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About Plait

Built by one engineer, his own data, and an AI cofounder.

Plait turns the recovery, sleep and strain your wearable already measures into what to eat today. 6,800+ athletes have connected a WHOOP, Garmin or Apple Watch beta so far.

6,800+
athletes connected a wearable
3
devices: WHOOP, Garmin, Apple Watch (beta)
45+
research-cited articles and guides
1+AI
human founder, AI cofounder

The team

Plait is a team of one, plus the AI he builds with. Adam sets the product direction and reviews releases; automated checks validate the nutrition arithmetic in generated plans.

Adam Eisenman, founder of Plait

Adam Eisenman

Founder & CEO

B.S. Electrical Engineering & Computer Science, Georgia Tech · S.M. EECS, MIT

Adam founded Plait after years of having rich WHOOP data and no clear answer to the only question that mattered each morning: what should I do differently today? He has built AI automation and data systems across satellite communications, logistics and crypto, and brings that engineering discipline to fitness data.

  • AI & automation: machine learning, analytics pipelines, business intelligence systems
  • Engineering: software, signal processing, predictive modeling
  • Operating experience: logistics businesses, technology investing, strategy
  • Fitness data: WHOOP, Garmin and Apple Health metrics, recovery and nutrition timing

"I obsess over details in both fitness and technology. I built Plait so other data-driven athletes get the insights they deserve. Your wearable captures incredible data. Plait helps you actually use it to get better."

The AI cofounder

Analysis, drafting and pattern search

GPT, Claude and purpose-built models · always on

Adam builds and maintains Plait. AI proposes meals from a defined food catalog; the server calculates nutrition from the weighed ingredients and checks the daily totals. Personal plans are generated automatically and are not individually reviewed by a person.

  • Data analysis: recovery, sleep and strain patterns across the user base, statistical modeling, trend detection
  • Personalization: daily calorie and macro targets that adapt to each morning's recovery
  • Research: literature review with PubMed and NCBI citations in every article
  • Content: first drafts of guides and posts, fact-checked and edited by Adam

"With AI as a cofounder I can analyze like a team of statisticians and write like a team of researchers, while keeping the personal judgment only a human founder can bring."

The origin story

The photo that started it

"Approaching 40, I saw a photo of myself and had a moment of truth: fix it or forget it. Plait didn't exist yet, but the same data-driven approach I used to change my own body is what it now does for thousands of others." Adam Eisenman
Adam at 39, the family photo that started it

Before · age 39

The family photo. Out of shape, low energy, tired of generic advice.

After

Adam at 41, after 18 months of data-driven training and nutrition

Age 41

Lean and strong after 18 months of tracking everything.

Adam at 42 doing a plank on a Pilates reformer

Age 42 · gym

Still training on data. Every session, every meal.

Adam at 42 on a boat at the beach

Age 42 · beach

The result of eating for recovery, not for a plan.

The turning point

At 39 Adam saw a family photo and barely recognized himself. Trainers, diet programs and fitness apps had all failed for the same reason: none of them were built around his actual data.

So he started tracking everything by hand: sleep, HRV, recovery, nutrition timing, workout performance. Spreadsheets became correlations, and the correlations turned up patterns no generic program had ever addressed.

The transformation took 18 months. The real breakthrough was the method: everyone deserves guidance based on their own data, not averages. That method became Plait.

How the human and the AI work together

A clear division of work: product judgment, generated meal ideas, and independently calculated nutrition.

01 · Human

Direction and review

Adam sets product direction and reviews changes. Content drafts go through a separate editorial review before publication.

02 · AI

Analysis at scale

AI proposes a weekly menu based on your calorie target and dietary preferences. Daily guidance uses your available sleep and recovery signals; missing data is clearly identified.

03 · Data

Evidence, not vibes

Ingredient nutrition comes from a curated USDA food catalog. Calories are calculated from protein, carbohydrate and fat, then checked against your daily target. Wearable calorie estimates and nutrition calculations remain approximations.

Questions about the methodology, or want to collaborate? Write to support@plait.fit.

Eat for the recovery your wearable is measuring.

Connect WHOOP, Garmin or Apple Watch (beta) and get today's calorie and macro targets in about a minute. Free to start, no card required.