What Organisations Say
After Working With Us
We share feedback from clients as it was given — including what we did well and where there was room to improve. Honest reflection matters to us as much as it does to the people we work with.
Back to Home40+
Organisations engaged
4.7/5
Average satisfaction score
3+
Years in Singapore market
78%
Clients who return for a second engagement
Client Perspectives
Feedback from organisations across Singapore that have completed Synthara engagements.
"The readiness consultation gave us something we had not been able to get from vendor meetings — an honest view of what we actually needed to fix before AI investment would make sense. The report was clear and direct, and it saved us from a decision we were close to making prematurely."
Tan Chee Wei
Head of Operations · Logistics Firm, Singapore
February 2025
"We came in expecting a six-week engagement and it ran closer to nine due to some complications with our legacy camera setup. The team was transparent about the delays and the reasons — no surprises on cost, and the end result was a working prototype that our production team understood how to use."
Ranjit Lakshmanan
Plant Manager · Food Manufacturing, Jurong
January 2025
"The decision support system has changed how our analysts prepare for morning briefings. Instead of compiling data manually, they're reviewing the system's suggestions and deciding whether they agree. That shift in workflow was not something we anticipated but it has been genuinely valuable."
Airina Sulaiman
VP Analytics · Financial Services, Raffles Place
December 2024
"We had an expectation that the consultation would validate our existing plan. It did not — and that was the right outcome. The assessment found two gaps we had underestimated. Addressing them before investing further was the correct call, and we appreciated the team being willing to deliver that message."
Goh Nai Leng
CTO · Professional Services Firm, CBD
January 2025
"The documentation they delivered with the computer vision project was better than anything I have received from a technology partner before. Our engineering team reviewed it and could follow the logic without needing us to explain. That is unusual and it has made maintenance significantly more manageable."
Nadia Pradeep
Head of Engineering · MedTech, one-north
February 2025
"Three months of support after deployment turned out to matter more than we expected. We had two operational edge cases in the first month that required model adjustments. Both were handled quickly and without additional fees. I would not have felt comfortable going live without that safety net in place."
David Lim
COO · Insurance Brokerage, Tanjong Pagar
January 2025
Engagement Snapshots
How specific situations developed through our engagements.
The Situation
A mid-sized logistics company was considering investing in an AI-based route optimisation system after seeing competitors discuss it publicly. Their leadership wanted to understand whether their data infrastructure could support the initiative before committing budget.
Our Approach
The readiness consultation reviewed three years of historical route and delivery data, the company's data collection practices, and the team's capacity to maintain an AI system post-deployment. Two sessions with operations and IT leads, followed by a third with the executive team.
Outcome
The report identified that data quality in two depot locations was insufficient for reliable model training. The company addressed these gaps over four months before proceeding. When they returned for a computer vision engagement six months later, the underlying data was in significantly better shape.
The Situation
A food manufacturer in Jurong Industrial Estate ran manual quality inspection for packaged goods — a process that was both time-intensive and inconsistently executed across shifts. The operations manager wanted to understand whether visual AI could help.
Our Approach
The engagement began with requirements gathering across inspection stations, reviewing existing camera positions and image quality. A proof-of-concept model was developed for three defect categories using labelled images from the client's own production line.
Outcome
The proof of concept reached 91% accuracy on the three target defect categories using existing camera infrastructure. The deployment recommendation included specific hardware adjustments to two cameras to improve performance in lower-light shift conditions. The client proceeded to full deployment based on these findings.
The Situation
An insurance brokerage needed to improve the speed and consistency of underwriting recommendations for commercial clients. Analysts were spending significant time compiling risk indicators from multiple internal systems before making recommendations.
Our Approach
The engagement involved integrating data from three existing platforms into a unified advisory layer. The model was trained on four years of historical recommendations and outcomes, with the interface designed around how analysts actually made decisions — not how management thought they did.
Outcome
Analysts reported reducing average recommendation preparation time from approximately 45 minutes to 18 minutes. Two model adjustments were made during the three-month refinement period to better handle an edge case in marine cargo policies. The system has been in production use since late 2024.
Contact Synthara
Reach out to discuss your organisation's context before committing to any engagement.
Phone
+65 6281 4637Office
8 Shenton Way, #36-01
Singapore 068811
Hours
Mon–Fri 9:00 AM – 6:00 PM SGT
Professional Recognition
PDPA Compliant
All client data practices aligned with Singapore's Personal Data Protection Act
IMDA AI Governance
Aligned with IMDA's Model AI Governance Framework for responsible deployment
AISG Member
Active participation in Singapore's AI Singapore ecosystem and knowledge network
2024 SME Innovator
Recognised by Singapore Business Review for responsible AI advisory practice
See what a Synthara engagement looks like for your organisation
The readiness consultation is a good place to start — and it stands on its own regardless of what comes next.