Enterprise
The complete enterprise AI stack. Inside your environment.
Deploy Run BiOS in your cloud or data center for inference, training, evaluation, data generation and continuous model improvement. Your data, checkpoints, model weights and operational controls stay within your enterprise boundary.
Illustrative. The stack runs where you decide it runs.
Build with leading models
Claude Haiku 5.5
Run BiOS AdaptiveCChat LatestClaude Opus 5Claude Opus 5.5Claude Sonnet 5Claude Sonnet 5.5
DeepSeek V4 FlashDeepSeek V4 Flash 0731DeepSeek V4 ProDeepSeek V4 Pro 0813DeepSeek V4.1 FlashGGLM-5.2GGLM-5.3GGLM-5.3-Flash
GPT-4.1GPT-4.1 MiniGPT-4.1 nanoGPT-4oGPT-4o MiniGPT-5GPT-5 MiniGPT-5 nanoGPT-5.1GPT-5.4GPT-5.4 MiniGPT-5.4 nanoGPT-5.5GPT-5.6 LunaGPT-5.6 SolGPT-5.6 TerraGPT-6 AstraGPT-6 LunaGPT-6 Sol
Kimi K2.7 CodeKimi K3
MiniMax M3o3o3-minio4-mini
Qwen3.5 397B-A17BQwen3.8 2.4T-A95BWhat runs on it
One deployment, the complete platform
Catalog inference, adaptive routing, custom model training, evaluation, data generation, dedicated endpoints and the BiOS Conscious Loop run together inside the environment you control.
Custom model training
Expert models, domain-specific models and vision tuning, trained on your own data inside your own environment. This is the centre of the enterprise product.
What you can trainCustom model serving
Your finished checkpoints on dedicated endpoints inside your environment, called by model id like any other model.
How serving worksCatalog inference
Every model in the served catalog answers from inside your deployment through the same OpenAI-compatible enterprise API.
See the catalogAdaptive routing
Available in the deployment when a workload spans several models: one endpoint that reads each request and sends it to the right model for the task.
How Adaptive worksData generation and evaluation
Approved production signals become curated datasets and enterprise evaluations that govern each new model version.
See the improvement loopAgent and framework integrations
LiteLLM, LangChain, CrewAI, coding agents and messaging agents connect to your endpoint with the same three values.
See integrationsThe enterprise boundary
Your data, models and operations stay under your control.
Run BiOS installs inside the environment you choose. Datasets, production signals, curated training data, evaluations, checkpoints and serving endpoints remain within the network and governance model your enterprise controls.
Your cloud account or your data centre. Your network policy, your identity, your storage.
- Signed images
- Engine releases
- Licensing
- Support and on call
No customer data, ever. The only line that crosses the boundary carries software in.
What you get
Enterprise control on a complete managed platform
Your enterprise keeps control of the data, infrastructure, governance and model weights. Your developers keep one API, while the Run BiOS team operates the platform.
Custom model training
Train expert models inside your enterprise boundary
The complete training platform runs inside your deployment. Your corpus, task examples and evaluations become an owned model and a checkpoint your enterprise controls.
Expert models
A model that does one job better than any general-purpose model. Fine-tune on your own task examples, then align it, until the format, the tone and the judgment hold on every call instead of most of them.
Domain-specific models
Continued pre-training on your own corpus of contracts, policies, tickets, filings and code puts your vocabulary and conventions in the weights, so the model stops needing a prompt to explain your business to it.
Vision-language models
Tune a VLM on the forms, layouts, diagrams, images and inspection frames your enterprise workflow handles.
The Run BiOS training platform
Your data · Your GPUs · Exported checkpointsPre-training, continued pre-training and supervised fine-tuning, for language and vision language models, as LoRA or QLoRA adapters or a full fine-tune of every weight. Start with the cheap method, escalate to the thorough one when the evals say it plateaued, all from the same job config.
Custom model inferencing
Serve the model you trained like any other
A checkpoint is only worth what you can run. Your custom models serve from dedicated endpoints inside your environment, on the same API your applications already speak.
One API, one new model id
Your fine-tuned model answers on the same OpenAI-compatible endpoint as the catalog. Change the model id and keep the client, the SDK and the code path you already shipped.
Dedicated, isolated capacity
A custom model serves from a dedicated endpoint on capacity reserved and sized for your enterprise workload.
Catalog and custom, side by side
The models you trained and the models we serve sit behind the same API surface, so one application can call both: the specialist for the work that needs it, the catalog for everything else.
Illustrative. One POST /v1/chat/completions, two model ids.
BiOS Conscious Loop
Every approved cycle makes your enterprise model stronger.
The BiOS Conscious Loop turns approved production signals into curated data, trains the next model version, evaluates it against enterprise goals and promotes it when it performs better. The full cycle runs inside your environment on your schedule.
Your team controls the workflows, cadence, evaluations and promotion gate. Run BiOS can also propose the loop from your production signals for your team to approve and refine. Every version and the intelligence it produces belong to your enterprise.
How the BiOS Conscious Loop works- 01Serve. Your applications answer real work through Run BiOS.
- 02Record. Every interaction and correction is kept, on your storage.
- 03Curate. Good outcomes become examples, corrections become lessons.
