Stop renting it. A private Large Language Model on hardware you own: full data privacy, offline capable, no monthly subscription.
Private. Offline-capable. No tokens metered. Runs on a single workstation in your office.
Engineered in Kuala Lumpur · deployed across Malaysia & Southeast Asia · MY · SG · TH · ID · PH · VN · BN
Founded 2015 · led by Ts. Dr. Leong Yee Rock - engineers who size, build & commission the system, then hand it over working. No black box, no lock-in.
A foundation model like Qwen3.6, Kimi K2.6 or DeepSeek V4 is a brilliant generalist - and it knows nothing about your company. It has never seen your product catalogue, your pricing, your customers, your past decisions or your KPIs. Plugging staff straight into a raw LLM gets you generic, forgettable answers. VYROX AI is the value-creating layer in between - the engineering, the fine-tuning, and the continuously learning system that turns a generic model into your company's private intelligence, working for your employees, your customers and your decision-makers, every day, on hardware you own.
VYROX AI doesn't just sit in the middle - it adapts both sides at once: fine-tuning the model toward your business, and shaping your workflows to what the model does best, so the two meet in the middle for the best possible outcome.
Most AI tools optimise for one person typing one prompt. Because VYROX AI is trained on the whole company's data and fed by everyone who uses it, its biggest impact shows up a level higher - in how teams talk to each other, and in how leadership decides.
"Adapting both sides" isn't a slogan - it's a concrete technical pipeline VYROX builds and operates for you, using the same techniques the frontier AI labs publish, applied to your business.
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| Layer | What it does | Built with |
|---|---|---|
| 1 · Data layer | Ingests and cleans your product, service, customer, support-ticket, design and decision data into a structured, searchable knowledge base. | Document parsing, embeddings, vector DB (Qdrant) |
| 2 · Knowledge layer (RAG) | Retrieves the exact facts relevant to a question before the model answers, so responses are grounded and citable, not hallucinated. | Retrieval-Augmented Generation, nomic-embed |
| 3 · Alignment layer (fine-tune) | Shapes the model's tone, format and judgment to match your brand voice, support style and internal standards - behaviour, not just facts. | LoRA / QLoRA fine-tuning on curated examples |
| 4 · Goal layer | Encodes your current goals, targets and KPIs as context the model weighs in every recommendation, so outputs stay commercially relevant. | Structured KPI/goal context, decision-history RAG |
| 5 · Feedback layer | Captures outcomes - which recommendation was chosen, what happened - and periodically retrains the model so it keeps improving. | Continuous fine-tuning cycle, human review gate |
Layers 1-2 typically ship first (fast, low-risk, immediately useful); layers 3-5 are added as your team's usage and data mature - the same crawl-walk-run path used across our chatbot → workflow → agentic spectrum.
VYROX AI stands between the open-weight LLM and your organisation. It handles everything the raw model can't do on its own - understanding your business context, staying current, enforcing your rules - so employees and customers get a system that speaks fluent "your company," not a generic chatbot.
We fine-tune the custom LLM on your product and service catalogue, customer records and history, design and brand style, and your customer-support tone - so answers sound like your company and are backed by your facts, not the model's generic guesses.
Every interaction - past decisions, current goals, targets and KPIs - feeds back into the model through the feedback layer above. The more your team uses their VYROX AI, the more accurate and business-aware it becomes.
Because it's grounded in your real decision-making history, goals and KPIs, VYROX AI can lay out solution alternatives with a recommended option, forecast outcomes, and help design the strategy and roadmap a decision-maker needs to raise profit and cut cost.
Beyond conversation, VYROX AI designs and deploys agentic AI agents that plan, call your internal tools and systems, and execute multi-step workflows on their own - automating the repetitive operational work behind the scenes.
Staff get accurate answers on policy, process, products and pricing in seconds instead of hunting through documents or waiting on a senior colleague - trained on your SOPs, not generic best practice.
