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The Future of Software 

At AgileAlgo, we believe the use of Artificial Intelligence will transform the way systems will be delivered and operated. Deeptech will be embedded in the design, implementation, testing, and operations of software. We believe generativeAI can help systems create other systems. AI create AI. We continue to apply these deeptech to ourselves. We are committed to help businesses significantly change the way software is developed. This is the future of software.

Please meet our Virtual System Implementer Team

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What do we do?

We help organisations create smart functions for your transactional systems. We understand that currently, implementing Artificial Intelligence (AI) can be expensive and may takes weeks to months to deliver. We aim to provide the AI use cases in hours or days using a generativeAI approach. Deliver small AI use cases where previously you will have thought that it is too costly to apply; every widget of your transaction systems can use AI.

We have created an AutoAI platform that use AI to create AI. We use written language to get to AI codes quickly. Call this the “ChatGPT” for language to AI codes with precision. We call this the Virtual Data Scientist Platform. The platform provides Data Engineering, AutoML and AIOps functionalities through written requirements. All good projects start with requirements, that’s what we start with.

We specialised in Graph Neural Networks for inference/prediction and support most other AI Algorithms including Big Data Analytics, NLP, ComputerVision and Search. We can create inference engines in 5 minutes! We can automatically trace your requirement to code. We machine learn the requirements to code to enable continuous improvement to the AI development capability.

We are cost efficient as you are self helping. We will translate that cost saving back to you. We will significantly change the way you get your AI software.


We are AgileAlgo. We are the future of software.

Our advantages

Create inference engine in 5 mins

Our platform support almost instant engine creation for your use case

Graph neural network focus

Latest technique, lesser data requirement than bigdata analytics

Automatic requirements tracing

Integrated requirements to code capability support full tracing

 Rapid smart end point deployment

  Integrated autodeployment of codes to compute

Easy to use

Compared to other autoML platforms, we use written requirements as our starting point

How does it work?

GenerativeAI using Agile style of development. Using epics and user stories to drive code development. Users need not understand coding but to define requirements.

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World's First Virtual Data Science Team!

 Can AI create AI? Can we mimic human analytical capability? At AgileAlgo, we are pushing the limit. We have created the world first Virtual Data Science Team. End Users will use a platform to translate their requirements to AI Code and it deploy automatically taking away the need for data engineering and data science resources. We use our propriety requirements to AI code engine to do that. We can support Graph Neural Networks and more. Find out more!


for AI

GenerativeAI has been the buzz word since ChatGPT came into scene in recent times. This concept is not new to AgileAlgo. We have been working on our Language Driven Development capability and offer our platform for clients to rapidly get from natural language to AI codes. We have created AI codes from requirements in hours and if not days especially in the area of everydayAI. We also explored other tools in this space and we believe we are still unique in our approach for generativeAI. We are happy to share an article on GenerativeAI for from natural language to code with precision.

Language Driven Development

At AgileAlgo, we strive to provide End User Organisation or System Integrators to code generate through written requirements. We call it Language Driven Development. All system project starts with requirements, to be exact, a software requirement specification. The requirements describe what the software will do and how it will be expected to perform. That is our start point. For this platform, we use an Agile style of requirement writing. Requirements are called User Stories. A user story is usually written from the user's perspective and follows the format: “As [a user persona], I want [to perform this action] so that [I can accomplish this goal].” User stories must be easy for anyone to understand. They also represent bite-sized deliverables that can fit into an overall outcome called an Epic. In Agile, the quality of the user story can be evaluated through a set of criteria. The acronym INVEST represent the need to be Independent, Negotiable, Valuable, Estimate-able, Small, and Testable. Essentially the user story needs to be implementable. This form the basis of how our platform ingest requirements, interpret them and intelligently match it with the required codes. It is expected that not all codes are available and we need to over-time build the first set and feed back the system to learn and reuse subsequently.

Graph Ontology

An Ontology is a set of concepts or categories within one subject matter or domain that show properties of entities as well as their relationships to one another. In the case of Graph Neural Networks, ontologies allow data to be semantic. In computer and information science, ontology is a technical term denoting an artifact that is designed for a purpose, which is to enable the modelling of knowledge about some domain. Artificial intelligence has retained the most attention regarding ontology in subfields like natural language processing within machine translation and knowledge representation. An ontology language is a formal language used to encode an ontology. There are a number of such languages for ontologies, both proprietary and standards-based. For VSI, we use the Web Ontology Language (OWL) for our Ontology design and input. The OWL format is a family of knowledge representation languages for authoring ontologies. Tools like ( ) and/or WebVOWL ( ) are available to document the ontology design of your domain.

Graph Neural Networks

Neural networks reflect the behaviour of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Neural Networks are essentially a part of Deep Learning, which in turn is a subset of Machine Learning. So, Neural Networks are considered highly advanced application of Machine Learning that are now finding applications in many fields of interest. Graph Neural Network is a type of Neural Network which directly operates on the Graph structure. They are designed to perform inference on data described by graphs. They can learn and model non-linear and complex relationships, which is important because in real-life, many of the relationships between inputs and outputs are non-linear as well as complex. Graph Networks have been applied at ultra-large Social Media, Search & eCommerce companies. The most prevalent application is at Google within their search engines where they have applied graph technology to associate information available on the internet. eCommerce Portal ranging from Decathlon and Shopee utilised such technology too. Currently the technology is only available to such ultra-large companies through their propriety software and not the general scale companies but as technology and tools becomes more prevalent and open, this can now be brought to the masses.

Meet the Team
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Tony Tay

Founder and CEO

Tony has 27 years of IT Industry experience with many years in Accenture and held the role of Managing Director, Health and Public Service, Technology Delivery Lead Singapore. Prior to Accenture, Tony was the Consulting Director for IDA International and the Chief Information Officer of Kim Eng Securities. Tony is currently serving on the Executive Council for the Singapore Computer Society

Jonathan Ang

Co - Founder &

Data Scientist

Jonathan Ang is the Knowledge Graph Data Scientist and co-founder of AgileAlgo. He started his Mathematical journey from a young age, showing an interest in anything analytical, both inside and outside the classroom. This interest led him to pursue higher education in Mathematics, earning a Bachelor of Arts and Master’s in Mathematics from the University of Cambridge in 2020. Previously, he worked at Singapore University of Technology (SUTD) as a Senior Research Assistant, before joining forces with AgileAlgo.


Francis Lee

Chief Operating Officer &

Chief Commercial Officer

With 25 years of Asia-wide enterprise sales and general management experience, Francis leads business development and commercialization for AgileAlgo. Francis was formerly Managing Director of Emerging Markets with SAP Southeast Asia, and later co-founded North Consulting Group with Tokyo-listed Future Corporation, Inc., where he served as CEO and Board Member until the firm exited to Big-4 firms around the region.

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