What is Reva?

Reva is a platform designed to assist businesses in optimizing their AI investments by enabling outcome-driven testing and comparison of Large Language Models (LLMs). It allows users to test various AI configurations and model changes, comparing potential outcomes based on historical or synthetic data. This approach helps businesses select the most suitable LLM for their specific tasks, ensuring that AI advancements translate into tangible returns.

The tool focuses on evaluating how different models perform in relation to a business's unique use case and success criteria, moving beyond abstract benchmarks. Reva supports future-proofing AI implementations by allowing testing against evolving models and prompts. It also offers features for continuous monitoring of deployed AI systems and aids in training, including Retrieval-Augmented Generation (RAGs) and custom fine-tuned models using generated synthetic data.

Features

Precision Model Selection: Matches the right AI model (e.g., OpenAI, Anthropic) to specific business needs, ensuring optimal performance and ROI.
Test Changes: Utilizes your own historic data or synthetic data to test model and configuration changes.
Custom Fine-Tuning & Synthetic Data: Tailors AI models to unique contexts using custom fine-tuning and generated synthetic data, dramatically improving accuracy and effectiveness.
Continuous Optimisation: Stays ahead with proactive monitoring and updates, adapting to new AI advancements as they emerge.
Data-Driven Monitoring: Employs innovative approaches to test AI product's performance and ensure every change benefits users and customers.
Model Evaluation & Comparison: Understands and compares how your task and your expected outcomes work with any models, ensuring any changes work.

Use Cases

Selecting the optimal LLM for specific business applications.
Evaluating the impact of AI model or configuration changes before deployment.
Future-proofing AI systems by testing against evolving models and prompts.
Monitoring the performance of deployed AI products for guardrails.
Training custom AI models using fine-tuning and synthetic data (RAGs or custom fine-tuned models).
De-risking AI product shipment with rigorous testing and deployment strategies.

FAQs

How does Reva help in creating LLM products that work?

Reva assists by enabling future-proofing and backtesting of prompts against evolving models, offering system monitoring for deployed guardrails, and supporting training through RAGs and custom fine-tuned models using synthetic data it generates.
What is meant by outcome-driven AI testing with Reva?

Outcome-driven AI testing means Reva helps your business use the latest AI advancements to achieve the best outcomes for your specific tasks, focusing on real returns and your success criteria rather than abstract benchmarks.
Can Reva help me choose between different AI models like OpenAI or Anthropic?

Yes, Reva offers Precision Model Selection to match the right AI model, such as those from OpenAI or Anthropic, to your specific business needs, ensuring optimal performance and ROI.
How does Reva support testing AI configuration changes?

Reva allows you to use your own historic data or its synthetic data to test model and configuration changes, enabling you to compare possible outcomes from the past or the future.