Customer-facing chat and voice that actually knows your products, stock, pricing and support history - consistent, on-brand, and available around the clock without adding headcount.
Leadership gets a system that reasons over the company's own goals and numbers - not a static dashboard, but a co-pilot that can be asked "what should we do about this?" and answer with your context in mind.
| For decision-makers, VYROX AI | What that looks like in practice |
|---|---|
| Proposes alternatives | Lays out multiple viable solutions to a business problem, each with trade-offs, and highlights the one it recommends. |
| Predicts outcomes | Uses your historical decisions and current KPIs to forecast where a choice is likely to land. |
| Plans & designs strategy | Drafts the roadmap and milestones to move from where the company is to where its targets say it should be. |
| Optimises profit & cost | Aligns every recommendation to your stated goals - maximise revenue and margin, minimise waste and spend. |
| Automates the follow-through | Hands the repetitive execution to purpose-built AI agents, so people focus on the decisions only they can make. |
| Keeps it private & compliant | Runs on hardware you own, so the goals, KPIs and decisions it learns from never leave the company - see Security & compliance. |
It knows your business, thinks toward your goals, and gets more valuable every day your people use it. Anyone can point staff at a public chatbot - only VYROX AI turns that raw model into a private system trained on your data and built to compound in value, so the intelligence your company owns tomorrow is smarter than the one it has today.
The full picture lives across six focused pages: what a private LLM can do for you, which models and hardware fit, what it costs, and why local wins. Start anywhere.
From document Q&A and RAG chatbots to workflow and fully agentic AI - plus the AI Doctor, AI Accountant and AI Lawyer co-pilots for regulated professions.
See the use cases →Benchmarks, the full GPU and Apple-Silicon map, a VRAM calculator, "what can my machine run?", and a live token-speed simulator - size your build with real numbers.
Explore models & hardware →Desk, Studio, Engine and Rack builds with itemised parts, a TCO calculator against your cloud spend, and the typical 7-month break-even - in writing.
Run the cost math →Citizen and state data that never crosses a border - air-gapped deployments aligned with PDPA, public-sector data classification and national AI governance.
Read the sovereign-AI case →Why prompts that never leave your building beat any vendor policy: the security model, deployment modes, and the assurances we put behind every build.
See why local wins →Plain-language answers to the questions everyone asks, a jargon-free glossary, and our essay on giving the right people the right AI - not everyone a chatbot.
Browse the resources →Manufacturing, retail and distribution, education, property management, logistics and hospitality: what a private AI does first in each, and what not to start with.
Browse industries →Preparing your documents, scoring a first use case, the week-by-week pilot, what to measure, and the eight reasons first projects stall.
Start here →Threat model, access control and roles, what gets logged, network isolation and air-gap, prompt injection, backup and recovery, plus a hardening checklist.
Review the controls →67 terms defined without jargon, grouped by what you are trying to do, plus the pairs people most often confuse.
Look up a term →The whole VYROX AI story - problem, solution, numbers - as a fast slide deck you can forward to your boss.
A clear, six-phase engagement. Indicative timelines for a typical SME deployment; you get something concrete at every step.
We map use cases, review your data and infrastructure, and assess fit. You get: a written findings summary and an honest local-vs-cloud-vs-hybrid recommendation. No cost, no obligation.
We design models, hardware sizing, deployment mode, integrations and security controls. You get: a detailed spec and a fixed-price quote in RM with timeline.
We source and configure hardware (or provision your VPC), install the stack, and build your RAG/data pipeline. You get: a configured, tested system; procurement handled for you.
We deploy on your premises (or tenancy) and connect data sources, SSO and existing tools. You get: a live, integrated, security-hardened system.
We tune retrieval and prompts on your real data, run accuracy checks, and train your staff. You get: a validated system, trained users, and docs (EN/BM).
Monitoring, updates, model upgrades and a support line. You get: SLA-backed support and a roadmap for new use cases.
