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The internal AI assistant your
GTM team already trusts

Instantly get accurate internal answers, built specifically for GTM teams.

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Trusted by leading teams

Your SDRs, AEs, CSMs and Support teams have to answer technical questions every day

That's why modern GTM teams choose kapa.ai

Battle-tested accuracy

The same trusted answer engine powering 150+ external deployments now leverages your internal docs, Slack, etc.

Purpose-built for complex products

Designed specifically for the needs of SDRs, AEs, SEs, CSMs, and Support engineers at technical companies.

Combine internal & external knowledge

Consistent, single-source-of-truth answers, whether external-facing or internal.

Accessible everywhere (even on calls)

Desktop app built for rapid access during live demos or customer meetings.

Grounded citations

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Grounded citations

….

Connect +40 LLM-optimized internal data sources

Automatically refreshing and secure data connectors that support all common data sources. See all supported integrations.

Secure by design and PII aware

Trusted by 100+ enterprises and ready for internal use-cases using sensitive data. Explore security features incl. PII data masking, encryption and RBAC.

SOC 2 Type II Certified

Certified stringent data security verified by third party auditors.

PII Data Anonymization

Automatically remove PII from data sources and questions.

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.

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.

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.

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