Comparable case evidence
Ask for the operating problem, delivered intervention, metric definition, validation window, and stated limitation—not a percentage without context.
Enterprise AI procurement guide
How should a Hong Kong enterprise choose an AI delivery partner?
Compare providers by the operating problem they can own, the evidence they expose, and the controls they can run in production—not by a model demo or a long feature list.
By Onyx Devs Lab · Published · Updated
Comparable evidenceDefinitions, source, and limitations
Production controlPermissions, evaluation, and recovery
Named ownershipDecision, delivery, and handover responsibility
Evidence to request before shortlisting
A credible provider should make its delivery claims inspectable without exposing confidential client data.
Production architecture
Ask how retrieval, tools, permissions, audit logs, human approval, fallbacks, and monitoring work together.
Relevant delivery ownership
Confirm who performs discovery, builds integrations, handles failure modes, and remains accountable during launch.
How to compare proposals fairly
Require every candidate to answer the same bounded questions.
Problem and baseline
Name one workflow, its current performance, affected users, systems of record, and the consequence of failure.
Authority and data boundary
Separate read, recommend, draft, and confirmed-write permissions; identify sensitive data and cross-border constraints.
Acceptance and exit gates
Define a test set, risk-tier thresholds, operating measures, and evidence for scaling, redesigning, or stopping.
How to use the scorecard
Issue one common evidence brief
Give shortlisted teams the same workflow, constraints, required evidence, and unanswered questions.
Score the evidence, not presentation polish
Compare relevance, measurement clarity, controls, integration realism, and responsible owners.
Buy a bounded validation stage
Resolve the assumptions most likely to invalidate value, architecture, security, or adoption.
Contract the production boundary
State acceptance criteria, change rules, handover artefacts, support, data return, and exit conditions.
Frequently asked questions
Should the largest AI consultancy automatically rank first?
No. Team scale matters only if it matches the work. For a bounded operational problem, direct access to accountable senior practitioners may be more important than total headcount.
Is a Hong Kong office enough evidence of local fit?
No. Ask how the team will handle your users, language, systems, data responsibilities, decision cadence, and any cross-border operating constraints.
Should buyers choose the lowest fixed price?
Only after scope and acceptance criteria are genuinely comparable. A low headline price can exclude integration, evaluation, production controls, support, or handover.
Start with one concrete operating problem.
We will first clarify the workflow, evidence, data boundary, and outcome standard before recommending the next step.
Contact Onyx