From AI Strategy to Production - Built for Asia Pacific Businesses

Yes. We offer AI discovery engagements where we assess your data assets, business context, and identify high-value AI use cases. Typical engagement: 2-4 weeks resulting in a prioritized roadmap with feasibility assessment, expected business impact, and estimated effort per use case. This is often the first engagement before committing to full AI development.

AI project costs vary widely based on complexity, data readiness, and production requirements. Proof-of-concept engagements typically range from USD 30,000-80,000. Full AI development projects with production deployment range from USD 100,000-500,000. Pricing depends heavily on data work required — projects with clean, labeled data are significantly faster and lower-cost than projects requiring extensive data preparation. We provide detailed estimates after the discovery phase.

Sometimes yes, sometimes no — and we'll tell you honestly. For some use cases (especially LLM-based approaches), large datasets aren't required. For others (custom classifiers, computer vision), data volume is critical. During discovery, we assess your data situation and recommend approaches that match your data reality, including techniques like transfer learning, few-shot learning, or LLM-based methods that work with smaller datasets.

Traditional ML builds custom models trained on your specific data — better for narrow, well-defined problems with sufficient training data. LLM-based approaches use pre-trained foundation models (GPT-4, Claude) augmented with your data through techniques like RAG or fine-tuning — better for tasks involving language understanding, content generation, or scenarios with limited training data. Many modern AI projects combine both. We recommend the approach during the feasibility phase.

Our development practices align with IMDA's framework across the core principles: transparency, explainability, fairness, accountability, and human oversight. For each AI project, we document model decisions, conduct bias testing, design human-in-the-loop controls where appropriate, and maintain data lineage. We also stay updated on the new Agentic AI Framework (January 2026) for projects involving autonomous AI agents. Our compliance documentation is available for clients who need to demonstrate AI governance to their own stakeholders or regulators.

Data privacy is a primary concern in AI work. We follow PDPA-aligned practices including data minimization (using only necessary data), anonymization techniques where appropriate, secure development environments accessed only via VPN, and contractual safeguards for cross-border data transfer. For sensitive use cases (healthcare, financial data), we implement additional measures including on-premise deployment options, federated learning approaches, and dedicated infrastructure. Your data is never used to train models for other clients.

Production AI requires ongoing care. After deployment, we set up monitoring for model drift (when real-world data diverges from training data), performance degradation, and unexpected behaviors. Most clients engage us for ongoing MLOps support — monitoring, periodic retraining, model improvements as new data accumulates, and infrastructure optimization. Without this, AI models typically degrade within 6-12 months of deployment.

This is a real risk in AI projects and we address it upfront. During the feasibility phase (Phase 1 of our methodology), we test whether AI is the right approach before significant investment. During the build phase (Phase 2), we run POCs against your success criteria before committing to production. If results don't meet criteria at any decision point, we present options: try alternative approaches, adjust success criteria, supplement with rule-based logic, or recommend stopping the project. Our 3-phase methodology is designed to fail fast and cheap, not late and expensive.