What organisations in Hong Kong say about working with us
Honest feedback from people who have been through scoping, configuration, and delivery with our team.
Back to Home40+
Organisations engaged
4.7/5
Average satisfaction score
6+
Years delivering AI projects
72%
Clients return for further work
What our clients say
Helena Kwok
Head of Content, Publishing
We publish across English, Traditional Chinese, and Simplified Chinese. Before working with Catalect, the translation bottleneck was genuinely slowing our editorial calendar. Their team took the time to understand our terminology — which is specific to education publishing — and the output quality was noticeably higher than what we had tried with generic tools.
January 2026
Raymond Liu
Operations Director, Logistics
The predictive maintenance system they built has changed how our fleet operations team works. We used to find out about vehicle problems when drivers called in. Now we get alerts 3–5 days in advance on average, which has reduced our emergency repair spend considerably. The calibration process took longer than I expected, but the result is that the alerts are actually useful — we do not ignore them.
December 2025
Sophia Chan
General Manager, Professional Services
We attended the Use Case Discovery workshop not knowing what to expect. By the end of the day we had a clear, prioritised list of six AI opportunities — with honest assessments of which were realistic to act on now versus later. The workshop leader was candid about limitations, which I found more useful than enthusiasm. We have since proceeded with one of the recommendations.
January 2026
Kenneth Lau
IT Manager, Manufacturing
Working with Catalect on integrating their maintenance system into our existing SCADA setup was more straightforward than I anticipated. Their team engaged directly with our IT staff, asked the right technical questions, and produced documentation that was genuinely useful. The 60-day support period gave us confidence during the initial months of live operation.
December 2025
Anna Ng
Marketing Director, Retail
Our product catalogue needed to be available in three languages simultaneously, which was creating real delays in our go-to-market process. The translation service Catalect configured now handles first-pass translation for the bulk of our content, which our editors then review for tone. Time-to-market on new product ranges has improved noticeably.
January 2026
James Wong
COO, Financial Services
We were sceptical about the discovery workshop concept — it sounded like a day that would produce a document no one would act on. That was not the experience. The facilitator pushed us to think concretely about implementation constraints and data availability, which made the outputs far more grounded than I expected. We walked away with two projects we are now actively scoping.
December 2025
Three projects in detail
Multilingual Content Operations — Publishing Firm
Challenge
A mid-sized educational publisher needed to produce English, Traditional Chinese, and Simplified Chinese versions of all content simultaneously. Manual translation created a 2–3 week lag that delayed product launches.
Solution
We configured an AI translation model fine-tuned on their educational vocabulary and integrated it into their CMS workflow. An editorial review step was retained for final approval, sitting after the AI output.
Results
Content lag reduced from 2–3 weeks to 3–4 days. Editorial review time decreased as reviewers were working with good quality first drafts rather than rough machine output. Two language coordinators redeployed to higher-value work.
Timeline: 5 weeks from scoping to integration
Fleet Maintenance Intelligence — Logistics Operator
Challenge
A logistics operator with 140 commercial vehicles was experiencing frequent unplanned breakdowns, carrying high emergency repair costs and disrupting client delivery commitments.
Solution
We built a predictive maintenance model trained on 18 months of maintenance records, OBD sensor data, and mileage logs. A dashboard for the fleet operations team surfaced alerts with severity levels and recommended maintenance windows.
Results
Emergency repair incidents reduced by approximately 40% in the first six months. Average alert lead time of 4 days allowed maintenance to be scheduled during low-demand periods. Fleet uptime improved measurably.
Timeline: 9 weeks from data assessment to live dashboard
AI Opportunity Mapping — Professional Services Firm
Challenge
A professional services firm knew AI was relevant to their sector but lacked a structured way to identify where to begin. Past discussions had produced broad ambitions with no actionable starting point.
Solution
We facilitated a Use Case Discovery workshop with nine participants across operations, IT, and management. Using our scoring framework, the team identified and ranked 11 potential AI applications across document processing, client communication, and internal reporting.
Results
Delivered a ranked report with effort and impact estimates for each use case. Three applications were identified as feasible within 12 months. The firm proceeded to commission detailed scoping for their top-ranked opportunity in the following quarter.
Timeline: 1 day workshop + 5 days to report delivery
Professional grounding
HKICT Awards Commendation
SME Innovation category, 2023
HKCS Member
Hong Kong Computer Society
PDPO Compliant
Personal Data (Privacy) Ordinance
Wan Chai District Business Member
Active participant since 2019
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