Business-first AI
We start with a measurable business KPI, then decide if AI is the right tool. Sometimes the honest answer is a simpler workflow.
About Lorong AI
We exist to make AI work for businesses that can't afford to get it wrong
Great AI outcomes don't come from algorithms alone. They come from people who understand boardroom pressure and last-mile operational reality in Singapore.
Lorong AI began after our founders saw too many black-box recommendations arrive without a clear owner, budget, or path into daily work. We bring AI strategy, data analytics, machine learning, and governance into one practical conversation.
Built in Singapore. Used across Southeast Asia.
Clear decisions first. Technical delivery follows.
6+
years helping teams put AI into daily operations
200+
organisations supported across Southeast Asia
12
ASEAN cities reached through client work
Our working principles
How we show up every day
AI consulting works when the people affected by a system can understand it, question it, and use it with confidence. These principles shape every recommendation and delivery sprint.
Radical transparency
Teams see the assumptions, limits, data gaps, and expected operating cost before a model reaches production.
Inclusive by design
We test decisions with the people who use the process every day, including frontline staff whose context rarely appears in a dashboard.
Lifelong learning
Our AI strategists, data analytics specialists, and machine learning advisors keep learning through delivery, review, and responsible AI practice.
The people behind the work
Guided by experience you can trust
Our 30+ specialists have worked in Big 4 consulting, regional banking, and health-tech scale-ups. They know that a useful model must survive a Monday morning handover.
Xuedun Pitriani
Founder and Chief ExecutiveXuedun founded Lorong AI after leading complex change programmes for Singapore and regional organisations. She keeps the first conversation close to the business problem, the people involved, and the decision that needs to improve.
Ankor Adriatico
Head of AI StrategyAnkor has advised leadership teams on AI readiness, operating models, and investment choices across ASEAN. He turns broad ambition into an AI charter with accountable owners and a sensible sequence of work.
Jenalyn Kawa
Head of Data and Machine LearningJenalyn leads data product and machine learning delivery for teams moving beyond pilots. Her focus is practical: trustworthy data, clear model monitoring, and tools that internal teams can maintain.
Ely Muruga
Ethics and Governance LeadEly helps organisations build review gates around privacy, bias, explainability, and accountability. Ely has contributed to responsible AI working groups and keeps governance connected to real operating decisions.
"If a team can't explain what a model is doing, the model isn't ready to run the business."
Xuedun Pitriani, Founder and Chief Executive
How we deliver
The Think-Build-Scale methodology, in detail
Every engagement has a visible decision point. You know what has been tested, what remains uncertain, and what the next investment should achieve.
Think clearly before you spend
We map the decision, audit readiness, speak with process owners, and write an AI charter. A regional retailer might use this phase to test whether demand signals are reliable enough for replenishment planning.
AuditData, systems, skills, and risk review.
AlignBusiness KPI and accountable owner.
DecidePrioritised roadmap with investment gates.
Build something people can test
Small MVP sprints turn the charter into evidence. Data scientists work beside domain experts, with ethics gates covering privacy, fairness, explainability, and human override before a recommendation is trusted.
PrepareClean, document, and permission data.
TestCompare model output with current practice.
ReviewRecord risks, trade-offs, and user feedback.
Scale with ownership in place
When the evidence is strong, we set up MLOps, monitoring, training, and governance playbooks. The handover includes the operating rhythm that keeps a model useful after the project team leaves.
OperateProduction controls and model monitoring.
GovernReview cadence, records, and escalation paths.
LearnMeasure business outcomes and improve safely.
Evidence and practice
Accreditations and partnerships that matter
We keep our external commitments specific. They inform how we assess systems and how we help clients prepare for responsible adoption.
IMDA AI Verify
Our teams stay involved with AI Verify discussions and responsible AI working groups in Singapore.
AWS Partner Network
Cloud delivery conversations include architecture, access controls, data handling, and the cost of running models.
PDPA compliance practice
Privacy considerations are addressed at the start of discovery, before data is selected for a use case.
Singapore Business Review
Our work and perspective have been recognised by Singapore Business Review's business audience.
Let's discuss how our team can accelerate yours
The founder often joins the first conversation. Bring the business decision, the current data picture, or the concern you need to resolve.