The universal MCPfication platform for enterprise AI transformation. Turn existing tools, workflows, and APIs into governed MCP tools while federating 1,000+ third-party MCPs through one unified control plane.
AI guardrails powered by Resource Aware Attention. Faster on any CPU than competitors on a $15K GPU with industry-leading accuracy.
CPU-OptimizedLow LatencyHigh AccuracyEdge Ready
Bud Cache
The enterprise-grade, accuracy-first response cache for Large Language Models. Reuse answers across genuinely equivalent requests on commodity CPUs, cutting inference cost and latency without ever serving a wrong answer.
Build, fine-tune, and ship custom models at scale. A complete foundry to train, evaluate, and deploy domain-specific models on your own infrastructure.
Fine-tuningEvaluationCustom ModelsDeployment
GenZ
A powerful general-purpose LLM finetuned for reasoning and instruction-following. Optimized for enterprise workloads with strong performance across benchmarks.
Explore the full collection of Bud's domain and use-case specific finetuned models. Optimized for performance across various industries and applications.
Zero-trust model ingestion framework integrated within Bud Runtime. Protects against supply chain attacks with sandboxed evaluation, deep scanning, and continuous runtime monitoring.
Advanced latent space exploration and representation learning. Unlock deep insights from your data with state-of-the-art embedding and feature extraction.
Transform from bare metal provider to AI-first cloud platform. Enable Model-as-a-Service, Token-as-a-Service, and AI PaaS offerings with enterprise-grade infrastructure.
Model-as-a-ServiceToken-as-a-ServiceAI PaaSSovereign AI
Bud For Original Equipment Manufacturers
Ship AI-native devices that work out of the box. Pre-integrated AI stack with zero-config deployment, OTA updates, and enterprise security built in.
A turnkey private AI appliance pairing Bud's inference stack with validated Dell infrastructure. Deploy enterprise GenAI on-premises with zero setup — pre-integrated, tested, and ready to run.
An enterprise AI Foundry optimized for AMD Instinct accelerators. Run, scale, and govern private and cloud AI models on AMD hardware with full performance and no vendor lock-in.
Ralph Laurencut its styling-agent run-rate 81% — $220K→$40K/mo — at equivalent accuracy, on existing hardware.
▲ Agents
08Bud Agentaugment & automate
07Bud Studioconsume & share
06Bud SENTRYsecurity & governance
05Bud MCP Foundryintegrate
04Bud AI Foundrydeploy & serve
03Bud Model Foundrytrain · 120+ archs
02Bud Podexperiment · GPUaaS
01Bud LayerZeroany hardware
Silicon ▼
Watch
Bud AI OS in 90 seconds.
The real failure point
It's not the models. It's the stack.
80% of enterprise AI never delivers value — and the post-mortems keep blaming the model. They're looking in the wrong place. The model works in the demo. It's everything around the model that breaks in production.
The DIY stack
What "build it yourself" actually looks like
Cloud frontier API — per-token, no FinOps
$ latency + lock-in
Orchestration framework — LangChain / custom
$ glue code
Vector DB + retrieval layer
$ sync drift
Eval / observability — separate vendor
$ blind spots
Guardrails — bolted-on gateway
$ gaps
FinOps + governance — spreadsheets & 3–5 more tools
2–4×
Token costs spiral — 40–60% of spend lost to overhead & oversizing
Unified control plane — audit & compliance built in
The Bud AI OS
One platform. Zero fragmentation.
The industry's first native Enterprise AI Operating System — a single stack from silicon to agents that you own, govern and run anywhere.
Bud is the only stack where every layer shares one data model — so they compound instead of leaking cost and signal at each handoff. That's the thing no point tool, however good, can give you.
Build & consume
Bud Agent
augment & automate
A universal enterprise agent — a PA & intern for every employee, with guardrails, observability and audit built in.
