AI that works
in the real world.

Start with one repetitive, measurable workflow that causes real friction, such as document entry, quotation follow-up, quality inspection or management reporting. Prove value with a short prototype, then address integration, permissions and human review.

Do not compare models or demos alone. Ask how existing workflows connect, where data comes from, who handles exceptions, how success is measured and who maintains the system after launch. Delivery depends on operational understanding, integration and continuous improvement.

Yes. The approach depends on APIs, database access, file exports or IoT gateways. The hard part is usually reliable data flow and exception handling—not the model alone.

Discovery and feasibility can often be completed in weeks, with a prototype focused on one clear use case. Production timing depends on data quality, integration, security and user testing. Define stage outcomes instead of committing to a large platform upfront.

Consider it when data is sensitive, latency matters, connectivity is limited or direct control is required. Compare cost, maintenance, performance and risk before deciding.

Define purpose and required data, minimize personal data, and set access, retention, supplier responsibilities and human review. Seek professional advice against current Hong Kong privacy requirements.

Record a baseline before launch—processing time, error rate, missed cases, conversion or review time. Compare the same metrics after deployment and include maintenance and human-review costs before scaling.