How Fyxer built an AI executive assistant people trust

For many professionals, work means keeping track of conversations and commitments across inboxes, meetings, messages, and apps. Without rock-solid context, those commitments can fall through the cracks, damaging projects and relationships.

Fyxer built an AI executive assistant that follows the thread as work moves across tools. It combines the latest OpenAI models with more than 500,000 hours of executive assistant workflows, dividing the work among dozens of specialized models that improve through real user feedback.

Email is one of the clearest places to see it in action. Two people can receive the same email and need completely different replies depending on the relationship, what has happened before, and what each person is trying to get done. That makes a seemingly simple task deceptively hard for AI.

Fyxer built its system around 30–50 specialized models, each responsible for a narrow part of the email workflow. Instead of treating email as a single text-generation task, Fyxer breaks the problem into a system of predictions, like deciding whether a message requires a reply or drafting responses that match a user’s tone and context.

Frontier OpenAI models support key parts of the experience, from understanding the email and finding the right context to generating the draft. Fyxer chose OpenAI because its models performed best on Fyxer’s internal benchmarks, offered strong fine-tuning capabilities for subjective tasks like tone and intent, and provided hands-on engineering support through whiteboarding sessions and technical collaboration.

“There’s something called Moravec’s paradox,” explains Fyxer Co-founder Archie Hollingsworth. “Things that humans find easy are hard for computers, and things that computers find easy are hard for humans.” Fyxer handles that complexity by learning how each user works, so it can respond like an assistant who already knows what matters.

Fyxer’s approach offers three lessons for founders building highly contextual AI products:

“Breaking the problem into many smaller models works much better than asking one model to write a good email,” Hollingsworth explains.