AI consulting · Hong Kong
Enterprise AI consulting, from strategy to production
AIKm is a Hong Kong AI consulting practice based at Hong Kong Science Park and one of the pioneering institutes shaping enterprise knowledge management in the city. We take enterprises from AI strategy to production: use-case selection, RAG and knowledge-graph implementation, agentic AI deployment, and governance for regulated bilingual operations under PDPO and cross-border data rules, guided by a farsighted team focused on the societal impact of deep learning and AI.
Engagement at a glance
What enterprise AI consulting with AIKm involves
- Who it's for
- Hong Kong and Greater Bay Area enterprises with meaningful data volume and repeated decision logic — trading, logistics, finance and insurance, property and facilities, retail and F&B, professional services and NGOs.
- What we work on
- AI strategy and readiness, enterprise RAG and knowledge systems, enterprise knowledge graphs, agentic AI deployment, AI governance for regulated business, getting found by AI systems, and setting up an in-house AI research lab.
- Delivery
- On-site at your Hong Kong office, at Hong Kong Science Park, Pak Shek Kok, N.T., or remote.
- Working languages
- English, Cantonese (廣東話) and Mandarin; bilingual English/中文 document corpora supported.
- Process
- Scoping call → 2–4 week discovery → 6–12 week pilot on real data → production rollout with evaluation, monitoring and knowledge transfer.
- Typical deliverables
- Prioritised use-case roadmap, scoped pilot plan, working pilot system evaluated on your data, governance and model-risk documentation, production and handover plan.
- Hosting options
- Cloud, or local hosting from a 32 GB i7 box computer to 128 GB unified-memory GPU systems where data must stay in-house.
- Where it fits less well
- Organisations without usable data or a repeated decision to improve usually get more value from the AI courses in Hong Kong first, or from a short readiness assessment rather than a build.
- How to enquire
- director@aikm.ai or the enquiry form; most projects start with a scoping call.
AI strategy & readiness
We assess where AI creates real value in your organisation, map it to your data and processes, and produce a prioritised roadmap that leadership can act on. Covers use-case selection, build-vs-buy, platform choices, and organisational readiness.
Enterprise RAG & knowledge systems
Design and build retrieval-augmented generation systems on your own documents: chunking and embedding strategy for bilingual (English/中文) corpora, hybrid retrieval, evaluation, and production deployment with citation fidelity your teams can trust.
Agentic AI deployment
Move beyond chat: multi-step AI agents that use your tools, query your systems, and produce finished work under human oversight. We handle task decomposition, tool integration (MCP and custom), reliability engineering and approval workflows.
AI governance for regulated business
Frameworks for deploying AI under Hong Kong PDPO, cross-border data rules, and international AI regulation. Model risk assessment, audit trails for agentic systems, and bilingual policy documentation designed in at the start.
Getting Found by AI Systems
Enterprise visibility across three layers: SEO to rank in traditional search, AEO (Answer Engine Optimization) to become the direct answer in featured snippets, voice search and AI assistants, and GEO (Generative Engine Optimization) to be cited inside ChatGPT, Perplexity and Gemini responses. We deliver question-led content, FAQ and HowTo schema, original data and expert quotes, author E-E-A-T bios, and citations on trusted sites so your organisation is both ranked and named by the AI.
Setting up an in-house AI research lab
We help Hong Kong enterprises stand up a lightweight AI research lab empowered by AI tools, typically 2-3 people plus outsourced technical support, whose remit is to model your own domain knowledge (pricing logic, operational heuristics, client risk profiles) into fine-tuned models, RAG systems and agent workflows. Large parts of the first-year cost can be covered by BUD and ITF funding.
Choosing an approach
RAG, knowledge graphs or AI agents: which fits your problem
| Approach | Best when | Main trade-off | Read more |
|---|---|---|---|
| Retrieval-augmented generation (RAG) | Answers must come from your own manuals, policies or contracts, with citations. | Cheapest to run and easiest to audit, but reasons over passages rather than relationships. | Four enterprise RAG patterns |
| Enterprise knowledge graphs | The domain is relationship-heavy: org structures, products, suppliers, regulations. | Higher modelling and maintenance effort, in return for answers that reason across links. | Knowledge graphs for enterprise AI |
| Agentic AI | Work is genuinely multi-step: research, cross-source synthesis, tool use under human approval. | Most capable and most expensive; needs evaluation, audit trails and approval workflows. | Governance for agentic AI |
Most Hong Kong deployments start with hybrid RAG and add graph structure or agents only where the problem demands it. For how to size the business case beforehand, see our guide to measuring AI knowledge-management ROI, the glossary of enterprise AI terms, and worked enterprise AI case studies.
