ScholarGraph
In developmentSemantic search and a knowledge graph over your own paper collection, from PDF upload to citation chains.
Request early accessAI applications for researchers and small technical teams.
I design and build document search, vision, and local-inference tools, and I run my own research product, RisetNusa, to prove they hold up.
One task, passed hand to hand.
A job moves from draft to review to a finished deliverable, and a failed review sends it back. I test several models at each step and build the workflow around the ones that fit your data, instead of defaulting to a single vendor.
- RisetNusa is live at risetnusa.ferronlabs.com
- AI explainer series on YouTube and Instagram
- Built by a solo engineer with an M.S. in AI
Live product
RisetNusa maps Indonesian AI research.
A working example of what I build for clients: search, trends, topic maps, and potential gap signals across AI and computer-science work from Indonesian institutions.
Open RisetNusa
Semantic search
Find papers by meaning, not just keywords.
Trends
See how topics grow across institutions and years.
Topic maps
Explore how research areas connect.
Potential gap signals
Under-explored areas, each with its evidence shown.
What I can build for you.
Small, focused builds for teams that have data and a clear question, but not yet the software.
Document search and RAG apps
A private web app that lets a team ask questions over its own papers, reports, or manuals, with the source shown for every answer.
Computer vision tools
Detection, classification, or inspection behind a simple dashboard or API, tuned on your images rather than a generic demo set.
Local and edge inference
Models that run on your own hardware behind a local interface, for work where the data cannot leave the building.
Scope
A short call and a written brief: the problem, the data you have, and what done looks like.
Plan
Architecture, timeline, and price in writing before any build starts.
Build
Regular check-ins with working software you can click through.
Handover
Source code, setup notes, and support while you settle in.
AI, explained so it makes sense.
One-minute videos that take a single AI idea, start from an everyday example, then add one layer of real depth. The videos are in Indonesian.
What is an LLM?
It predicts the next token
Why does AI make things up?
Sounds right, is wrong
Generative AI vs AI agent
Answers vs actions
How does an LLM see an image?
Patches of 28 by 28 pixels
Next from the lab.
Two research tools in progress. Ask for early access.
TeXWeave
In developmentA collaborative LaTeX editor with context-aware completion and journal template formatting.
Request early accessFerron Labs is one engineer, on purpose.
I’m Fadhil Elrizanda, an AI engineer in Jakarta with an M.S. in Artificial Intelligence. Ferron Labs is my studio for client builds, research products, and teaching AI in plain language. You work with me directly, from the first call to handover.
Have a problem worth solving?
Send a short brief: what you need, the data you have, and your timeline. I reply within two working days.