A free 45-minute Local-AI Audit: your real cloud spend today, the right hardware, the models, and the costed payback - no obligation, no sales deck.
Cloud AI (ChatGPT, Copilot, Claude subscriptions) is fast to start and easy to try. Local AI (a private LLM running on hardware you own) costs more upfront and pays back over time. Here is the comparison without the marketing gloss, using the same figures already on this page.
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| Dimension | Cloud AI subscription | Local AI (VYROX build) |
|---|---|---|
| Cost at 10 users | Roughly RM 80-150 per seat per month, forever, and it rises as usage grows | One-time build from RM 22,000 (Studio AI tier), then electricity only |
| Typical break-even | Never breaks even, it is a recurring cost line item | Around 7 months against typical cloud AI spend, illustrative example |
| Where your data goes | Sent to a third-party cloud server, often outside Malaysia | Never leaves your building or your private network |
| Works with no internet | No, an outage or ISP fault stops your team cold | Yes, fully air-gapped operation is possible (offline, no external network at all) |
| Customisation depth | A system prompt and file uploads, reset every session | Fine-tuned on your own product, customer and support data, and it keeps learning |
| Model choice | Locked to whatever the vendor ships this month | Pick from 51+ open models (Qwen3.6, Kimi K2.6, GLM-5.1, DeepSeek V4 and more) |
| Vendor can change terms | Yes, pricing, limits and features can shift with no notice | No, you own the model, the pipeline and the hardware outright |
Cloud AI still makes sense for quick, low-stakes experiments or a single freelancer. Local AI earns its keep once a team of 5 or more relies on it daily, or the data involved is patient records, client files, financial statements or anything under PDPA (Malaysia's Personal Data Protection Act, our data-privacy law).
Replace a growing stack of per-seat AI subscriptions with one system you own, trained on your own catalogue and customer history.
The AI Doctor co-pilot turns consultations into SOAP notes and referral letters without patient data ever touching a third-party server.
Contract review, drafting and research over matter files, with solicitor-client privilege preserved because nothing leaves the firm's network.
Invoice, receipt and bank-statement extraction plus MFRS reporting help, on a server that never sends client financials to the cloud.
Citizen-service and internal knowledge assistants that keep state and citizen data on government-owned infrastructure. See the sovereign AI page.
Not a demo script. These are ordinary requests a staff member can type into the private chat window in the first week, before any fine-tuning or agent work has started. Each one only needs the documents you already have, loaded into the knowledge layer.
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| Role | What they actually type | What it needs to answer |
|---|---|---|
| Owner / MD | "Summarise the five biggest issues raised in customer complaints last quarter, and tell me which ones repeat." | Complaint log or ticket export |
| Admin / HR | "What is our leave carry-forward rule for confirmed staff, and where does the handbook say it?" | Staff handbook, HR policy PDFs |
| Finance | "Pull the line items from these 40 supplier invoices into a table with date, supplier, amount and tax." | Scanned invoices, a vision model |
| Sales | "Draft a quotation cover letter for this enquiry using our standard terms, and flag anything the customer asked for that we do not offer." | Price list, standard T&C, past quotes |
| Operations | "Which step of the delivery SOP covers a damaged carton on arrival, and what does the driver have to record?" | SOP documents, forms |
| Customer support | "Write a reply to this email in our usual tone, in English and Bahasa Malaysia, and keep it under 150 words." | Past reply examples, tone guide |
| IT / technical | "Explain what this legacy script does, then rewrite it with comments and error handling." | The source file, a coding model |
| Management team | "Turn these meeting notes into decisions, owners and deadlines, and list what was left unresolved." | Raw notes or a transcript |
The pattern is the same in every row: the model supplies the language and the reasoning, your documents supply the facts. That is why the first two engineering layers, the data layer and the knowledge layer (RAG, Retrieval-Augmented Generation), ship first. Fine-tuning, goal context and agents come after your team has a habit of asking.
