Why Telvar exists
Telvar was established in Singapore to address a straightforward problem: many mid-sized businesses collect data but have no practical way to extract analytical value from it. The forecasting tools that large enterprises use require data science teams that most organisations cannot afford to maintain.
Our approach is structured and scoped. We arrive with a specific set of questions, work through your data with care, and deliver a written output — not a sprawling dashboard that requires interpretation. The findings are yours to use, share with stakeholders, and act on without needing to come back to us first.
We focus on three services where we believe careful modelling genuinely shifts how a team thinks and plans. Everything we do stays within that scope.
What we are here to do
Clarity, not complexity
Every output we produce is written to be understood without a statistics background. The model logic is explained. The uncertainty is stated.
Collaborative working style
We treat your domain knowledge as an input to the model, not an obstacle. Your team's understanding of context almost always improves the output.
Singapore market knowledge
We understand the commercial rhythms of Singapore — public holidays, MAS reporting cycles, port logistics patterns, and regional procurement seasonality.
People behind the work
A small team with complementary skills across applied modelling, data engineering, and client delivery.
Wei Lin Ng
Eight years building demand models for retail, logistics, and B2B service businesses across Southeast Asia. Focused on translating model outputs into planning-ready language.
Rohan Krishnamurthy
Specialises in assessing and improving data pipelines for mid-sized organisations. Leads all Readiness Check engagements and the data preparation phase of modelling projects.
Cheryl Lim
Manages client relationships from initial scoping through to delivery and follow-up. Ensures the questions an engagement is built around are the right ones before any modelling begins.
Standards we hold ourselves to
These are not certificates on a wall — they describe how each engagement is actually run.
Data Confidentiality
Client data is used only for the commissioned engagement. We sign NDAs on request and do not retain data beyond project closure. Storage follows Singapore PDPA requirements.
Written Deliverables
Every engagement ends with a structured written report. Findings are stated in plain language. Uncertainty ranges are documented. The logic behind each conclusion is visible.
Transparent Methodology
We explain which methods we use and why. If a model does not fit your data well, we say so. Presenting an inaccurate model as reliable would harm your planning more than help it.
Scoped Timelines
All three services have defined timelines that we discuss and agree before work begins. Scope changes are discussed openly. We do not extend projects without mutual agreement.
Knowledge Transfer
Where models are handed over, we include a walkthrough session so your team can interpret outputs and run updates independently — reducing reliance on repeated external engagements.
PDPA Compliance
All data handling follows Singapore's Personal Data Protection Act. We advise clients when their data includes personal information that requires anonymisation before analysis.
Analytics work grounded in Singapore's commercial environment
Singapore's position as a trading hub generates data patterns that differ from larger markets. Demand cycles are shaped by port activity, MAS reporting periods, public sector procurement timelines, and the calendars of neighbouring markets in Malaysia, Indonesia, and Thailand.
Telvar builds calibration into every model we develop — accounting for these regional factors rather than applying generic time-series techniques that miss the texture of how Singapore businesses operate. This is most visible in our demand forecasting work, where seasonal features drawn from local commercial context consistently outperform standard date-based variables.
For customer behaviour analysis, the multilingual and multicultural composition of Singapore's customer base often creates meaningful segmentation patterns that a market-agnostic model would overlook. Working with teams who understand their own customers' profiles is part of how we surface those distinctions.
Want to know if we are the right fit?
A short initial call usually answers that. No commitment, no formal pitch — just a conversation about what your data situation looks like and whether any of our services fit it.
Get in Touch