Engagement stages
Discovery, pilot and production at a glance
| Stage | Typical length | What happens | You receive |
|---|---|---|---|
| Discovery | 2–4 weeks | Assessment of data, workflows, constraints and governance obligations. | Prioritised use-case roadmap and a scoped pilot plan. |
| Pilot | 6–12 weeks | Build and evaluate a working system against real data. | Evaluated pilot, measured results and a production decision. |
| Production | Rollout and handover | Deployment with evaluation, monitoring and knowledge transfer to your team. | Running system, monitoring setup and documentation for your staff. |
Why an AI research lab
Every Hong Kong company should have an AI research lab
It sounds like a slogan, but Hong Kong's operating environment makes it a practical necessity. High rents and payroll compress margins across property, retail, logistics, insurance and trading; IT talent is scarce; and off-the-shelf SaaS rarely fits PDPO obligations, trilingual (Cantonese, English, Mandarin) workflows, or Greater Bay Area supply chains. Singapore mid-caps almost all run an AI task force already, and a two- to three-year lag shows up in gross margin.
The core purpose of an AI research lab is not to publish papers. It is to model your own business: a trading company's declaration logic, a restaurant group's table-turn forecasting, a brokerage's client risk preferences. That domain knowledge is the firm's most valuable asset, but it usually lives in the founder's head, in Excel sheets and in WhatsApp threads, and external vendors cannot extract it for you.
High-ROI scenarios by industry
Cantonese customer-service bots, dynamic pricing, food-waste forecasting.
Email auto-routing and quote generation, container-slot optimisation.
Automated compliance-document review (PDPO / SFC), claims triage.
NLP classification of maintenance tickets, key-clause extraction from tenancy contracts.
Cross-jurisdiction (HK + Mainland) contract comparison, tax memo drafting.
The lightweight model
An AI research lab in Hong Kong can be very light
Forget DeepMind. A Hong Kong-scale AI research lab is typically 1-2 people: one PM who understands the business, and one Python / LLM API engineer (which can be outsourced). The goal is not to invent new models, but to evaluate existing ones, connect your own data, run a proof of concept, and embed it into an existing workflow. Government funding covers most of year one through the BUD Fund and the Innovation and Technology Fund (ITF) enterprise support scheme.
The cost of doing nothing
What happens in three to five years
A competitor's agents finish in one afternoon what your five staff take a week to do, quoting three days faster and around 15% cheaper. Ten years of client data still sit in your ERP unmodelled, so on exit your valuation is a step lower (acquirers are already asking about data assets). Post-95 and post-00 hires arrive, find no AI workflow, and leave. Put strictly, not every firm should have an AI lab, but every firm with meaningful data volume and repeated decision logic should have a lightweight AI research unit: one that researches the business, not the AI.
How we engage
Discovery, pilot, production
Most engagements start with a two to four week discovery: we assess your data, workflows and constraints, and return with a scoped pilot plan. Pilots typically run 6 to 12 weeks against real data. Production rollouts include evaluation, monitoring and knowledge transfer to your team.
We offer solutions ranging from a conventional 32G memory i7 small box computer, to a 128G unified GPU Nvidia Spark, to a 128G unified memory AMD AI Max+, alongside other local hosting options — so the right-sized infrastructure is matched to each stage of your pilot and production rollout.
Who advises you
Your AI consultant and advisory bench
Engagements are led by Dr Hoson H.S. Lam, AIKm's Chairman & Chief Scientist and lead AI consultant in Hong Kong. Technical reviews on retrieval, agentic AI and evaluation draw on AIKm's Fellow Members, Prof Kenneth Lam and Dr W.M. Tsang, both named on Stanford University's list of the world's top 2% most-cited scientists. Governance and delivery are reviewed by AIKm's board advisors: Prof John Mok, Prof Wilton Chau and Prof. Dr. Kelvin WAN (HBR Advisory Board · AI Topleader). See the full profiles on the advisory board page.
Discuss a projectSee case studiesTeam trainingGuide: measuring AI KM ROI
Engagement models
Our Engagement Engines
AIKm delivers advisory and capability building through complementary engines: one-to-one executive advisory, in-house corporate programmes, and long-term partnerships that stand up an AI research lab inside your organisation. Pick the engine that matches how your organisation wants to move.
