
Adaptive Engine
Evaluate, tune, and serve the best LLMs for your business with reinforcement learning optimization.
Adaptive Engine provides a comprehensive platform designed to evaluate, fine-tune, and deploy large language models (LLMs) tailored for enterprise needs. It leverages reinforcement learning to optimize model performance based on measurable business metrics, ensuring that enterprises can achieve best-in-class accuracy and efficiency in their AI implementations. The platform is ideal for businesses looking to enhance their AI capabilities by generating synthetic data and employing AI judges for performance prediction and validation.
Adaptive Engine is particularly useful for companies aiming to streamline their AI deployment process, offering capabilities such as enterprise search (RAG), text-to-SQL translation for business intelligence, and personalized customer support agents. With Adaptive Engine, enterprises can boost analytics performance and enhance customer satisfaction, all while reducing risks associated with AI deployment in production environments.
Pros:
- Optimizes LLMs for business-specific metrics
- Reduces risk with A/B testing before production
- Enables high accuracy in enterprise search and retrieval
- Supports specialization with reinforcement fine-tuning
Cons:
- May require significant computational resources
- Requires expertise in AI model integration
- Limited to businesses with measurable performance metrics
Features:
- Reinforcement Fine-Tuning - Use reinforcement learning to optimize model performance based on specific business metrics.
- AI Judges - Evaluate model performance and predict production outcomes with specialized AI judges.
- Enterprise RAG - Retrieve enterprise knowledge with high accuracy, eliminating misinformation.
- Text-to-SQL Translation - Enable natural language interface to databases, streamlining business analytics.
Integrations:
- Not Disclosed - Description
Pricing:
- Not Disclosed - Description
Implementation Timeline
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Onboarding
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Sandbox
No
Compliance
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