AI & Machine Learning Company in Singapore

A scoped AI proof of concept at Singapore rates runs S$55,000 to S$130,000 over six to ten weeks, covering data audit, a baseline model, and a hosted demo with an initial AI Verify run. A custom production ML model (fraud scoring, churn prediction, recommendation, document extraction) typically lands at S$170,000 to S$420,000 including MLOps, monitoring, AI Verify reporting, and a MAS Veritas-aligned model risk file. Full production AI systems with RAG, multiple models, fine-tuning, and enterprise integrations range from S$350,000 to S$1.6M. Singapore rates sit above Kuala Lumpur and Jakarta because of the MAS-regulated talent premium and EDB incentive economics, but below Sydney and Tokyo at equivalent quality. Eligible portions may qualify for IMDA, EDB, or Enterprise Singapore co-funding. We give fixed-fee proposals rather than open T and M estimates.

AI Verify is IMDA and the Foundation for AI Verify’s open-source AI governance testing framework, the first of its kind globally and the de facto Singapore standard for trustworthy AI. It combines a process checklist against eleven internationally aligned principles (transparency, explainability, repeatability, safety, security, robustness, fairness, data governance, accountability, human agency, inclusive growth) with technical tests for fairness, robustness, and explainability run against your actual model. Our Singapore engagements integrate AI Verify into CI so the report is regenerated on every material release, and we ship the artefacts in a form that MAS, IMDA, SNDGO, and HSA reviewers accept. For generative AI, we also align with the AI Verify Project Moonshot evaluation toolkit for LLM red-teaming and benchmarking.

Yes. We ship RAG systems, internal copilots, contact-centre assistants, and document extraction pipelines for DBS, OCBC, UOB-style stacks and for digital banks under MAS supervision. For MAS-regulated clients we stay inside ap-southeast-1 or Azure Southeast Asia, apply MAS TRMG and Notice 655 controls, run MAS Veritas fairness and accountability assessments, and produce the challenger model documentation internal model risk needs. We have patterned deployments after MAS Veritas published case studies from DBS, UOB, and HSBC Singapore, including strict PII redaction, prompt injection defences, human-in-the-loop review gates, and evaluation harnesses that run before every production push. Output logs feed existing SIEM, model risk, and MAS Notice 655 reporting pipelines.

Every Codazz Singapore engagement opens with a PDPA Data Protection Impact Assessment scoped against the Personal Data Protection Act 2012 as substantially amended in 2020 and 2021, including the new mandatory data breach notification regime, deemed consent by notification, business improvement and research exceptions, and the enhanced financial penalty cap. We minimise personal data in training sets, document consent and lawful basis under the PDPA, implement automated decision-making safeguards where the model materially influences individuals, and build deletion flows that respect the PDPA access and correction obligations. Training data, vector stores, and inference logs default to ap-southeast-1. Cross-border transfers to other ASEAN regions, India, or Europe are documented against the PDPA transfer limitation obligation with contractual and binding corporate rules instruments.

When a project requires genuine research, such as low-resource Southeast Asian NLP across Bahasa Indonesia, Thai, Vietnamese, Tagalog, Tamil, and Singlish, maritime or satellite computer vision, or reinforcement learning at production scale, we scope collaborations with AI Singapore (the national programme at NUS), NUS School of Computing, NTU SCSE, SMU School of Computing and Information Systems, or SUTD’s AI faculty rather than overselling in-house science capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Singapore AI projects actually stall after a strong research prototype. For standard work (RAG, fine-tuning, classical ML, computer vision on known architectures) no academic partner is needed. Where AI Singapore’s 100E or LearnAI programmes, EDB incentives, or Enterprise Singapore grants are in scope, we structure the engagement to fit the eligible cost categories and audit trail those programmes require.

We default to AWS ap-southeast-1 (Singapore), one of AWS’s global flagship regions, as the primary region for storage, training, and inference. Azure Southeast Asia (Singapore) is the alternative for Microsoft-aligned banks and government agencies, and Google Cloud asia-southeast1 (Jurong West) covers GCP clients. For LLMs that must stay onshore under MAS outsourcing rules, HSA medical device data rules, or Government Commercial Cloud (GCC) policies, we self-host SEA-LION, Llama 3, Mistral, or Qwen on regional GPU instances, or use Anthropic Claude and OpenAI through Bedrock and Azure Southeast Asia. Cross-border to ap-southeast-3 (Jakarta), ap-southeast-7 (Bangkok), ap-south-1 (Mumbai), or ap-northeast-1 (Tokyo) is acceptable only when the PDPA transfer impact assessment and MAS outsourcing notification (where applicable) sign off, documented in the data processing agreement.

A typical Singapore bank or fintech model (fraud scoring, AML transaction monitoring under the MAS 626 AML/CFT framework, credit decisioning, KYC document extraction) takes fourteen to twenty-four weeks from kickoff to production, assuming clean historical data and MAS Veritas plus MAS TRMG documentation as part of scope. Week 1 to 4 is data audit and baseline. Week 5 to 12 is modelling, iteration, and challenger testing. Week 13 to 18 is MLOps, monitoring, shadow-mode deployment, and internal model risk review. Week 19 to 22 is the AI Verify report, MAS Veritas fairness assessment, and TRMG outsourcing notification where applicable. Week 23 onward is gradual rollout. If you are pre-data or need labelling, add six to eight weeks. We have shipped to this cadence inside MAS-regulated stacks on AWS ap-southeast-1 and Azure Southeast Asia.

Most custom AI work qualifies under one or more Singapore co-funding instruments. EDB’s Pioneer Certificate and Development and Expansion Incentive can offer concessionary corporate tax rates for substantive AI investment, Enterprise Singapore’s Enterprise Development Grant supports up to 50 percent of qualifying project costs for SMEs, IMDA runs Grow Digital and the AI Apprenticeship Programme, and AI Singapore’s 100 Experiments (100E) co-funds applied AI research at S$250,000 per project for qualifying problem statements. National AI Strategy 2.0 has earmarked S$1B over five years, materially expanding available envelopes. We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned with these programmes’ audit requirements. Final eligibility sits with your grant consultant and EDB, IMDA, Enterprise Singapore, or AI Singapore.