Jev, Firecrawl & the $1B rev club
Shai Goldman <[email protected]>
Brex hosts three AI-related events this week and highlights Jev, a new model by Typesafe AI, as a solution to save money on token spending. Jev complements existing LLMs, such as OpenAI and Anthropic, by acting as a security guardrail to verify proposed system actions. Brex showcases Jev's capabilities through an example of an automated security guardrail for an AI customer support agent.
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Jev, Firecrawl & the $1B rev club
Button Shai Goldman
Happy Tuesday, Brex Community!
There are three AI related events that Brex is hosting this week, see bottom of the email for specific details.
On a AI related note. The current trend is trying to save money on token spending and the most wide discussed solution the past two weeks is Jev, which is a new model released by Typesafe AI on Sept 15. For those who are unfamiliar, Jev is not competing directly against existing LLMs such as OpenAI and Anthropic, it is complementary to them. Here is an example:
A common enterprise use case for Jev is acting as an automated security guardrail for an AI customer support agent. In this setup, the LLM handles the conversation, while Jev strictly verifies the LLM's proposed system actions before they are allowed to execute. Here is how the interaction flows when a customer asks a support chatbot for a refund:
The User Request: A customer tells the chatbot, "I meant to pause my subscription, but I got charged today. Please refund my payment."
The LLM (The Communicator): The LLM reads the message, drafts a polite reply, and generates a system command to trigger the company's billing software: issue_refund(amount: $15.00).
Jev (The Inspector): Before that refund actually processes, the application code intercepts the command. It sends the customer's chat history and the LLM's proposed action to Jev, asking a strict yes/no question: "Is this tool call safe, within policy, and explicitly requested by the customer?".
The Decision: In about 250 milliseconds, Jev bypasses text generation entirely and outputs a single structured data point: a 99% probability that the action is correct.
The Execution: Because Jev's confidence score exceeds the company's predefined 95% safety threshold, the software automatically executes the refund.
If Jev had returned a low confidence score, perhaps because the LLM hallucinated a policy or the user was trying to trick the system, the code would automatically block the LLM's action and route the ticket to a human employee. By pairing the two models, a company gets the fluid conversational abilities of an LLM, but relies on Jev's highly calibrated, low-latency decision-making to prevent the LLM from making unchecked changes to user data, while also saving money on tokens.
On another AI note, two massive new announcements released with 24 hours of each other, both Cognition and Higgsfield has crossed the $1B annualized revenue rate market, things are moving very quickly, the opportunities are massive. Interestingly , both of them are not even 3 years old startups 👀
Have a great week.
Company. Firecrawl is the web data API for AI agents, helping startups build products that can search, scrape and interact with the live web. Teams use it to power everything from customer research to AI apps that need current information.
The problem. Getting useful data into an AI product often means building and maintaining connections to messy websites and scattered sources. Even a capable AI agent can miss the information it needs because ordinary web search only reaches part of what's available.
The solution. Firecrawl finds and turns web content into clean data so teams can spend more time building their product. Its new library, Alexandria, goes further by connecting AI agents directly to official data providers and specialized indexes for deeper research.
Why now. AI agents can build software and carry out increasingly complex research, making the knowledge they can reach more valuable. They are building a library that grows with those capabilities and gives people and organizations a reason to contribute what they know and get paid.
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