Use Cases
Unlock the Power of
RAG
Unlock the Power of
RAG
Generate fact based responses with peace of mind.
Supports your RAG use case
Supports your RAG use case
RAG is a general architecture that can be applied in many ways to get your data into an LLM.
Question Answering
Synthesizing answers requires multiple sources of information and opens up domain specific areas.
Question Answering
Synthesizing answers requires multiple sources of information and opens up domain specific areas.
Question Answering
Synthesizing answers requires multiple sources of information and opens up domain specific areas.
Chatbot / Co-Pilot
Enabling conversational interaction with your domain specific knowledge, including nuances of dialog context and history.
Chatbot / Co-Pilot
Enabling conversational interaction with your domain specific knowledge, including nuances of dialog context and history.
Chatbot / Co-Pilot
Enabling conversational interaction with your domain specific knowledge, including nuances of dialog context and history.
Enterprise Search
Better results with semantic retrieval and ability to answer complex queries that correlate several data sources.
Enterprise Search
Better results with semantic retrieval and ability to answer complex queries that correlate several data sources.
Enterprise Search
Better results with semantic retrieval and ability to answer complex queries that correlate several data sources.
Agents
Agents performing tasks do so based on external environment knowledge. RAG helps you deciding how to handle request.
Agents
Agents performing tasks do so based on external environment knowledge. RAG helps you deciding how to handle request.
Agents
Agents performing tasks do so based on external environment knowledge. RAG helps you deciding how to handle request.
RAG Fine Tuning
RAG Fine Tuning
Create synthetic scenarios
Fine tune results by creating synthetic scenarios.
Create synthetic scenarios
Fine tune results by creating synthetic scenarios.
Create synthetic scenarios
Fine tune results by creating synthetic scenarios.
Visualize metrics
Visualize metrics on model score cards and evaluations.
Visualize metrics
Visualize metrics on model score cards and evaluations.
Visualize metrics
Visualize metrics on model score cards and evaluations.
Perform failure extraction
Perform failure scenario extraction to improve your fine-tuning set.
Perform failure extraction
Perform failure scenario extraction to improve your fine-tuning set.
Perform failure extraction
Perform failure scenario extraction to improve your fine-tuning set.
Evaluate LLMs with Confidence
Evaluate LLMs with Confidence
Okareo enables developers to confidently ship applications that use AI/LLMs through synthetic test scenario generation, driving intelligent evaluation, fine-tuning, error reporting, and health monitoring.
Recent Blogs
Optimizing Your RAG - Practical Guide for Software Engineers
RAG isn't just about "slapping a Vector DB on an LLM"; building a real app involves many technical decisions.
Optimizing Your RAG - Choose an Embedding Model That Fits Your Data
Explore embedding models based on the type of data retrieval you are building your RAG around.
Intro to RAG Fine Tuning
Learn more about task and chat based LLMs and how to evaluate behavior and performance.
Recent Blogs
Optimizing Your RAG - Practical Guide for Software Engineers
RAG isn't just about "slapping a Vector DB on an LLM"; building a real app involves many technical decisions.
Optimizing Your RAG - Choose an Embedding Model That Fits Your Data
Explore embedding models based on the type of data retrieval you are building your RAG around.
Intro to RAG Fine Tuning
Learn more about task and chat based LLMs and how to evaluate behavior and performance.
Recent Blogs
Optimizing Your RAG - Practical Guide for Software Engineers
RAG isn't just about "slapping a Vector DB on an LLM"; building a real app involves many technical decisions.
Optimizing Your RAG - Choose an Embedding Model That Fits Your Data
Explore embedding models based on the type of data retrieval you are building your RAG around.
Intro to RAG Fine Tuning
Learn more about task and chat based LLMs and how to evaluate behavior and performance.
