Built for the hard parts of AI deployment
Neuralith was founded in Singapore in 2019 by a team with backgrounds in machine learning research, distributed systems engineering, and enterprise technology strategy. What brought the founding team together was a shared frustration: most AI engagements produced models that degraded silently in production, or infrastructure decisions made without a coherent understanding of long-term implications.
The name reflects the firm's philosophy — neural, for the domain, and lithic, meaning stone, for the kind of foundational thinking we bring to each engagement. We are not here to be fast or to be fashionable. We are here to be right in ways that hold up over time.
Our work is concentrated in three areas: AI observability and monitoring, graph-structured machine learning, and enterprise AI platform selection. Each of these sits at a meaningful juncture — where AI touches operations, where structure in data shapes outcomes, and where organisational choices about tooling have long shadows. These are problems we find genuinely interesting and where we believe careful engagement makes a substantive difference.
We work primarily with mid-to-large organisations in Singapore across financial services, logistics, and technology. Our clients tend to already have data capabilities and want to deepen or stabilise their AI investments, not start from scratch.
Founded
2019
Clients Served
60+
Location
Robinson 77, Singapore
Focus Areas
- AI Observability & Monitoring
- Graph Neural Networks
- Enterprise AI Platform Setup
The people behind Neuralith
Rohan Krishnamurthy
Co-Founder & Principal
Former ML research engineer with ten years of experience in model lifecycle management and production ML systems. Leads client engagements in observability and monitoring.
Serene Loh
Co-Founder & Graph AI Lead
Specialist in graph-structured learning with a doctorate from NTU. Has designed GNN architectures for fraud detection and supply chain analysis across Southeast Asia.
Jonathan Tan
Platform Strategy Lead
Enterprise technology strategist with deep experience in AI/ML platform evaluation and deployment. Previously led AI infrastructure at a regional bank for seven years.
Quality standards we apply to every engagement
Data Protection & PDPA Alignment
Every engagement begins with a mutual NDA. Data access is scoped to what is necessary. We design solutions that align with Singapore's PDPA requirements and your internal governance obligations.
Structured Requirements Analysis
We do not begin design before we understand context. Every project starts with documented requirements, stakeholder mapping, and a clear definition of what good looks like for the organisation.
Reproducible, Documented Work
All deliverables include documentation that your team can use without us. Monitoring playbooks, runbooks, architecture decision records — work that does not depend on continued access to Neuralith.
Vendor-Neutral Advisory
We hold no commercial relationships with AI platform vendors. Recommendations are based on fit with your organisation's situation, not on what generates referral fees or commercial benefit for us.
Team Knowledge Transfer
We build with your team, not beside them. Internal capability development is a deliberate part of each engagement so that the work compounds for your organisation after we leave.
Defined Scope & Honest Timelines
Timelines are agreed before work begins and scoped conservatively. If something changes, we flag it early. We do not inflate scope and we do not make commitments we cannot stand behind.
What guides our work
AI implementation in Singapore operates at a particular confluence — sophisticated data infrastructure, regulatory expectations shaped by MAS and PDPC, and organisations at varying stages of maturity in how they think about AI governance. Our work accounts for all of these dimensions, not only the technical ones.
We hold the view that most AI failures are not model failures. They are monitoring failures, infrastructure mismatch failures, or organisational misalignment failures. That is why the three areas we focus on sit deliberately at the boundary between technical work and organisational practice.
Precision in how we scope, structure, and deliver work is not a stylistic choice — it is how we make sure the work holds up. Engagements with Neuralith are designed to be the kind of AI investment you make once and build on, rather than something you revisit after a year of poor visibility into what is actually happening.
Want to discuss a
specific AI challenge?
Reach out. We respond within one business day and all initial conversations are strictly confidential.
Contact Neuralith