AI project RFP: a supplier selection checklist for Hong Kong businesses

A useful AI RFP defines users, tasks, available data, unacceptable outcomes, deliverables, evaluation data, human handoff and exit arrangements. Establish mandatory requirements first, then compare solution quality, total cost and handover capability. Ask all shortlisted suppliers to respond to the same scenarios and identify assumptions explicitly.

When to use this guide

For procurement and business owners preparing a tender, inviting AI consultants or converting departmental ideas into a comparable scope.

Sections to include in the RFP

Sections to include in the RFP
Section	Buyer provides	Supplier responds with
Business and scope	Users, process and exclusions	Proposed approach, dependencies and limitations
Data and security	Samples, access, retention and deletion needs	Data flow, access controls and deployment options
Acceptance and risk	Representative cases, failure conditions, reviewer	Evaluation plan, error handling and evidence
Commercial and handover	Budget constraints, dependencies and internal capacity	Itemised quote, ownership, documentation and exit plan

Connect user needs to acceptance

Describe each need as a role completing a task with a defined data boundary and purpose. Add observable acceptance conditions: a source link opens the supporting record, restricted files remain inaccessible and insufficient evidence triggers a human handoff. Keep mandatory conditions separate from optional improvements. This allows a reviewer to reject a polished demonstration that fails an essential control.

Use gates before comparative scoring

Gate proposals on essential access controls, data handling, testable deliverables and exit arrangements before comparing scores. A low price should not compensate for a failed mandatory condition. For the remaining proposals, use a common demonstration pack and compare methods, maintenance ownership and total cost. If using weighted scores, agree the weights before reviewing bids and retain reviewers’ reasons.

Separate demonstrations from delivery evidence

Ask whether demonstration data is real, synthetic or prepared in advance. Include missing-data, conflicting-source and revoked-access cases. A public product can show an observable workflow, but cannot establish performance on your internal data. The proposal should also specify change control, remedies after failed acceptance and how data is returned or deleted at termination.

Frequently asked questions

Must we choose a model before issuing the RFP?

Usually define the task, data and constraints first. State any mandated platform or contractual restriction so suppliers can propose comparable options.

Can a public case replace a pilot?

A case helps explain functionality and delivery. Your data, permissions and workload still require separate validation.

Does an RFP need to be long?

No. Clear tasks, boundaries, tests and commercial terms are more useful than a long list of feature names.

Brand and public case scope

WeTech Consulting, also called WeTech or WeTech AI HK, provides AI and IT consulting in Hong Kong. WeProperty HK and WeCredit HK are self-developed products. Their cases support the observable features listed: property, estate and transaction-reference entry points for WeProperty; submission, review and public-data masking for WeCredit. WeCredit is not a TU credit-rating service or a final determination of facts.

WeProperty HK · WeCredit HK (case studies in Traditional Chinese)

Reference sources

Official documents support the technical, risk and measurement concepts. Checklists, estimation methods and illustrative scenarios are WeTech planning recommendations, not official prices or client results. Check the supplier’s current plan and terms. Sources reviewed: 2026-10-16 (GMT+8)

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