the absence of robust AI risk management frameworks can expose enterprises to security vulnerabilities and compliance risks, making it challenging to gain stakeholders' buy-in to move forward with production deployments.
Managing Costs and Demonstrating Business Value
As enterprises progress from GenAI experiments to large-scale deployments, costs can escalate rapidly, mainly when dealing with sophisticated compound AI systems that require significant computational resources. Additionally, demonstrating clear and tangible business value becomes crucial to justify continued investment. With a well-defined cost management and value measurement strategy, projects can be scaled back or abandoned altogether.
Build Compound AI System withDatabricks and Karini
Set Up a Knowledgebase
Create and Test Agentic Prompts
Deploy Agentic Chatbot
Review Prompt History
Utilize Karini AI’s no-code recipes to create a data ingestion pipeline, preprocess data into LLM-ready formats, and store it in Databricks vector store. This ensures a scalable, cloud-agnostic GenAI platform ready for AI application development.
Set Up a Knowledgebase
Create and Test Agentic Prompts
Deploy Agentic Chatbot
Review Prompt History
Utilize Karini AI’s no-code recipes to create a data ingestion pipeline, preprocess data into LLM-ready formats, and store it in Databricks vector store. This ensures a scalable, cloud-agnostic GenAI platform ready for AI application development.
Karini AI: Building Better AI, Faster.
Orchestrating GenAI Apps for Enterprises GenAiOps at scale.