Singapore's #1 AI Development & Consultancy Partner

We build custom machine learning models, generative AI integrations (using GPT-4, Claude, Gemini, and open-source LLMs), AI-powered automation pipelines, natural language processing systems, predictive analytics models, computer vision applications, and MLOps infrastructure for production deployment. Our in-house engineering team — not outsourced contractors — handles everything from proof of concept through to production deployment and ongoing model maintenance.

Both, depending on what's appropriate for your use case. Many business problems are better solved by building a custom pipeline around an existing LLM (ChatGPT, Claude, Gemini) than training a model from scratch. We assess your requirements, data availability, and budget to recommend the right approach — custom ML where differentiation matters, existing APIs where speed and cost efficiency are the priority.

AI consultancy defines the strategy — identifying which business problems AI can solve, assessing data readiness, mapping the technology architecture, and building the business case and roadmap. AI development executes that strategy — building, training, testing, and deploying the actual models and systems. We offer both, which means the strategy we recommend is grounded in what's actually buildable and deployable at your scale.

A focused proof of concept — demonstrating whether an AI approach works for a specific problem — typically takes four to eight weeks. A production-ready system, with MLOps infrastructure, monitoring, and integration into existing workflows, typically takes three to six months. Timeline depends on data availability, integration complexity, and the accuracy threshold required. We're transparent about scope and timeline before any development begins.

 Yes. Many of our clients start with no data pipelines, no ML infrastructure, and limited internal AI expertise. We assess your current data environment, identify what's needed to make AI viable, and build the foundational infrastructure before any model development begins. We also run internal capability-building workshops so your team understands how to work with AI systems after we deliver them.

We implement MLOps infrastructure for model versioning, monitoring, retraining triggers, and performance drift detection — because production readiness requires more than a working model. Security practices include data anonymisation, access controls, API security, and compliance with relevant data protection regulations (PDPA in Singapore, GDPR where applicable). We don't hand over a prototype and call it done — deployment and monitoring are part of every engagement scope.

We start with a scoping workshop — defining the problem, assessing your data, and validating that AI is actually the right approach (not every problem needs ML). From there: proof of concept (four to eight weeks), production development, testing, and deployment. You receive full documentation, model monitoring dashboards, and a team handover session. We don't deliver black boxes.

We've built AI systems across financial services (risk modelling, fraud detection, document processing), marketing and media (content intelligence, audience modelling, campaign optimisation), logistics (demand forecasting, route optimisation), healthcare (patient data analysis, operational automation), and SaaS (product recommendation engines, churn prediction, usage analytics). Most use cases involve automating processes, extracting intelligence from unstructured data, or predicting outcomes. AI Rudder, a Singapore AI SaaS company, is a strong example: custom AI-led systems contributed to a 795% increase in qualified leads and helped them close their USD $50M Series B.