01Environmental technology
Water Doctor Pte Ltd
Embedded engineering across operational research, reviewed reporting, business development and editorial workflows.
Strategic technology partnership
Read the company storyAI Forward Deployed Engineering
From traditional work
to AI-native
operations.
Your business, your people, your systems.
An engineering team that connects them.
We help companies make AI part of how work gets done: process design, training, software development, Agentic Crews and the operating practices around them.
Since 2008. Business software experience behind today's AI-native work.
Implemented in three company environments
Experience across
real businesses.
AI-native processes and workflows are implemented in all three companies. Each story explains its business context, the work and our relationship.
02Fashion commerce · Indonesia
PT Hanafashion Indonesia
AI-native processes and workflows in the business behind Hanafashion, Bigins.ID and Eraya.
Related operating business
Read the company story
03Brand, content & commerce
Aright Tech Pte Ltd
AI-native working across brand, portal and content operations, with documented editorial and creative-production workflows.
Related business and platform work
Read the company storyCompany experiences include an external technology partnership and related operating businesses. Each case has its own context and scope.
The tools behind the practice
Daily AI practice.
Deliberate controls.
Desktop tools, command-line agents and private AI are parts of a working engineering environment. We build the operating discipline around them.
Tools, agentic teams & private AICodex and Claude, used in real work. We work across desktop and CLI environments, including Claude Code. Agentic teams divide research, implementation and review into clear tasks, with defined handoffs and a person responsible for the result.
Memory, context and token management. Useful information needs an owner and a scope. We organise reusable instructions and memory, select relevant context, set token budgets and test whether the workflow still performs when its information changes.
Data, security and private AI. We scope what agents can read, which tools they can call and where information may be sent. For suitable private workloads, we also deploy and evaluate locally hosted models, including work with NVIDIA DGX Spark. Model choice, network access and the surrounding applications determine the actual data boundary.
A business capability, developed together
More than a tool.
A way of working.
AI-native readiness connects leadership, staff capability, information, software and daily operation. An effective programme addresses those relationships.
Business & process
Understand the operating model, choose priorities and define how responsibilities and decisions should change.
People & capability
Connect training and practical learning to the work leaders, operators and technical owners will actually do.
Systems & development
Build applications, integrate existing tools and develop Agentic Crews where specialist responsibilities help.
Operation & improvement
Evaluate real tasks, assign operational ownership and improve the capability as the business and its information change.
The role of AI FDE
Close to the work.
Accountable for
the engineering.
A forward deployed engineer works with the people who know the business. Discovery continues into implementation: the team learns the exceptions, connects the relevant systems and develops the workflows around real operating constraints.
Codex and Claude are the tools we use most often in our desktop and CLI workflows. Our engineers use them for implementation, investigation and review, and coordinate agentic teams for work that benefits from specialist roles. Engineers remain responsible for architecture, testing and release. We manage the memory, context, token budgets and permissions around that work.
The first release can focus on one important process. The broader programme builds the capabilities needed to connect and extend that work across the company.
See how delivery worksPeople are part of the implementation
Build the system.
Develop the team.
Leaders choose priorities and decision boundaries. Operators learn to review output, correct mistakes and handle exceptions. Technical owners learn the components they will maintain.
Training, adoption and handover need their own deliverables. We scope them around the responsibilities your people will take, with engineering support assigned where your team needs it.
Training & adoptionExplore the practice
GUIDE / 01
What an AI FDE actually does
The role, the engineering and the decisions inside an engagement.
GUIDE / 02A practical readiness roadmap
Sources, systems, permissions and useful evidence before expansion.
GUIDE / 03Learning through deployment
Make training part of the work your team will operate.
wGrow · Since 2008
Bring the business.
Let's shape what comes next.
Tell us about your people, systems and operating priorities. We can discuss the right starting point and a scoped path towards AI-native working.
Discuss your business
Scope, fees and responsibilities agreed
before diagnostic or implementation work.