Service · 02
AI consulting and AI product strategy for institutions
Raylab helps institutions find AI use cases worth building, then designs the product and workflow around them.
Contact UsAn AI application starts with the work to be supported, the decisions that remain human, and the data available. Raylab follows that order: the problem, the product experience, then the technology.
Relevant founder experience includes AKTIF AI for the House of Representatives and Digdaya AI-NU within PBNU’s ecosystem. In both assignments, the contribution was product direction and user experience; he did not build the AI model or the entire infrastructure.
Situations we address
Many AI ideas, no clear priority
We screen ideas through discovery: how large the problem is, who uses the system, and whether the data and processes are ready.
Attractive prototypes that stop at the demo
We build small prototypes and MVPs tested with real users, so investment decisions rest on use rather than presentation.
Concerns about data and risk
Data rules, access rights, and the boundaries of AI roles are defined first through governance aligned with Indonesia’s Personal Data Protection Law.
AI that sits outside the workflow
AI assistants separated from daily work rarely get used. We design them inside the flow people already follow.
How we work
- 01
Understand
We map users’ work, data, and constraints, then list AI opportunities and their risks.
- 02
Design
Selected opportunities become product experiences: what AI does, what people still decide, and how output is reviewed.
- 03
Build
Prototypes and MVPs are built in stages and tested, with answer quality evaluated before scope expands.
- 04
Adopt
We prepare user support and usage monitoring so the next improvement is grounded in how the product is used.
Deliverables
AI use-case discovery
A prioritised list of AI opportunities with rationale, data prerequisites, and risks.
Prototype & MVP
An AI product real users can try, proving value before larger investment.
AI governance
Rules for data use, roles, human review, and oversight that keep AI safe and accountable.
Relevant use cases
PBNU (Nahdlatul Ulama)
Digdaya NU
Bringing letters, boards, cadres, pesantren, mosques, and an AI assistant into one ecosystem.
Founder’s role: Head of Digital PMO, PBNU (consultant)
BNPB (National Disaster Management Agency)
BNPB Information Systems Audit and Grand Design
Shaping disaster management information architecture from an audit of 50+ applications to a grand design.
Founder’s role: Product Strategy for the grand design and architecture
Supporting experience
DPR RI Expertise Agency (Badan Keahlian)
AKTIF AI
An AI ecosystem supporting the drafting of academic papers and bills at the DPR RI Expertise Agency, from research and regulatory harmonization to drafting.
Founder’s role: Product Experience Advisor
This experience comes from the founder’s assignments before Raylab was founded, as an individual consultant and alongside other companies and teams.
Questions about this service
Do we need clean data before starting?
Not necessarily. Discovery assesses data readiness early and identifies what needs fixing, so you know the next step before building anything.
Does Raylab only advise, or also build AI products?
Both. We can stop at recommendations and a roadmap, or continue into prototypes and MVPs with Digital Product Delivery.
How does Raylab address AI safety and ethics?
Through governance agreed from the start: data rules, roles and access, limits on automated decisions, human review, and compliance with Indonesia’s Personal Data Protection Law.
Can AI be used in government or large organizations?
Yes, when governance and processes are ready. The founder’s experience includes an AI platform for legislative work in the House of Representatives and an AI assistant within PBNU’s digital ecosystem.
What are you trying to achieve?
Tell us what you need. We will start with that context.