How we work
From business case to production, with one team
The engineers who scope your AI are the ones who ship and improve it. No hand-offs.
Principles
The principles behind every engagement
Start from the business case
Tech advisory, a data audit and AI strategy research come first, so the build goes where AI pays back.
One team, no hand-offs
The engineers who scope your AI are the ones who ship and improve it. You keep one point of accountability throughout.
Built around your systems, data and regulator
Models, hosting and guardrails are chosen to fit the systems you already run, the data you hold and the rules you answer to.
Improvement scoped in from day one
Monitoring adoption and model performance is part of the build. After go-live, we stay to review, iterate and improve.
The four steps
Four steps, one accountable team
Every engagement moves through the same four steps, and the engineers who start it are the ones who finish it.
Talk to an AI engineer
01
Discover
Start from the business case
Run tech advisory, a data audit and AI strategy research to find where AI pays back, before any build starts.
Tech advisory on the systems and processes AI must fit
A data audit of what you hold and where it sits
AI strategy research to find where AI pays back
02
Design
Design an architecture that fits your stack
Choose models, hosting and guardrails around your existing systems, your data and your regulator.
Models chosen per project across Qwen, Claude, OpenAI, Gemini and ILMU
Compute and cloud from FPT AI Factory, NVIDIA, Alibaba Cloud or Google Cloud
Locally hosted models where data must stay in-house
Guardrails for AI safety, alignment and governance
03
Implement
Build AI into production
Integrate retrieval, agents and automation across your core systems, including bank, government and SSM portals.
Retrieval over your own documents and data
Agents with the tools, MCP servers and APIs they need to act safely
Integration with ERP, CRM, case management and third-party portals
04
Improve
Improve continuously after go-live
Monitor adoption, review model performance and iterate, with improvement scoped into the build from day one.
Adoption monitoring once your teams are using the system
Model performance reviews, with MLOps to keep models accurate in production
Iterations planned from day one, not bolted on later
Full stack AI engineering
What every system is made of
The brain
Foundational intelligence core
Custom knowledge integration, RAG and beyond, specialised model fine-tuning, and multimodal intelligence across vision and language.
The action
Agentic and autonomous solutions
Agentic process automation, custom tool, MCP and API development for agents, and multi-agent systems that do the work.
The guardrails
AI governance
AI safety and alignment, governance guardrails, and locally hosted models where data must stay in-house.
Explore the three layers
Projects in progress
Already mid-build? We can take it over
Not every project starts with us. We have taken over projects midway and delivered them, including a centralised pawn brokerage system for iPajak Group’s six branches across Malaysia.
Once a system is live, we stay to improve it.
Read the Fundaztic and iPajak story
Daren and the ALPHV team have been exceptional to work with and have done a commendable job taking over the project midway, especially in providing their technical expertise to meet our specific needs.
Chong Voon Ying
CEO, iPajak Group
FAQ
Frequently asked questions
How engagements start, what we connect to, and where your data and models run.
How do we get started?
Talk to our team. Every engagement starts from the business case, with tech advisory, a data audit and AI strategy research before any build begins.
Can you work with our existing systems?
Can our data stay in-house?
Which AI models and clouds do you use?
Let’s build what’s next
Tell us where your business needs to be. We’ll map the path to get there.