Built by people who understand how supply chains actually fail
We founded Deepfield with a simple observation: most supply chain problems aren't data problems — they're decision-making problems. AI can help, if applied thoughtfully.
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How Deepfield came to be
Deepfield was established in Kuala Lumpur by a group of engineers and operations specialists who spent years working inside Malaysian manufacturing and logistics businesses. What they kept encountering was the same pattern: companies had data, but the decisions made from that data were still largely intuitive, slow, and reactive.
The gap wasn't technology — it was application. Existing AI tools were either too generic for supply chain specifics, or required infrastructure that smaller Malaysian operators couldn't sustain. Deepfield was built to fill that space: practical AI implementations, designed for the operational realities of Malaysian business.
Today, we work alongside manufacturers, distributors, and logistics providers across Peninsular and East Malaysia — helping them turn supply chain data into sharper, more coordinated decisions.
Our Mission
To help Malaysian businesses make faster, better-informed supply chain decisions — by building AI systems that fit how they actually operate.
Our Approach
We don't sell platforms or subscription tools. We engage directly with your team to understand your specific constraints, then build AI models tailored to your workflow.
Our Values
Transparency in what AI can and can't do. Respect for your team's operational knowledge. Steady, methodical progress over rushed deployments.
The People
Our core team
Ahmad Razif
Co-Founder & Lead Engineer
Former supply chain systems architect with deep experience in ERP integration and predictive modelling for manufacturing environments across Malaysia.
Nurul Liyana
Co-Founder & Operations Director
Operations management specialist who spent over a decade coordinating distribution networks for FMCG and electronics supply chains in Southeast Asia.
Kevin Heng
Head of Data Science
Applied ML researcher focused on demand forecasting and geospatial optimisation, with prior work in logistics data science at regional freight operators.
How We Work
Quality standards & protocols
Data Security
Client data is handled under signed data processing agreements. We use encrypted transfers and access-controlled environments throughout every engagement.
Model Validation
Every AI model we deliver is validated against held-out datasets before deployment. We document performance metrics and communicate limitations clearly.
PDPA Compliance
Our data handling practices are aligned with Malaysia's Personal Data Protection Act 2010. We advise clients on their own PDPA obligations where relevant.
Version Control
All model development follows structured version control. Clients receive documentation of model versions, training configurations, and deployed artefacts.
Transparent Reporting
Progress reports throughout the engagement. We document what works, what doesn't, and why — so your team fully understands the systems we deliver.
Post-Deployment Support
Ongoing availability after go-live to monitor model performance, manage data drift, and address operational questions as your environment evolves.
Expertise & Context
AI supply chain work in the Malaysian context
Malaysian supply chains carry specific structural characteristics that affect how AI models need to be built. Distribution networks span both Peninsular and East Malaysia, introducing logistical complexity that generic routing tools don't address well. Manufacturing clusters in Selangor, Penang, and Johor operate within distinct sub-supplier ecosystems. Port dependencies through Klang and Penang add variability that needs to be modelled explicitly.
Deepfield's team has worked directly inside these environments — not as consultants observing from a distance, but as people who have managed inventory positions, optimised delivery windows, and dealt with the consequences when forecasts fall short. That operational background informs every model we design.
Supply chain AI works best when the people building it understand both the mathematics and the operational reality. Demand sensing models need to account for Malaysian public holidays, festive demand patterns, and supplier lead times that vary by region. Route optimisation for last-mile delivery in KL requires understanding of traffic patterns, restricted delivery zones, and customer time window constraints. Visibility platforms need to surface information that operations managers can act on — not just data for its own sake.
We work with manufacturers managing complex product portfolios, logistics operators coordinating multi-hub distribution, and businesses in the process of digitalising their supply chain data infrastructure. Our engagements are structured, bounded, and focused on producing working systems rather than recommendations.
Interested in working with Deepfield?
We're happy to have an initial conversation about your supply chain and where AI might add genuine value.
Reach Out