Bud Studio
consume & share
Consume models and build & share agents from a familiar dashboard — desktop, terminal, VS Code & web. 60+ pre-built agents.
Govern & integrate
Bud SENTRY
security & governance
Zero-trust security, custom guardrails, enterprise RBAC, FinOps controls and compliance monitoring — at the runtime level.
Bud MCP Foundry
integrate
Turn existing enterprise software, APIs & workflows into secure, federated MCPs — no custom integration. GenAI-ready from day one.
Deploy & train
Bud AI Foundry
deploy & serve
One stack for multi-modal inferencing, scaling, observability, evaluations, memory, tools & data — open and closed models alike.
Bud Model Foundry
train
Train across 120+ architectures with PEFT, DEFT, post-training & agentic training — optimised for low compute, memory & bandwidth.
Infrastructure
Bud Pod
experiment
Private GPUaaS, AIPaaS & serverless with one-click deployment, job scheduling and pipelining for researchers and developers.
Bud LayerZero
any hardware
Heterogeneous hardware abstraction across NVIDIA, AMD, Qualcomm & Intel — 600+ hardware types. Deploy in any environment.
Live deployments
Measured results, not projections.
Numbers from production systems running today — across regulated, cost-sensitive, sovereignty-critical industries.
RL
Ralph Lauren — AI styling agent
Retail · named reference
↓ 81% run-rate
$220K/mo
$40K/mo
Prior stackOpenAI · 20s 85.46%
With BudTuned SLM · 6s 85.44%
81%
lower run-rate
3.3×
faster response
6.5×
faster to market
±0.02%
accuracy delta
This case proves the inference + model layer of the OS. The deployments below extend the same architecture across the full stack — governance, agents and sovereign infrastructure included.
Financial services
300%+
ROI on back-office automation
Annual TCO from $2.4M to $768K. A domain-tuned SLM at 92% accuracy with frontier fallback. 60% less manual processing.
Government · sovereign
60K+
Users, fully air-gapped
A national tax authority running 39 agentic use cases on-prem. Zero external data exposure. Compliance built in at the runtime.
Healthcare
96%
Clinical documentation accuracy
Air-gapped, on-prem. 45% less physician documentation time, 87% physician approval. Patient data never leaves the perimeter.
The compounding architecture
Architected to compound with use.
Most enterprise software gets more expensive with scale — more tickets, more drift, more cost. Bud AI OS is built so the opposite happens.
01
Agents run
Production workflows generate real signal on what succeeds, fails, and where accuracy gaps live.
02
ART trains SLMs
Agentic Reinforcement Training turns that production data into cheaper, sharper domain models.
03
Context optimizes
Prompts, retrieval and workflows auto-tune against live performance — no manual prompt-engineering treadmill.
04
Better agents
Smarter models feed back in. Better agents produce better data, which trains even better models.
Why a fragmented stack can't do this: when your agent framework, training platform, inference engine and governance are four separate tools, there's no shared data model for the signal to flow back through. The loop only closes when the layers are one system.
Why own it
The enterprises that win the next decade won't rent intelligence by the token. They'll own an AI estate — their models, their data, their hardware — that compounds into a moat competitors can't buy off a shelf.
Sovereign by design
On-prem, air-gapped or any cloud. Your IP and customer data never leave your perimeter.
Predictable economics
Token-level FinOps and cost-aware routing. Unit cost per task, not a surprise cloud bill.
Hardware freedom
600+ hardware types across NVIDIA, AMD, Intel & Qualcomm. No vendor lock-in, ever.
Governance built in
Guardrails, RBAC, audit and compliance at the runtime — engineered for regulated industries.
Book a demo
Own your AI, end to end.
See Bud AI OS unify your stack on your infrastructure. A 30-minute working session with our team — no slideware.
Mapped to your stack, hardware and target use case
A live proof-of-concept path on your own data
A cost & TCO model for your actual workloads
Book your demo
We'll get back to you within one business day.
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