Fyxer built an AI executive assistant that people actually trust—53% of its AI-generated email drafts are accepted as written. That number matters because email is deceptively hard: the same message can need completely different replies depending on the relationship, history, and goal. Fyxer’s approach shows what it takes to make an AI system feel like an assistant who already knows what matters.
Why one big model isn’t enough
Instead of treating email as a single text-generation task, Fyxer breaks it into 30–50 specialized models, each handling a narrow job. A reply-decision model classifies whether a message needs a response, a scheduling action, or just visibility. Intent and outcome models predict where the conversation is heading. Retrieval models pull relevant memories from past interactions.
“Breaking the problem into many smaller models works much better than asking one model to write a good email,” explains Fyxer co-founder Archie Hollingsworth. This mirrors a pattern we’ve explored before: execution models decide how much your orchestrator can be trusted. When each step is a focused prediction, you can evaluate and improve it independently.
Training data from real assistant work
Fyxer didn’t start with a blank slate. Before launching the AI product, the company ran a human-powered executive assistant service for years. That generated a dataset of more than 500,000 hours of annotated workflows—real examples of how professional assistants manage communication.
Those examples capture the small judgments behind a good response: when to answer quickly, when to wait, which earlier conversation matters. Fyxer uses supervised fine-tuning and Low-Rank Adaptation (LoRA) to create task-specific model variants while controlling training cost. OpenAI’s fine-tuning platform and managed fine-tuning team helped transfer that customer-specific learning into production models.
Turning user edits into a self-training loop
When someone edits a draft before sending it, the difference between the original and final email shows which output they preferred. Fyxer converts those comparisons into training data using Direct Preference Optimization (DPO). The model learns from pairs of outputs: the original draft and the user-edited version.
Every drafting change then goes through an A/B test. Fyxer ships the new version only when it produces a statistically significant improvement. With enough user volume, the team can sometimes reach that threshold within a day.
What the metrics actually tell you
Fyxer grew from $1 million to $32 million in annual recurring revenue in 2025. But Hollingsworth points to a different signal: “Over 90% of our users are still paying at the 90-day mark with us, and still using us every day.” Retention matters more than ARR because it shows the assistant is genuinely useful, not just sold.
The 53% draft acceptance rate is a strong baseline, but it also means nearly half of drafts still get edited. That’s not a failure—it’s the feedback loop working. Each edit becomes training data for the next iteration.
Fyxer’s vision is to get customers to a place where they never have to open their computer and can trust the assistant to manage all of it. That’s a long way off, but the path is clear: break the problem into specialized models, train on real workflows, and let user behavior drive continuous improvement.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.