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An enterprise agent builder that connects natural language instructions to company tools and data warehouses.
around 15 enterprise customers · around 10 team members
The agent should prove its usefulness on the buyer’s own workflow before the sales call ends.
Fewer bars = easier, cheaper, or faster for an AI-assisted solo builder. Editorial judgments based on the case details.
Catera is an AI agent builder aimed at companies that want agents to use their existing infrastructure and tools. Evie described the company as serving growth-stage businesses, especially Series B through D, and said it had around 15 enterprise customers, a team of around 10, and low-end seven-figure ARR. Its original product was a customer analytics platform that reached roughly $150K–$170K ARR but demanded substantial consulting and struggled to create recurring product usage. The founders eventually pivoted toward agents after a customer request led them to build a more useful workflow. In the new product, a user describes a job, grants access to tools, and specifies when it should run. The product’s attraction in sales is visible: the team can configure a rough agent during a call and let a prospect see the workflow operate. Its reported enterprise traction is meaningful, though the number and revenue are founder statements rather than audited figures.
Catera’s earlier customer discovery started with targeted LinkedIn messages asking prospective users for interviews, not pitching a finished platform. The founders drew on YC affiliation and prior work credentials, kept the note to two sentences, and reported high-single-digit response rates, roughly 8–10%.
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Data credibility: ARR, customer count, and team size are reported by the founder and were not independently audited. Pricing details are described in the interview; no financial records were reviewed.
The first analytics revenue arrived when one customer committed to paying a few hundred dollars a month for help understanding customer retention and personas. The founders then built a handful of features on top of a Jupyter notebook over several days.
Catera’s strongest early growth signal came after the pivot: prospects could see an agent perform a task during a sales conversation, and the founders said deals became easier to close. The product also turns its own capabilities into outbound proof.
The founders had to tolerate a long period when analytics customers were paying but the software was not becoming a durable product. They described roughly 18 months of consulting-heavy work, weak stickiness, and unclear pricing.
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