Orca21 Labs is building a self-improving system that keeps every company on the best AI, and gets better at it with every company it serves.
AI gets better every few months, and most companies cannot keep up. 95% of company AI pilots show no measurable effect on profit, and only 36% of workers feel properly trained to use AI.
We are building a system that keeps every company on the best AI, gets better at it with every company it serves, and gets better at the way it improves. That is RSI?Recursive self-improvement. A system that gets better at its job, and also gets better at the way it improves itself., applied to how companies work. It runs as three loops, one inside the other.
The inner loop already runs for every company we work with. The middle loop is Pod?The part of our system we are building to learn across companies. It pools test results from 5 or more companies at a time, with no names and no documents, and finds which AI wins for which job., which we are building next (section 04). The outer loop is where the system learns to choose better changes.
We first built this loop for LessonOrca, our tutoring company. Each student got a harness?Everything one learner gets: their AI agents (such as a tutor and a quiz maker), their plan and their lesson formats.: their own tutor agents, plan and lesson formats. After every session, a strategy model read the results and proposed one change to the plan, and a tutor approved or corrected it.
The harness improved itself with every session, and the results followed.
After every session
Example
Proposed change to Maya’s plan
− Exponential functions
+ Transitions, part 2
Reason: her weakest skill on the last two practice tests.
A model proposes the change. A person approves or corrects it.
LessonOrca now runs on its own with hired tutors. Orcapo runs the same loop on how a company works. See LessonOrca tutoring.
The first step is Corporate 0.1, inside Orcapo, our product for companies. It tests every change on the company’s own past work, and today an expert approves every change. Here is one round, start to finish.
Our system inside Orcapo, for companies of 10 to 199 people. Every month it tests new AI on the company’s own past work. An expert approves, corrects or rejects every change, and the owner says yes before anything goes live.
The film follows one round at Tidewell, a fictional software company. The screens are product designs with example data.
Every test we run at a company records which AI won for which job, on that company’s own work. One company’s results help that company. Pooled across many companies, they show what works for a sales team, a support desk or a lab before anyone has to test it again.
That is Pod?The part of our system we are building to learn across companies. It pools test results from 5 or more companies at a time, with no names and no documents, and finds which AI wins for which job.. With each company’s consent, it will compare results across companies, with no names and no documents, and add the upgrades that win to the starting setup every new company gets. So each company starts ahead of the one before it, and the system gets better with every company it serves.
Each step runs on the same engine, so what it learns in one place carries over to the next. Pod starts once Orcapo meets its target. After Pod comes Corporate 1: a company whose tools and training improve on their own.
Co-founder and CTO
Self-supervised learning, foundation models and learner modeling
First-author paper with Google and MIT (IEEE Access, 2026): a self-supervised foundation model for spectroscopy that trains with 60–90% less labeled data
Technical lead, U.S. Treasury EyeNote: built its currency recognition model (99.9% precision) for 100,000+ blind and low-vision Americans, starting in high school
Inventor on a provisional patent in impedance spectroscopy: built Chronus Health’s best 3-part white blood cell classifier, in use for over a year
Architect of the LessonOrca Student Profile: turns hundreds of observations about a student into one context record
Built ZemoMemo: models each learner’s forgetting curve (FSRS-6) and schedules recall for 1,000+ learners
Co-founder and CEO
AI infrastructure, education businesses and go-to-market
AI/ML consultant at Databricks: infrastructure research with field engineering and product: lakehouse architectures, open table format benchmarks, enterprise AI strategy
Founder and CEO of WEInstruct, an SAT and ACT tutoring company: personalized plans for 30+ students, 5 tutors, $10,000+ revenue
Founder of Tin2Totes, a profitable recycling operation: 50+ collection sites across Washington and Oregon, recurring monthly revenue
Founder of The Bag Project: raised $10,000+ and distributed 10,000+ units across four states and Pakistan
Washington DECA State Vice President: elected to lead 15,000+ members across 15 regions; International Finalist and State Champion, National Economics Challenge
Berkeley SkyDeck, Pad-13 (Batch 22) · UC LAUNCH accelerator, Fall 2026
We are raising a $1M pre-seed round on a post-money SAFE.
Email us at hello@lessonorca.com.