AI-native design
A canvas for ideas. Code you can build on.
Move from a written brief to an editable interface. Shape the layout on a visual canvas, work with design tokens, and generate code from the same design.
Explore NokuvaThe AI Factory for building your own frontier capabilities.
LegionEdge is an AI/ML lab dedicated to building the frontier models of the future, and to making it easier and more accessible to build your own. Every layer is offered as a service, so the stack you scale on is yours. Most of the work is open.
01Protocols02Data03Inference04Training05Compute
Our partners
- Ziva
The scaling story
From your data to a model you can operate.
Building useful AI takes more than access to a model. It takes the right data, a training method that fits the task, and infrastructure that fits your product.
LegionEdge brings those disciplines together. We help you develop the capability inside your business, with a clear path from the first experiment to deployment.
How we can helpThe workload comes first.
Start with the task, the data, and the constraints. We establish an evaluation baseline before choosing a model or committing to a training run.
The layers work together.
Data quality, training, and serving are connected decisions. Work with one team across the stack, or bring us in for the part you need.
The work stays useful.
The engagement includes the agreed datasets, weights, evaluations, and implementation details. Your team can inspect the result and keep building on it.
Services
Specialist work at every stage.
Engage the lab for one part of your stack or the full project. Each engagement starts with a defined scope, evaluation criteria, and a practical handoff.
Managed datasets
Start with our open corpora or commission data for your domain. Generation, curation, and validation are designed around the task you want to teach.
Typical handoffValidated corpus, provenance, and a reproducible data pipeline.
Model training
Fine-tune an existing model or scope a custom training run. Compare the result against a baseline and the evaluations that matter to your product.
Typical handoffModel weights or adapters, evaluation results, and run configurations.
Inference optimization
Reduce the cost of serving a model through quantization, distillation, and runtime optimization, with quality and performance measured together.
Typical handoffAn optimized model, benchmark results, and deployment guidance.
Protocol design
Define how your agents, tools, and systems exchange information. Make context, interfaces, and compatibility explicit as your architecture grows.
Typical handoffA protocol specification, reference implementation, and conformance tests.
Research partnerships
Work directly with the lab on a technical question. Structure the experiments, review the evidence, and decide what is worth taking into production.
Typical handoffA scoped research program, experiment results, and technical recommendations.
Dedicated compute
Capacity & pricingOn-demand or reserved GPU capacity for training and serving. Choose the infrastructure your workload needs, with clear hourly pricing.
Starting at
$4.79per GPU-hour
Products
Research in practice.
Our work in context protocols and agent systems becomes tools for designing, developing, and shipping software.
All productsDeveloper tools
An editor that works with your context.
A Rust-native IDE and local desktop tools built around our context protocols. Bring project context into the coding workflow, where your AI assistant can use it.
Explore TavocCloud infrastructure
Deployment across clouds.
Application deployment and infrastructure operations, informed by our agent systems research. Bring your product across clouds without making each one a separate workflow.
Open-source version control
Version control for agent fleets.
A Rust core with copy-on-write snapshots, scoped views, and optimistic merge queues for concurrent work. Open source under GPL 3.0.
Developer documentation
Documentation for the work ahead.
Working examples and technical references for connecting an application, training a model, and putting it into production.
Browse documentationOpenAI-compatible API
Examples in Python, JavaScript, and curl.
Getting started
Quickstart
Create a workspace API key, choose a model, and send a request with an OpenAI-compatible SDK.
Authentication · Model selection · First request
API reference
Chat completions
The request and response contract for inference, with examples for messages, parameters, and streamed output.
Request fields · Response formats · Streaming
Model development
Fine-tuning
Prepare a training dataset, configure a run, evaluate the result, and serve your model on dedicated capacity.
Training data · Evaluation · Deployment
Latest news
Product releases, research, and engineering notes from the team.
PartnershipJul 18, 2026
LegionEdge partners with Google Cloud
A dedicated NVIDIA H200 cluster on Google Cloud now powers our training and research — and Google Cloud becomes a first-class target for everything we ship.
ProductJul 17, 2026
LegionEdge launches LegionEdge Cloud
One panel for the compute side of AI work: train models, manage capacity, and spin up inference — on our fleet or yours.
CompanyJul 16, 2026
LegionEdge acquires Haze
The Haze team joins Nokuva as part of our initiative to build the next layer of infrastructure for design.
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Visit researchGet started
Start with a workload.
Connect to the platform directly, or work with our team on a specific technical challenge.
Self-service
LegionEdge Cloud
Create a workspace, explore the available models, and connect your application. The documentation takes you through your first request.
Custom engagements
Work with our team
Bring your data requirements, model goals, or infrastructure constraints. We'll define the scope, deliverables, and next steps together.
Discuss your project