Credal aims to connect company data to LLMs ‘securely’
Credal.ai, a Y Combinator-backed startup that gives enterprises a way to connect their internal data to text-generating, cloud-hosted AI models, has raised $4.8 million in a seed round led by Spark Capital.
Credal was founded by Jack Fischer and Ravin Thambapillai, who previously worked at Palantir and bonded over a mutual interest in security and compliance. Ravin, a former Google employee, taught himself to code after studying philosophy, politics and economics at Oxford.
“We realized that, with our backgrounds in enterprise data security and AI from Palantir, we were in a unique position from which to build an AI data platform that enterprises could actually trust,” Fischer told TechCrunch in an email interview.
Fischer and Thambapillai initially set out to build what they describe as a “decision-making assistant” for enterprises that’d use large language models (LLMs) — models along the lines of ChatGPT — to read documents and give advice on strategic, C Suite-level decisions. But the project eventually morphed into something broader: a tool to connect data from internal data sources to outside LLMs.
As the platform exists today, Credal can be used to build general knowledge or domain-specific, AI-powered chatbots for a range of use cases. For example, a company could tap Credal to create a bot that answers security questions about software that the company licenses, drawing on the latest documentation.
Credal doesn’t serve LLMs itself. Rather, it sits between users submitting prompts (e.g. “What’s the latest version of this software?”) and an API from a third-party LLM provider like OpenAI or Anthropic, acting as a “co-pilot” that can be deployed in existing apps like Slack.
Credal attempts to automatically direct prompts to the “most appropriate” LLM if a company’s using more than one, based on factors like the sensitivity of the data being submitted, cost, company policy and a model’s technical capabilities. In some cases, it employs more than one LLM to accomplish a task — for instance, using Anthropic’s Claude and GPT-4 to structure company documents.
Credal was founded by Jack Fischer and Ravin Thambapillai, who previously worked at Palantir and bonded over a mutual interest in security and compliance. Ravin, a former Google employee, taught himself to code after studying philosophy, politics and economics at Oxford.
“We realized that, with our backgrounds in enterprise data security and AI from Palantir, we were in a unique position from which to build an AI data platform that enterprises could actually trust,” Fischer told TechCrunch in an email interview.
Fischer and Thambapillai initially set out to build what they describe as a “decision-making assistant” for enterprises that’d use large language models (LLMs) — models along the lines of ChatGPT — to read documents and give advice on strategic, C Suite-level decisions. But the project eventually morphed into something broader: a tool to connect data from internal data sources to outside LLMs.
As the platform exists today, Credal can be used to build general knowledge or domain-specific, AI-powered chatbots for a range of use cases. For example, a company could tap Credal to create a bot that answers security questions about software that the company licenses, drawing on the latest documentation.
Credal doesn’t serve LLMs itself. Rather, it sits between users submitting prompts (e.g. “What’s the latest version of this software?”) and an API from a third-party LLM provider like OpenAI or Anthropic, acting as a “co-pilot” that can be deployed in existing apps like Slack.
Credal attempts to automatically direct prompts to the “most appropriate” LLM if a company’s using more than one, based on factors like the sensitivity of the data being submitted, cost, company policy and a model’s technical capabilities. In some cases, it employs more than one LLM to accomplish a task — for instance, using Anthropic’s Claude and GPT-4 to structure company documents.
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