Credal aims to connect company data to LLMs ‘securely’


Data prep picture with data coming in from many different sources and coming out the other side as charts and applications.
Image Credits: miakievy / Getty Images, 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.

Plenty of platforms offer ways to connect company data to LLMs — see Unstructured, Deasie and LlamaIndex. And OpenAI’s expanding its built-in plug-in framework. But Credal’s unique spin on this is a strong emphasis on compliance and security — at least the way Fischer tells it.

Credal attempts to automatically redact, anonymize and otherwise warn when sensitive data is about to be sent off-network, say to an LLM hosted on a public cloud. And it provides logs that show what data’s been shared with which LLMs.

Data sent to Credal is retained by default and kept for 30 days after accounts expire — which might give some companies pause. But admins can change this and choose to wipe data at any time, Fischer emphasizes.

Fischer also claims that Credal is one of the few vendors of its kind to be registered under the Data Privacy Framework, the recent U.S.-EU agreement that governs the transfer of personal data between the two countries. That’s enabled it to win contracts with publicly traded, regulated European enterprises like Wise, Fischer says.

“IT departments at enterprises want visibility, and control, over how AI is being used within their organization,” he added. “Credal gives them [this transparency] in a standard format across multiple LLM providers, providing fine-grained data controls over who can access which models, what data can be used by each user and for what purposes … Unlike other ‘AI on your data’ systems, Credal automatically mirrors the permissions of the source systems it connects to, so when a user asks a question, the AI responds from only company documents that are both relevant and accessible to that user.”

Since launching in April, Fischer claims that Credal has handled over a quarter of a million LLM calls and ingested around 100,000 corporate documents. The company has 11 customers currently, several of which have signed “six-figure” contracts, according to Fischer.

With the capital from the seed round, Credal plans to expand its headcount (which stands at five employees at present) and “expand the product to cover more data sources and perform more sophisticated data retrieval,” Fischer says.

“The AI industry at the moment is suffering a relative imbalance between the huge enthusiasm and the as-yet still relatively small number of companies using LLMs to create real-world value,” Fischer said. “Credal is solving that by embedding deeply with a small number of amazing enterprises and actually solving their problems end-to-end.”

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