Rilibay - Applied AI Lab
Applied AI, built for
one industry at a time.
We design, ship and run focused AI tools with the people who know the work. Adopting AI should feel like hiring a specialist, not buying a science project.
| No. | Build | Status | |
|---|---|---|---|
| 001 |  | Audience engagement | Live |
| 002 | Undisclosed | Vertical AI tool | In research |
| 003 | Undisclosed | Vertical AI tool | In research |
01 - The lab
Three ways we put AI to work.
- 01 / Build
Our own vertical products
We pick one industry and one painful workflow, then build a focused tool for it. We run it in production and keep improving it.
See products - 02 / Co-build
With domain operators
You know the work and the customers. We bring product, engineering and AI. We agree ownership and terms before we write any code.
The co-build programme - 03 / Adopt
Inside your team
We add AI to the tools you already use. We also tell you where AI isn’t ready for the job yet.
Start a conversation
02 - Build 001 · Live
Rilifi: live engagement for rooms that need to participate.
Our first product. Rilifi brings live quizzes, polls and spin wheels to classrooms, training sessions and events. Shared team folders let a whole team own its content together.
- Creators
- 1,000+
- Countries
- 10+
- Participants
- 50K+
Figures as published on rilifi.com.
Next We’re building AI question generation. Hosts will draft a quiz from a topic in seconds.
Which topic should we cover after the break?
- AEvaluating model output37%
- BKeeping customer data private30%
- CWriting better prompts20%
- DControlling AI costs13%
03 - Method
How a build happens.
Every tool we ship goes through the same four stages, ours or a partner’s. Each stage ends with something you can hold.
- 1
Sit with the work
We start inside the workflow, not a slide deck: who does what, with which files, and where time leaks.
Output → a written workflow map and a go / no-go call
- 2
Prototype on real examples
We build a working prototype and test it on real examples you’ve cleared us to use. We measure quality. We don’t guess.
Output → a prototype and its first evaluation report
- 3
Ship with guardrails
Live software with review steps, audit trails and fallbacks. The model drafts. People approve.
Output → a live tool your team uses daily
- 4
Operate and improve
Models and data change. We watch quality and fix drift before your users notice it.
Output → a regular quality report and changelog
04 - Principles
Practical AI over hype, in five lines.
- 01
Useful beats impressive.
A tool that saves a coordinator an hour a day is worth more than a demo that wins a meeting.
- 02
Measure, then ship.
Every model-powered feature ships with an evaluation set built from real examples. No numbers, no release.
- 03
People approve. Models assist.
Our tools draft, sort, extract and suggest. A tool earns more freedom one task at a time, with evidence.
- 04
Your data works for you.
We use customer data to serve that customer. We never sell it. We never use it to train models for anyone else.
- 05
Small and sharp.
One industry and one workflow, done well, beats a platform that tries to do it all.
05 - Lab notes
Written from the bench.
All lab notes06 - Questions
Straight answers.
What is an applied AI lab?
A team that turns AI into working tools for specific jobs. Research labs ask what models can do. We ask which part of a job a model should handle. Then we prove it works before it ships.
What does Rilibay build?
Focused software for specific industries. Our first product is Rilifi. It runs live quizzes, polls and spin wheels for audiences. Our next builds are AI tools, each one shaped around a single industry’s work.
Do you build AI tools for other companies?
Yes, in two ways. In our co-build programme, we team up with people who run a business. Together we build an AI tool for their industry. We agree ownership and terms first. We also take on a few small projects for teams that want AI inside the tools they already use.
How do you decide if a workflow is a good fit for AI?
We ask five questions. Is the input messy but the output clear? Can a person check a result faster than they could make it? Does the work happen often? Is the cost of a mistake small and contained? Do real examples exist to test with? If the job fails the test, we say so and suggest a simpler fix.
Where is Rilibay based?
In New Delhi, India. We work with teams all over the world.
Start a conversation
Bring us a workflow. We’ll tell you if AI should touch it.
Thirty minutes, no slides needed. You describe the work. We give you a straight answer on whether an AI tool would help, and what it would take.