Making AI smarter, safer, and more efficient.
We're an AI research lab. We build the protocols, datasets, and efficient training methods behind next-generation AI — and offer every layer as a service, so the stack you scale on is yours. Most of our work is open.
Our Partners
- Ziva
Scaling AI on rented infrastructure follows a script.
Cost, lock-in, and burn rate — the same three scenes play out at nearly every company building on someone else's stack.
Scene 01 — Cost
Scene 02 — Lock-in
Scene 03 — Burn rate
Storyboard
The pilot was cheap; production isn't. Metered GPUs, per-token pricing, and egress fees mean spend scales with every user you win — and the meter never sleeps.
- Idle clusters bill around the clock
- Per-token costs grow with adoption
- Egress fees to reach your own data
Pipelines calcify around one vendor's formats and APIs. By the time pricing changes — and it will — migrating costs more than staying. They're counting on it.
- Proprietary formats and APIs
- Data gravity makes leaving expensive
- Repricing arrives without recourse
Infra spend compounds while the roadmap crawls. Every month on rented rails converts runway into someone else's margin — and the models still aren't yours.
- Burn scales ahead of revenue
- Compute competes with headcount
- Runway measured in GPU hours
Three scenes, one root cause — infrastructure you rent but don't control.
Every layer of the AI stack, offered as a service.
Synthetic data, training, efficiency, and a research team alongside yours — four services that end with models and data you own.
Service 01 — Data
Service 02 — Training
Service 03 — Efficiency
Service 04 — Partnership
Service index
01Synthetic datasetsData
High-quality training data, generated and validated by our pipeline. Start from the sets we've already released, or commission data built for your domain.
- Code-generation and UI/UX corpora
- Validation built into the pipeline
- Released with reproducible harnesses
02Model trainingTraining
Custom training and fine-tuning built on our efficient-methods research — frontier-grade results without a frontier-grade compute budget.
- Fine-tuning on your own data
- Efficient architectures by default
- Evaluated before handoff
03Model efficiencyEfficiency
We make models smaller, faster, and cheaper to serve — so inference runs on hardware you control instead of a metered endpoint.
- Quantization and distillation
- Inference optimization
- Deploys on your own hardware
04Expertise on tapPartnership
Our researchers work across the whole board with your team — architecture, data, and training decisions grounded in work we publish openly.
- Direct line to the lab
- Reviews at every layer
- Methods published, not gatekept
Four layers, one outcome — models, data, and infrastructure that are yours.
Products that prove the research.
Our products are testbeds for our protocols, datasets, and efficient methods — they prove the work at scale.
Design tools
Developer tools
Infrastructure
Open source
01AI-native design editor
The premier design editor for the AI era.
Describe your vision, refine it on a real canvas, build a token system, and convert to production-ready code — with no design-to-code handoff.
- 01AI-native canvas
- 02Built-in design system
- 03Production-ready code
02Rust-native code platform
Your development environment, supercharged.
A complete IDE and local desktop apps, built on our context protocol research for efficient, accurate assistance.
- 01Rust-native IDE & desktop apps
- 02Context-aware assistance
- 03Efficient by design
03Multi-cloud deployment
Deploy anywhere without the complexity.
Foltrac handles the infrastructure so you can focus on building — our agent systems research powers its autonomous operations.
- 01Deploy anywhere
- 02Autonomous operations
- 03Infrastructure that manages itself
04Agent-native version control
W0rktreeVersion control that keeps up with agent fleets.
A Rust version-control core built for thousands of concurrent workers — copy-on-write snapshots, scoped views, and optimistic merge queues instead of locks. GPL 3.0, on GitHub.
- 01Copy-on-write snapshot worktrees
- 02Scoped views under 2% of a repo
- 03Optimistic merge queues, no locks
The future of AI should be built together.
We publish our work as it lands — papers, datasets, and model weights, open for anyone to build on.
From the lab.
Papers, specs, datasets, and announcements — published as they land, open for anyone to read.
