AI & Machine Learning Company in Hong Kong

A scoped AI proof of concept at Hong Kong rates runs HKD 320,000 to HKD 720,000 (roughly USD $40,000 to $92,000) over six to ten weeks, covering data audit, a baseline model, and a hosted demo with Cantonese and English UI if relevant. A custom production ML model (fraud scoring, claims triage, churn prediction, KYC document extraction) typically lands at HKD 950,000 to HKD 2,350,000 (around USD $120,000 to $300,000) including MLOps, monitoring, and HKMA or SFC-aligned model documentation. Full production AI systems with RAG, multiple models, fine-tuning, and enterprise integrations range from HKD 1,950,000 to HKD 9,400,000 (USD $250,000 to $1.2M). Hong Kong rates sit above Singapore for financial services AI because of the HKMA examination intensity and the cross-border China-aware compliance overhead. The HKD is pegged to USD at 7.75 to 7.85 so dollar conversions stay stable across the engagement. We give fixed-fee proposals in HKD rather than open T and M estimates.

The HKMA issued its Generative AI guidance for authorised institutions in 2024, building on its earlier 2019 High-Level Principles on AI. The guidance covers governance, risk management, data quality, fair treatment of customers, model explainability, third-party reliance, and incident management for generative AI specifically. Banks must classify GenAI use cases by risk, maintain board-level oversight on high-risk deployments, document data lineage, ensure human-in-the-loop control for customer-facing decisions, and report material incidents. Our banking AI engagements ship with HKMA-aligned classification, model cards, bias and fairness reviews, prompt injection defences, evaluation harnesses that run before every production push, and incident response playbooks that map directly to the HKMA's expectations. HKMA examiners look for evidence of structured governance and challenger testing, not slideware, and our deliverables reflect that lived experience.

Yes, and this is a genuinely tricky problem most vendors handle badly. Cantonese is not Mandarin written down. Hong Kong written Cantonese mixes traditional Chinese characters, Cantonese-specific characters that do not exist in Mandarin, English code-switching, and a register that is closer to spoken speech than mainland written Mandarin. Models trained on Simplified Chinese Mandarin (the bulk of available Chinese training data) miss the dialect, the cultural references, and the politeness conventions Hong Kong users expect. We build Cantonese evaluation harnesses with native HK speakers, fine-tune on Cantonese-specific corpora when off-the-shelf models fall short, route Mandarin and Cantonese queries through separate pipelines when accuracy matters, and ship Traditional Chinese UI with the right local idioms. For HK products that serve Greater Bay Area customers we ship Simplified Chinese flows in parallel with proper cross-border data routing through the Standard Contract framework.

Cross-border data movement between Hong Kong and mainland China sits under the Personal Information Protection Law, the Data Security Law, and the Cyberspace Administration of China's cross-border transfer regime. For most Hong Kong-to-mainland flows of personal information we execute the Standard Contract for Cross-Border Transfer of Personal Information and file it with the provincial CAC office before any data moves. For Greater Bay Area pilots there is a streamlined data flow framework under the GBA inclusion guidance that some Hong Kong financial institutions can use with lower overhead, but eligibility is workload-specific. We coordinate with mainland counsel, route training and inference through compliant region pairs (ap-east-1 plus AWS Beijing or Ningxia, or Azure East Asia plus Azure China 21Vianet), and document the lawful basis for every transfer. We will tell you up front if a use case is better served by keeping mainland data inside mainland infrastructure entirely rather than risking a cross-border misstep.

When a project requires genuine research depth (novel architectures, rare-event detection, specialised computer vision, low-resource Cantonese NLP) we scope collaborations with HKUST, HKU, CUHK, or Hong Kong AI Lab affiliated researchers rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Hong Kong AI projects actually stall after the academic prototype. For standard work (RAG, fine-tuning, classical ML, computer vision on known architectures, banking risk models) no academic partner is needed. We will tell you up front which bucket your problem fits into, and we coordinate with Cyberport and HKSTP's AI cohort companies when our clients need adjacent capabilities (vision sensors, robotics integration, edge inference hardware) that sit outside our scope.

We default to AWS ap-east-1 (Hong Kong), Azure East Asia (Hong Kong), and GCP asia-east2 (Hong Kong) for storage, training, and inference. For LLMs that must stay in Hong Kong (HKMA-regulated banking, HKIA-regulated insurance, SFC-regulated asset management with sensitive client data) we use Azure OpenAI in East Asia, Anthropic via Bedrock ap-east-1, or self-host Llama 3, Mistral, and Qwen on Hong Kong GPU instances. For Greater Bay Area workloads that need mainland inference, we extend into Azure China (21Vianet), AWS Beijing or Ningxia (Sinnet and NWCD), and Alibaba Cloud regions with the Standard Contract for Cross-Border Transfer of Personal Information executed. Cross-border to the US or EU is acceptable only when a PCPD impact assessment and (where applicable) HKMA outsourcing notification sign off. We document residency in the model card and the data processing agreement so PCPD, HKMA, and SFC reviewers have a clear answer.

A typical Hong Kong banking or insurance model (fraud scoring, AML transaction monitoring, claims triage, underwriting, KYC document extraction) takes fourteen to twenty-two weeks from kickoff to production, assuming clean historical data and HKMA Generative AI or HKIA-aligned documentation as part of scope. Week 1 to 4 is data audit, PCPD impact assessment, and baseline model. Week 5 to 12 is modelling, iteration, challenger testing, and bilingual evaluation if Cantonese is in scope. Week 13 to 18 is MLOps, monitoring, shadow mode deployment, prompt injection testing if GenAI is used, and internal audit review. Week 19 onward is gradual rollout, often staged by branch or product line. If you are pre-data or need labelling (especially Cantonese annotation), add six to ten weeks. We have shipped to this cadence inside HKMA-examined and HKIA-regulated stacks.

SenseTime remains HKEX-listed and Hong Kong-headquartered, but US Entity List designations on the parent and on specific subsidiaries mean that procurement at multinational banks, insurers, and global asset managers in Hong Kong now routinely screens vendor stacks for sanctioned-party exposure. We are sanctions-aware by default. We avoid SenseTime, Megvii, iFlytek, and other Entity-Listed components in workloads built for US-touching counterparties, document the provenance of every model, dataset, and library in a bill-of-materials suitable for procurement review, and route compute through cloud regions with clear export-control posture. For Hong Kong-domestic or mainland-facing workloads where the client explicitly accepts the procurement profile, we can use mainland model providers (Qwen, Yi from 01.AI, Baichuan, DeepSeek) under documented governance. We will tell you up front which constraints apply and structure the architecture around them rather than discovering the issue at procurement.