- 04Train. A new version, on settings you approved once.
- 05Evaluate. Scored against production, on tests you wrote.
- 06Promote. Advances when the candidate clears the enterprise evaluation gate.
You set the cadence and the promotion gate. Every version is kept, so going back is one action.
Built for
When renting inference stops working
Most teams should use the managed platform. These are the situations where running the stack yourself is the only version that works.
Governed enterprise data
Workloads with strict processing, residency, privacy and access-control requirements.
GPU capacity you already own
Enterprise hardware and cloud capacity ready for a production inference and training stack.
A private environment
Isolated networks, private links and fully disconnected sites under enterprise control.
Models shaped by your work
Enterprise corpora, task examples, evaluations and operating judgment that become owned models.
One platform across the AI lifecycle
Catalog inference, custom training, evaluation, data generation and dedicated endpoints behind one API.
What does deployment look like?
01
Bring your capacity
GPUs you already own, or capacity you procure in your preferred cloud or data centre. The cluster stays yours: your account, your nodes, your network policy.
02
We install the stack
Run BiOS deploys the training and serving stack into your cluster with a dedicated, revocable setup credential, then scopes every runtime component down to its own identity.
03
Validate together
GPU readiness, endpoint reachability, routing and autoscaling are checked with your team before production traffic touches the cluster.
04
Train, serve and keep improving
Your applications use the same OpenAI-compatible API. Inference, training, data generation and evaluation run inside your boundary. The BiOS Conscious Loop improves models on the cadence and promotion policy you choose.
Questions teams ask before the first call
Where does the enterprise stack run?+
Inside your environment, including your cloud account or data center. Inference, training, evaluation, data generation, checkpoints and the BiOS Conscious Loop operate within your network and governance boundary.
Is it the same API as the cloud platform?+
Yes. One OpenAI-compatible API, the same model catalog and BiOS Adaptive routing across your on-prem deployment and our managed capacity. Code written against one runs against the other.
Who operates the deployment?+
Run BiOS operates the serving stack: model deployment lifecycle, upgrades, GPU health monitoring, autoscaling and observability. Your team owns the environment, the hardware and the network policy.
What about fine-tuning?+
Fine-tuning runs on dedicated GPUs inside your environment, and the checkpoint is a file you own. Export it and serve it inside your deployment or anywhere else. Our own training engine is written from the ground up, down to the model implementations and covers pre-training, continued pre-training and supervised fine-tuning, as a full fine-tune or as an adapter, for language models and vision language models.
How does the model improve over time?+
The BiOS Conscious Loop turns approved production signals into curated data, trains the next version and evaluates it against the model already serving. Your promotion gate advances the stronger version, and every version remains available for governance and rollback.
Who designs the Conscious Loop?+
Your enterprise can define each workflow, signal, evaluation, cadence and promotion rule. The Run BiOS team can also propose a complete loop from approved production signals for your team to review and refine. The resulting model and every version belong to your enterprise.
How portable are the models and assets?+
The weights, checkpoints, production signals, curated data, evaluations and version history remain on enterprise-controlled storage. Your enterprise can continue serving and developing those assets through the deployment it owns.
What if traffic outgrows our cluster?+
Hybrid capacity is an option: eligible workloads can overflow onto Run BiOS-managed capacity during spikes or while your own GPUs are being expanded or remediated. Which workloads may overflow, when and how routing behaves are agreed with your team up front.
What is the difference between an expert model and a domain-specific model?+
An expert model is trained on task examples with supervised fine-tuning, so it holds a format, a tone and a judgment call reliably. A domain-specific model is trained on a corpus with continued pre-training, so your vocabulary and conventions live in the weights. Most production models use both: pre-train on the corpus, then fine-tune on the task.
Can we serve our own fine-tuned models inside the deployment?+
Yes. A finished checkpoint deploys to a dedicated endpoint in your environment, behind the same OpenAI-compatible API and called by model id like any catalog model. You can also export the file and serve it somewhere else entirely.
What does it take to get started?+
A Kubernetes cluster with NVIDIA GPU nodes, network access that lets us manage the stack and a conversation with our team about your environment. We confirm fit, then install and validate with you.
Proof
Workloads already running this way
Anonymized engagements, as enterprise work should be. The document-heavy ones, where owning the model mattered as much as running it.
Legal
Legal: diligence at deal speed, with a model the firm owns
Contract review was bottlenecked by hours and a rented general model. A fine-tuned model on the firm’s own clause library changed that.
Drafts arrive in the firm’s own clause languageRead the studyRetail & e-commerce
Retail: a storefront that writes and answers in its own voice
Product copy and support answers ran on a premium model across the whole catalog. Right-sizing the models changed the economics.
The bill stopped tracking the sales calendarRead the studyManufacturing & logistics
Manufacturing: manuals that answer back
Maintenance knowledge lived in binders and inboxes. A fine-tuned model over the company’s own documentation brought downtime down significantly.
Downtime brought down significantlyRead the studyBring us your environment.
Tell us where training and inference need to run, whether that is a cloud account, a data centre or GPU capacity you already have and we will confirm fit, install and validate with your team.
One OpenAI-compatible API · BiOS Adaptive routing · We operate the stack