What our clients say
Feedback from our clients
These are edited versions of feedback we have received, shared with permission.
"We came in not knowing much about AI. The Neurova team took time to understand our actual problem before suggesting anything. The chatbot they built handles about 60% of our support queries now, and our team was trained to update it themselves."
"The ML Ops engagement gave our engineering team structure they didn't have. We had models sitting unused because deploying them felt too risky. After six weeks, we had a pipeline we trusted. Wei Liang was particularly helpful in explaining why each decision was made."
"We were sceptical about whether AI was actually relevant to our business. The Innovation Sprint gave us a clear answer — yes, in one specific area, with a working prototype to show the board. That was exactly what we needed before committing to more."
"What I appreciated most was that they pushed back on our initial brief. They told us our framing was too broad, helped us narrow it to something achievable, and delivered on that. That kind of honesty is rare in vendors."
"Small team, but they move quickly and communicate clearly. The weekly updates were short and useful. I never felt I had to chase them for information. Would use them again for the next phase."
"Our HR team had no technical background and I was worried they'd struggle to work with an AI system. Priya from Neurova made sure every step was explained to them. The handover session was genuinely useful — not just a walkthrough of slides."
A closer look at three engagements
Automating customer support for a logistics operator
The situation
A mid-sized logistics company in Selangor was receiving hundreds of identical support queries daily — shipment status, delivery windows, invoice queries. Their small customer service team was spending most of their time answering questions that had the same five answers.
What we did and what changed
We built a chatbot integrated with their tracking system and trained on six months of historical queries. After six weeks, it was handling around 60% of incoming queries without human intervention. The team redirected their time to complex cases requiring judgement.
Stabilising ML deployment for a fintech platform
The situation
A fintech startup had built a credit scoring model that worked well in testing but was unreliable in production. Models were being updated manually, with no monitoring in place. When performance dropped, they often didn't know until a business stakeholder noticed.
What we did and what changed
We introduced automated deployment pipelines, model versioning, a monitoring dashboard with drift alerts, and a retraining schedule. The engineering team now operates the system with confidence. Three months later, they reported catching and correcting a performance issue independently, without our involvement.
Testing AI feasibility for a manufacturing firm
The situation
A Penang-based manufacturer wanted to explore whether AI could help predict equipment maintenance needs before failures occurred. They had sensor data going back two years but weren't sure if it was the right kind of data or whether the problem was solvable at their scale.
What we did and what changed
The Innovation Sprint confirmed the data was suitable and produced a working prototype with 73% accuracy on historical data. We recommended a clear next step: a six-week engagement to bring it to production. The client presented the prototype to their board and received budget approval for the next phase.
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