Client Experiences
Novarc Labs · Testimonials

What clients say about working with us

A selection of feedback from research engagements across Singapore, alongside case studies that describe the work and what was achieved.

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01 — Client Feedback

From recent research engagements

"The literature review we commissioned gave us a genuinely current picture of NLP approaches in our domain. The annotated bibliography alone has been referenced by our internal team multiple times since delivery. It was structured with us as the audience, not a generic reader."

LT

Li Ting Ng

Head of AI Strategy, fintech firm

February 2026

"We needed someone to help us frame a research question that had been sitting unresolved for months. The methodology advisory sessions were specific and structured — we came out with a document we could actually use. The session notes were detailed and honest about our constraints."

RK

Roshan Kumar

Research Lead, government tech agency

January 2026

"What I valued most was that they told us early on what the data environment made difficult. A lot of firms would have pushed ahead and delivered something that looked complete but was weaker for it. Novarc Labs flagged the issue in week two and we redesigned the scope together."

HW

Hui Wen Loh

VP Research, regional healthcare group

February 2026

"The technology landscape report informed our AI procurement decision in a way that would have taken our internal team months to replicate. The executive summary was genuinely readable for our board — something harder to achieve than it sounds."

AY

Adrian Yeo

CTO, Singapore logistics company

January 2026

"We worked with Novarc Labs on a research partnership covering a genuinely novel ML evaluation problem. The methodological documentation they produced has since been used as a reference point for two internal projects. It holds up well under scrutiny."

SM

Selina Mah

Director of R&D, enterprise software firm

December 2025

"The scoping conversation itself was useful — it helped us identify that our original research question was actually two distinct questions, one more tractable than the other. We narrowed the scope and got a much stronger result. That kind of thinking is what you pay for."

TN

Thanh Nguyen

Principal Scientist, research institute

February 2026

02 — Case Studies

Three engagements, described in detail

Case Study 01 · Literature Review · December 2025

AI document classification in a regulated sector

CHALLENGE

A Singapore financial services organisation needed to understand current AI-based document classification approaches before a technology investment. Internal knowledge was limited and the procurement timeline was tight.

APPROACH

Structured literature review covering transformer-based classification, performance in low-resource settings, and limitations in regulated data environments. Delivered in four weeks with an annotated bibliography of 38 sources.

OUTCOME

The report directly shaped the vendor evaluation framework used in the subsequent RFP. Three of five evaluation criteria were drawn from the limitations section. Procurement was completed within the original timeline.

Case Study 02 · Research Partnership · Oct–Dec 2025

Evaluation methodology for an AI-assisted triage system

CHALLENGE

A healthcare technology team needed a rigorous evaluation framework before clinical deployment of their AI triage tool. Standard metrics were insufficient, and internal AI evaluation expertise was limited.

APPROACH

Eight-week research partnership covering evaluation literature for high-stakes AI, development of a context-specific framework, and a pilot evaluation plan aligned to deployment constraints and available data.

OUTCOME

The framework was adopted as the internal standard for AI system assessment across the organisation. The pilot evaluation identified one significant performance gap before clinical deployment. The report was reviewed by the ethics board.

Case Study 03 · Methodology Advisory · January 2026

Research design for an internal NLP benchmarking study

CHALLENGE

A government AI team wanted to benchmark NLP models against their specific use cases but were uncertain how to design the study to produce valid, comparable results.

APPROACH

Three advisory sessions covering research question formulation, benchmark dataset design, evaluation metric selection, and controls for validity threats. Written notes after each session, followed by a methodology document.

OUTCOME

The team completed the benchmarking study using the methodology document as their primary reference. The study informed a model selection decision and was included as supporting documentation in a budget proposal for further AI capability investment.

03 — By the Numbers

What the work adds up to

80+ Completed research engagements
6+ Years of applied AI research support
4.7 Average satisfaction score (out of 5)
3 Core specialist team members
04 — Contact

Ready to discuss your research question?

The conversation is informal and without commitment. We will give you an honest view of whether and how we can help.

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5 Research Link, #02-07
Singapore 117610

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Sat: 10:00 am–1:00 pm SGT