| Offering | Duration & Team Size | Fee (HKD) | What's Included |
|---|---|---|---|
| Engine 1 — Executive AI Advisory (One-to-One) | |||
| AI Readiness Sprint | ~10 hours | HK$30,000–40,000 | AI opportunity map, governance & PDPO baseline, tool-stack recommendation. |
| AI Implementation Sprint | ~20 hours | HK$50,000–60,000 | Everything in the Readiness Sprint plus a working RAG/agent prototype built on your own documents. |
| Hourly advisory | Flexible | HK$2,000–3,500 / hour | Personalised AI strategy, governance, and hands-on coaching. |
| Engine 2 — Corporate Capability Programmes (In-House) | |||
| Capability Programme + Deliverables | 15–21 hours · up to ~20 staff | HK$130,000–180,000 | Training fused with a working prototype on your data, a governance playbook, and 5 bundled advisory hours. |
| Standard In-House Programme | 15–21 hours · up to ~20 staff | HK$90,000–160,000 | Customised training for your industry, tools, and data-governance requirements. |
| Small-Team Programme | ~10 hours · up to 10 staff | HK$60,000–80,000 | Focused entry-level engagement for smaller teams. |
| Engine 3 — Long-term Partnership (AI Research Lab Set-up) | |||
| AI Research Lab Set-up | 6–12 months · dedicated lab team | Scoped per project | Lab team stand-up, first fine-tuned model, RAG pilot, governance framework; BUD Fund / ITF funding-eligible. |
All fees in HKD. Delivered in English, Cantonese, or Mandarin, on-site or online.
Governance work aligns with the Hong Kong Personal Data (Privacy) Ordinance (PDPO) and international standards including ISO/IEC 42001. AIKm operates from Hong Kong Science & Technology Parks (HKSTP).
Frequently asked questions
AI consulting in Hong Kong: FAQ
What does an AI consultant in Hong Kong actually deliver?
AIKm's AI consultants in Hong Kong deliver an assessed roadmap, a working pilot on your data, and a production deployment plan. Typical outputs include a use-case shortlist, a scoped 6-12 week pilot, evaluated retrieval or agent systems, and a governance framework aligned with Hong Kong PDPO and cross-border data rules.
How long does a typical AIKm consulting engagement take?
Discovery runs 2-4 weeks. Pilots on real data run 6-12 weeks. Production rollout with monitoring, evaluation and team handover usually takes a further 8-16 weeks, depending on integration surface and governance requirements.
Does AIKm work with regulated industries in Hong Kong?
Yes. AIKm advises on AI deployment under the Hong Kong Personal Data (Privacy) Ordinance (PDPO), cross-border data transfer rules, and international AI regulation, and builds audit trails and model-risk assessments into agentic AI systems from the start.
Can AIKm build RAG or agentic AI on our own documents?
Yes. AIKm designs and builds enterprise RAG and agentic AI systems on client-owned document corpora, including bilingual English and Chinese content, with hybrid retrieval, evaluation and citation fidelity, and integrates with your existing tools via MCP or custom connectors.
Should we build RAG, an enterprise knowledge graph, or AI agents?
It depends on the problem. Retrieval-augmented generation (RAG) fits when answers must come from your own manuals, policies or contracts with citations; it is the cheapest to run and the easiest to audit. An enterprise knowledge graph fits relationship-heavy domains such as org structures, products, suppliers and regulations, at the cost of higher modelling and maintenance effort. Agentic AI fits genuinely multi-step work — research, cross-source synthesis and tool use under human approval — and needs evaluation, audit trails and approval workflows. Most Hong Kong deployments start with hybrid RAG and add graph structure or agents only where the problem demands it.
Which industries in Hong Kong get the most value from enterprise AI consulting?
Organisations with meaningful data volume and repeated decision logic: retail and F&B (Cantonese customer-service bots, dynamic pricing, food-waste forecasting), logistics and freight (email auto-routing, quote generation, container-slot optimisation), finance and insurance (PDPO and SFC compliance-document review, claims triage), property and facilities (maintenance-ticket classification, tenancy-clause extraction) and professional services (cross-jurisdiction contract comparison, tax memo drafting).
Can our data stay in-house rather than in the cloud?
Yes. AIKm supports local hosting options ranging from a conventional 32GB memory i7 small box computer to a 128GB unified GPU Nvidia Spark or a 128GB unified memory AMD AI Max+, so the infrastructure is sized to each stage of the pilot and production rollout while data remains on your premises.
How do I engage AIKm's consulting practice?
Contact AIKm at director@aikm.ai, or use the enquiry form at https://aikm.ai/contact. Most engagements begin with a scoping call and a short written brief before the discovery phase.
What are AEO and GEO (Getting Found by AI Systems), and how are they different from SEO?
SEO earns rankings in traditional search results. AEO (Answer Engine Optimization) makes your content the single direct answer in featured snippets, voice search and AI assistants. GEO (Generative Engine Optimization) gets your organisation cited inside generative AI answers from ChatGPT, Perplexity and Gemini. AIKm helps enterprises win all three by combining structured data (FAQ, HowTo), question-led writing, original data and expert quotes, and authority signals (E-E-A-T, author bios, trusted citations).