SuperTuned
Site map
- Home — https://supertuned.ai/
- Team — https://supertuned.ai/team
- Contact — https://supertuned.ai/contact
- hello@supertuned.ai
01. The bet
A specialist model that runs inside the live-ops loop.
Stealth until summer 2026.
02. What we do
A base model and a growing library of specialist adapters, trained against the multi-dimensional preference real operators run their games on — retention, monetization, fairness, feel. Every adapter is post-trained inside the environment of the studio whose work it captures. Studios keep their data. We ship the adapter. The library compounds as the industry does.
Trained on what shipped, what stuck, and what converted.
03. Why it has to be us
Four prerequisites. One team that has all four.
Data
200 million players' worth of live behavioral signal. Not licensed, not scraped, not borrowed. Studios keep custody — we train adapters inside their environment. No telemetry leaves the studio.
Domain
Twelve years operating live economies. Sixteen years shipping games. Co-founders for twenty-four years. Every company since 2002.
Method
A post-training stack run with our training partner. A shared open-weights base, plus a library of per-studio adapters tuned against operator-elicited preference.
Distribution
First commercial deployment, summer 2026. Stealth until launch.
Most teams claiming to build AI for games have one of these. We have all four.
04. The model
A base model. A library of adapters. One layer for every studio.
An open-weights base in the 8B–30B parameter range, with per-studio low-rank adapters that hot-swap at serve time. Each adapter is trained inside the studio's environment against the multi-dimensional preference that operator runs on — retention × monetization × fairness × feel. Studios keep the data. We keep the library. Cross-studio compounding lives in the library, not the base.
- Players in the training signal: 200M+
- Shipping games: 16 years
- Operating live economies: 12 years
- Founding partnership: 24 years
- Prerequisites met: 4 / 4
- First deployment: Summer 2026
One base. Many adapters. One library.
05. On the name
Tuned is fitting to the past. Super-tuned is fitting to the preference behind it.
Most specialist models are trained on (input, output) pairs from historical work. They curve-fit to the surface — the artifacts a team shipped, not the judgment that produced them. SuperTuned is post-trained against an operator-elicited preference signal that captures the multi-dimensional shape real operators run live products on — retention × monetization × fairness × feel. The preference is the asset. The adapter is the artifact.
- TUNED: Fitted to historical designs. Strong inside the portfolio. Weak outside it.
- SUPER-TUNED: Fitted to the underlying preference. Generalizes across genres, formats, and stacks.
06. What's next
A tuning layer for the games industry. Quietly.
One adapter goes live in August. The next ones get easier. If you're running live signal of your own, we should talk.
Team
Two founders. Twenty-four years.
Roby John and Navneet S Waraich ("W") have co-founded together for twenty-four years, every company since 2002. SuperTuned is what they've been building toward.
- Roby John — CEO. Operator. Sixteen years shipping games. Twelve years operating live economies. https://www.linkedin.com/in/robyjohn
- Navneet S Waraich ("W") — CTO. Architect. Two decades on distributed systems and game infrastructure. https://www.linkedin.com/in/nswaraich
Contact
Talk to us.
We reply within one business day.