Accuracy is everything.
We optimize kapa for one thing: providing the most accurate answers about your product. That system is what we call the Answer Engine.
+k
AI answers generated per month by kapa.ai.
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Companies trust kapa.ai in production.
“Kapa.ai is the only LLM-based system I trust to put in front of our customers”
Rusty Wilson
VP Support @ Sonatype
Our philosophy: Evaluation-Driven Development
Academic benchmarks and leaderboards only take you so far.
You need custom evaluations to align AI models. And at kapa.ai, our mission is to help companies to deploy reliable AI assistants to answer technical product questions.
So we have developed specialized evaluation frameworks for answering technical product questions that go beyond generic metrics, incorporating real-world customer feedback.
This approach allows us to continuously refine our system and deploy latest research and models, only when it improves accuracy for all users of kapa.ai.
How the Answer Engine works
The Answer Engine is our end-to-end system optimized for answering technical product questions. It’s model-agnostic, meaning it changes over time as new techniques and models come out, and is designed for production use-cases.
Provides answers based only on your knowledge content, reducing hallucinations.
Synthesizes information from multiple sources to provide comprehensive answers.
Answers questions only specific to your product, to be safely deployed to customers.
Acknowledges limitations when information is unavailable and suggests relevant resources, and letting you know opportunities to improve your docs.
Handles queries across different product versions and deployment options.
Breaks down challenging and vague users queries into specific sub-questions to improve answer quality.
How do I use it?
Are there any
prerequisites?
Where can I
find tutorials?
Processes content in one language and responds in the user's preferred language.
Frequently asked questions
What LLM do you use?
kapa.ai is model agnostic, meaning we're not tied to any single language model or provider. Our mission is to stay at the forefront of applied RAG, so you don't have to. We constantly evaluate and incorporate the latest academic research, models, and techniques to optimize our system for one primary goal: providing the most accurate and reliable answers to technical questions.
To achieve this, we work with multiple model providers, including but not limited to OpenAI, Anthropic, Cohere, and Voyage. We also run our own models when necessary. This flexible approach allows us to select the best-performing model for each specific use case and continuously improve our service as the field of AI rapidly evolves. To ensure data privacy and security we have DPAs and training opt-outs with all providers we work with.
How accurate is kapa?
Kapa's accuracy is very high, assuming your content is of good quality. That’s of course easy to say but hard to prove. So the best way to understand how kapa performs is to try it on your own content by requesting a demo here. Note that one of Kapa's strengths is its ability to help you identify gaps in your content, allowing you to continuously improve your documentation and, consequently, the accuracy of kapa. We provide analytics and insights to help you understand where your content can be enhanced for better accuracy
How do you solve hallucinations?
We address hallucinations through a combination of grounded answers and rigorous evaluations. Our system is designed to provide answers based solely on your documentation, which significantly reduces the risk of hallucinations. In nearly all cases, incorrect or incomplete answers are due to issues with existing content or missing information. See more here. Additionally, our evaluation frameworks continuously test the system's outputs against our test set, allowing us to identify and correct any tendencies towards hallucination.
Do you use fine-tuning or RAG?
At Kapa, we're model- and technique-agnostic, meaning we use whatever methods perform best for each specific use case. That said, we are strong proponents of Retrieval-Augmented Generation (RAG), as it offers practical way to ensure explainability and grounding answers in your content. We work closely with leading academics in this field, including Douwe Kiela, one of our investors and an author of the original RAG paper. This collaboration keeps us at the forefront of RAG research and implementation.
Turn your knowledge base into a production-ready AI assistant
Request a demo to try kapa.ai on your data sources today