QuantiSage

Buying an AI build in Singapore turns less on the model than on data readiness, IP ownership, PDPA accountability, and who keeps the system running after handover. Use this checklist before you shortlist.

How to evaluate an AI consultancy in Singapore

• Separate the proof-of-concept from the production build in the contract: time-box the POC with written numeric success criteria, and quote the hardened, monitored production system as a distinct scope.
• Ask for two shipped production case studies in Singapore — systems running on live data with users, not demos or POCs — and confirm they were not quietly retired after launch.
• Pin model and IP ownership in writing: who owns the trained weights, the training pipeline, the labelled dataset, and any fine-tuned derivatives, and whether the vendor may reuse them for other clients.
• Confirm PDPA handling for any personal data used in training — how it is sourced, minimised, and deleted on request — and ask how they document evaluation and human-in-the-loop controls against the IMDA/PDPC Model AI Governance Framework and AI Verify.
• Check vendor and model neutrality: ask whether they are tied to one cloud or foundation-model provider, and get unit pricing for ongoing MLOps, retraining, and incident response alongside a fixed-fee build SOW.
• Make knowledge transfer and post-handover maintenance explicit: who maintains the solution, what documentation and runbooks you receive, and whether your team can operate it without the vendor.

Verify for QuantiSage

• Confirm key details directly with the vendor — this listing isn't vendor-managed yet.
• Ask for two recent Singapore client references you can speak with.
• Ask for a written scope of services before comparing quotes.
• Request evidence of relevant certifications and their current validity.

Questions to ask

• Can you share two Singapore production systems — live and on real data — that I can speak to a reference about?
• Who owns the trained model, pipeline, and labelled data when we finish, and can you reuse any of it for other clients?
• How do you measure accuracy and hallucination, and where does a human review output before it reaches a customer or a recorded decision?
• After handover, who maintains and retrains the system, and what knowledge transfer and documentation do we receive?