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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.

+

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 team did its best to stump kapa.ai, and time and time again, it delivered helpful, accurate responses.”

Michael Ellis

Developer Lead @ Ionic

“Kapa.ai is much better at answering questions about Prisma than ChatGPT is”

Soren Schmidt

CEO @ Prisma

Trusted by hundreds of Enterprises and Startups to power production-ready Al assistants

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 ot 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.

"By constantly testing all permutations of the latest models and academic techniques, the kapa.ai team stays ahead in providing highly accurate answers, solidifying their position at the forefront of applied RAG."

Douwe Kiela

Kapa.ai Investor, Professor at Stanford University, Author of RAG Paper, Founder of Contextual 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.

Grounded Answers with Citations

Provides answers based only on your knowledge content, reducing hallucinations.

How do I create a table in ExampleDB?

Use the

CREATE TABLE

CREATE TABLE

CREATE TABLE

command. For Syntax

details see our

Table Creation Guide

Table Creation Guide

Table Creation Guide

How do I create a table in ExampleDB?

Use the

CREATE TABLE

command. For Syntax

details see our

Table Creation Guide

Optimized For Combining Knowledge Sources

Synthesizes information from multiple sources to provide comprehensive answers.

How do I fix "Memory allocation" error?

Increase

max_memory_allocation

max_memory_allocation

max_memory_allocation

(see

Documentation

Documentation

Documentation

or try the workaround

mentioned in this

Github Issue

Github Issue

Github Issue

How do I fix "Memory allocation" error?

Increase

max_memory_allocation

(see

Documentation

or try the workaround

mentioned in this

Github Issue

Stays On Topic

Answers questions only specific to your product, to be safely deployed to customers.

How do I get started with OtherDB?

I'm sorry, but as an AI assistant for ExampleDB, I'm tuned to answer questions about ExampleDB.

How do I get started with OtherDB?

I'm sorry, but as an AI assistant for ExampleDB, I'm tuned to answer questions about ExampleDB.

Knows When To Say “I Don’t Know”

Acknowledges limitations when information is unavailable and suggests relevant resources, and letting you know opportunitites to improve your docs.

Does ExampleDB support time-series data?

Hmm I don't know how to answer that question based on the content. But I recommend checking out the

Data Types overview

Data Types overview

Data Types overview

Does ExampleDB support time-series data?

Hmm I don't know how to answer that question based on the content. But I recommend checking out the

Data Types overview

Multi-Product and Multi-Version Support

Handles queries across different product versions and deployment options.

How do I get started with ExampleDB?

That depends. If you use the open-source

version follow the

Self-hosted Guide

Self-hosted Guide

Self-hosted Guide

and if

you use the cloud sign up for an account.

How do I get started with ExampleDB?

That depends. If you use the open-source

version follow the

Self-hosted Guide

and if

you use the cloud sign up for an account.

Query Understanding

Breaks down challenging and vague users queries into specific sub-questions to improve answer quality.

How do I use it?

What are steps

to set up?

What are steps

to set up?

What are steps

to set up?

Are there any

prerequisites?

Where can I

find tutorials?

Multilingual By Design

Processes content in one language and responds in the user's preferred language.

🇧🇷 Quantos idiomas são suportados?

Suportamos mais de 80 idiomas, incluindo inglês, espanhol e mandarim, conforme

menionado em nosso

Help Center

Help Center

Help Center

.

🇧🇷 Quantos idiomas são suportados?

Suportamos mais de 80 idiomas, incluindo inglês, espanhol e mandarim, conforme

menionado em nosso

Help Center

.

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Bad answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Bad answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Bad answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Bad answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

  • Good answer

    The processData() method has been deprecated.

    As outlined in the

    Documentation

    , you

    should now use the analyzeData() method:

    result = analyzeData(data)

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.

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.

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.

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 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 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 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.

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.

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.

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.

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.

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.

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

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