Make business knowledge useful every day. Control your data, access and models.
From an employee finding a policy to an R&D team working with unpublished material: place inference and retrieval in a controlled environment and connect them to real work. Measure value with your own tasks.
Retrieve the policies this employee is allowed to read, prepare a checklist with sources, and let the employee verify the original text.
Authorised business documents
Local model and retrieval
Answer draft with sources
Employee review and use
Illustration only. Answer quality, citations and permissions require deployment tests.
VALUE IN EVERYDAY WORK
Start with the work your team already does.
BUSINESS / EFFICIENCY
Give time spent finding information back to the work.
Policies, product information and project files live in different places. Teams answer the same questions repeatedly.
How to use it
Build a cited knowledge base for questions, document comparisons, meeting summaries and report drafts. Answer from authorised sources and say when evidence is missing.
How to validate it
Compare retrieval time, citation accuracy and human edits on the same tasks. Improve one frequent workflow before expanding to the team.
BUSINESS / GOVERNANCE
Give your team an AI workspace with clear rules.
Separate tools make it difficult to manage what people enter, which documents they see and which model versions they use.
How to use it
Assign document access, assistant functions and model versions by department and role. Define usage policies, approvals and necessary operational records. Help new staff find authorised company knowledge.
How to validate it
Test permitted access and confirm that restricted documents are not retrieved or cited. Agree account removal, model updates and responsible owners.
R&D / ENGINEERING
Bring internal technical knowledge into the next development cycle.
Code, design documents and experiment records are team assets. Engineers need repeated tests and a clear account of the versions behind results.
How to use it
Assess local coding assistance, technical document questions, test drafts and tool workflows. Pin models, precision and test sets; integrate internal tools where agreed.
How to validate it
Measure test success, engineer edits and repeatability. Code merges, tool writes and releases remain subject to your review and approval process.
EDUCATION / RESEARCH
Keep research data and experiment conditions under control.
Unpublished papers, interviews, research datasets and student information need appropriate purpose and access controls.
How to use it
Use suitably de-identified or authorised samples to assess literature organisation, research code, model comparisons and teaching experiments. Set accounts, data scopes and repeatable model environments for each group.
How to validate it
Record model versions, datasets and experiment settings; check citations. Research teams confirm privacy, ethics, consent, copyright and model licences.
SECURITY / DEFINE THE BOUNDARY
Local data needs control over every connection.
Local inference and retrieval can process data inside an agreed environment. Control also depends on who can sign in, what they can read and where the system connects.
Map where data goes
Locate documents, indexes, prompts, logs and backups. Configure external search, tools, updates and supplier remote support separately.
Carry access into the answer
Apply identity and document permissions at retrieval, limit tool capabilities, and test unauthorised questions, wrong citations and malicious documents.
Keep the system dependable
Agree updates, encryption, backup recovery, retention, deletion and incident handling. Confirm responsibilities and support in the proposal.
Local deployment does not mean fully offline or zero risk. Networks, tools and operations need explicit design and validation.
HONG KONG / PRIVACY & AI GOVERNANCE
Make Hong Kong privacy part of the deployment design.
AI involving personal data needs assessment under the Personal Data (Privacy) Ordinance, Cap. 486. Local architecture can support control of location and operations; responsibilities depend on the organisation’s purposes, processes and safeguards.
Ordinance and data protection principles
Define collection, purpose and retention
The six principles cover collection, accuracy and retention, use, security, openness, and access and correction. Confirm purposes and permitted scope before connecting data to AI.
Assign responsibility for models, risk and human oversight
The PCPD model framework recommends governance, risk assessment, human oversight, testing and ongoing management. Include those arrangements in deployment and acceptance planning.
The employee checklist addresses permitted uses, privacy, lawful and ethical use, security and policy violations. Organisations can use it to develop internal AI policies.
Processor security and retention also require management. Buying hardware or locating data in Hong Kong does not establish compliance. Assess relevant sector rules, contracts and cross-border requirements for your organisation.
Official sources checked: 2026-10-08. This is deployment planning information; confirm legal applicability with your organisation and legal advisers.
When is local deployment worth assessing?
Consider data control, ongoing use and work outcomes together.
Recurring internal document questions, drafts or knowledge tasks.
Role-based control of sensitive data or unpublished R&D.
Pinned models and repeatable business or research tests.
A plan for data preparation, operations and team adoption.
Measure cost alongside useful results.
Compare hardware or rental, power, maintenance, integration and model updates with task completion time and human review. Suitability depends on usage frequency, quality and concurrency.
For occasional public-data tasks, or before access and operations are ready, a small pilot can help establish whether local deployment is needed.
Start with one workflow worth improving.
Bring shareable samples and questions. See local AI at work, then agree the delivery scope.
01
Choose a task
Pick policy Q&A, technical retrieval or research coding. Describe the current process and the outcome you want to improve.
02
Validate quality and boundaries
Test answers, sources, permissions, context and concurrency. Record the human review required.
03
Agree delivery and handover
Choose Spark, dual nodes or enterprise compute from the results. Confirm accounts, operations and support.
DGX Spark in stock. The agreed scope can be deployed in as little as 3 days once hardware, environment and requirements are ready.
No. Compare total cost, task frequency, model quality, context and concurrency. Measure time, human edits and resources on the same work rather than relying on peak hardware specifications.
Does buying a Spark complete enterprise AI?
Hardware is a compute starting point. Data preparation, model capacity, permissions, integration, acceptance and operations still need planning. Larger models or concurrency may require dual nodes, multiple nodes or enterprise GPUs, subject to tests.
How can a school start?
Choose literature retrieval, research code or teaching experiments with suitably de-identified or authorised samples. Confirm ethics, consent, copyright and model licences. Training or fine-tuning needs a separate model and hardware assessment.
Can local AI still give wrong answers?
Yes. Models can misunderstand, miss information or generate false content. Check sources, define what happens when evidence is missing, and retain human review appropriate to the work’s risk.
NEXT STEP / TALK TO US
Bring your workload. See a real demonstration.
Tell us your tasks, document formats and expected users. We can confirm models, configuration and delivery scope together.
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