Every vendor lists the wins. Here is the other half, so you can plan around it instead of discovering it in month two. None of these are reasons not to deploy. They are reasons to scope the first use cases properly.
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| Limitation | What it means in practice | How we work around it |
|---|---|---|
| No live web by default | An offline or air-gapped system cannot look up today's exchange rate, a news item or a competitor's website. | Feed it a scheduled internal data export, or, if policy allows, add a controlled one-way search tool on a non-air-gapped deployment. |
| Only as good as your documents | If the SOP is three years stale or two versions of the price list exist, the model will confidently repeat the wrong one. | Document clean-up is part of the data layer. We flag conflicts and duplicates during ingestion rather than after go-live. |
| Not a substitute for a professional | The AI Doctor, AI Accountant and AI Lawyer co-pilots draft and summarise. They do not diagnose, sign off accounts or give legal advice. | Human review gates are built in. The qualified professional reviews and signs, exactly as they do today with a junior's draft. |
| Image and video generation is separate | A text model does not create pictures or video. Those need different models with their own hardware appetite. | Scoped as a separate workload if you need it, sized on its own. It is not bundled into a language-model build by default. |
| It can still be wrong | Grounding in your documents cuts hallucination sharply, but no model is at zero. Numbers, names and dates deserve a glance. | Citations on by default, plus an accuracy check during the tune phase using questions your team already knows the answers to. |
| It does not read your mind | A vague prompt gets a vague answer. Staff who have never used AI need an hour of practical guidance, not a manual. | Staff training is part of the tune-and-train phase, with worked examples from your own business. |
| Hardware sets a ceiling | A Desk AI tier machine cannot serve 40 concurrent users or run a frontier-scale model at full precision. | Size honestly at audit stage. The tiers exist precisely so you buy for the load you actually have. |
| Integration is scoped, not free | Connecting to accounting software, an ERP or an EMR takes engineering. Each system is different. | Every integration is quoted per project over MCP (Model Context Protocol). We say no when a connection is not worth the effort. |
Every item above applies to cloud AI too. The difference is that a local build lets you see and control each of them, because the model, the documents and the guardrails are all on hardware you own.
A one-time build is not a zero-cost system. It is a small, predictable one. Four things cost money after handover: electricity, maintenance, model upgrades and, eventually, hardware refresh. Three of those four are cheap or free.
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| Cost line | How often | What drives it |
|---|---|---|
| Electricity | Monthly | Power draw of the GPU or Apple Silicon machine, multiplied by the hours it is actually generating, plus a little for idle and cooling. Worked example below. |
| Maintenance and support | Per SLA | Monitoring, patching, backups and a support line. Optional, but most teams take it because nobody wants to own the stack alone. |
| Model upgrades | Free | Open-weight models carry no licence fee. Moving from one to its successor is a download and a config change, not a new contract. |
| Storage growth | Occasional | Model files and your document index grow. A disk is cheap next to the build, and it is the easiest part to expand. |
| Hardware refresh | Multi-year | The machine keeps working after the models move on. Refresh is a planned capital decision, not a surprise, and the old unit still runs the older models. |
| New use cases | On request | Additional integrations, agents or fine-tuning rounds are scoped separately. Nothing is metered per question or per user. |
Local AI is not the right first move for everyone. Run through these before you spend anything. If most of the left column is true, a build will pay for itself. If most of the right column is true, wait, or start with a small pilot instead.
Amber is not a no. It usually means a smaller starting point: one department, one document set, one Desk AI class machine, and a decision point after a few months of real use. The free audit exists to reach that answer honestly, including the answer that you should not buy anything yet.
Bring those six answers to the audit and you will leave with a costed spec and a break-even date rather than a brochure.
Short, plain-language answers. For the full glossary and long-form FAQ, see the Resources page.
Book a free 45-minute Local-AI Audit. We measure your current cloud spend, spec the exact build, and give you the costed break-even date - in writing, no obligation.
No deck pitch. Just engineers sizing your build.
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