AI LAB

1. Program Overview

The RIL AI Lab is a structured, membership-based innovation program designed to enable builders, engineers, designers, and technologists to experiment, prototype, and ship AI-powered solutions. The Lab prioritizes outputs over instruction, systems over supervision, and visibility over certificates. Members work in monthly cycles to explore AI use cases, build functional demos, and showcase outcomes through a disciplined demo-driven cadence. This document defines the operating model, governance, tooling, and enforcement mechanisms required to run the program seamlessly with minimal manual oversight.

2. Program Objectives

Primary Objectives

• Enable consistent AI experimentation and rapid prototyping

• Produce portfolio-grade demos, tools, and workflows monthly

• Identify high-performing builders for advanced RIL initiatives

• Reduce coordination overhead through automation-first systems

Secondary Objectives

• Create a visible AI innovation culture within RIL

• Build a reusable pipeline of ideas that can mature into products

• Position RIL as a regional hub for applied AI innovation

3. Target Participants

The AI Lab is designed for:

• Software engineers

• Data scientists

• Product builders

• Designers working with AI-enabled workflows

• Technologists transitioning into applied AI

Pre-requisite: Participants must be capable of independent execution. The Lab does not provide foundational AI training.

4. Membership Structure

4.1 Membership Tiers

1. Builder Member (Core Tier)

Access:

• Monthly AI problem prompts

• Weekly demo sessions

• Shared AI tools and service credits

• Community channels and repositories

Obligations:

• Minimum of one demo submission per month

• Participation in at least two weekly demo session

Fellow (Advanced / Invite-Only)

Eligibility: Consistent high-quality outputs Peer recognition and facilitator endorsement

Additional Benefits:

• Increased AI tool credits

• Leadership of mini-projects or themes

• Priority consideration for paid RIL work

• Public recognition through RIL channels

5. Program Cadence

The AI Lab operates on a fixed monthly cycle with mandatory weekly demos built into the cadence. This structure ensures continuous progress, visibility, and accountability without ad‑hoc follow‑ups.

Week 1 – Prompt Release & Kickoff

• 3–5 AI problem prompts released

• Members may select a prompt or propose a custom project

• Custom proposals require async approval

• Members signal intent to demo during the month

Week 2 – Build + Weekly Demo Session

• Independent build period

• Weekly catch‑up demo & insights session

• Members may demo early progress, experiments, or pivots

Week 3 – Build + Weekly Demo Session

• Continued build period

• Weekly catch‑up demo & insights session

• Focus on refinement, learnings, and blockers

Week 4 – Demo Week & Close‑Out

• Final weekly demo & insights session

• Formal demo showcase (60–90 minutes)

• Evaluation, scoring, and documentation

Monthly Participation Requirement 1. Each member must complete a minimum of two (2) weekly demos per month 2. Demos may occur in any weekly session within the cycle 3. Failure to meet the minimum demo requirement results in loss of eligibility for awards, tool access, and renewal into the next cycle

End of Month

• Innovator of the Month selection

• Public showcase and write‑ups

• Tool access reset and cycle restart

6. Weekly Demo & Insights Sessions

The AI Lab runs weekly catch‑up demos and insights sessions to maintain momentum, accountability, and shared learning without introducing heavy coordination overhead.

• Frequency: Weekly

• Duration: 45–60 minutes

Format:

• 3–5 member demos (5 minutes each)

• 10–15 minutes of shared insights (patterns, tools, failures, wins)

• Live screen-sharing only (no slide decks)

Purpose:

• Surface progress early

• Encourage peer learning through real builds

• Identify promising ideas and blockers quickly

Rules:

• Fixed day and time each week

• Attendance is tracked automatically

• Members must demo at least once per month

• Two missed sessions in a month results in loss of eligibility for awards and tool access in the following cycle

7. Tooling & Infrastructure

7.1 Community & Communication

Slack

7.2 Project Tracking

• Notion workspace

• Standardized member project templates

• Automated status tracking

7.3 Code Repositories

• Central GitHub organization

• One repository per member per month

• Standard project scaffolding provided

7.4 Demo Submissions

• Centralized submission form

• Automated demo agenda generation

• Submission windows enforced programmatically

8. Automation & Enforcement

The AI Lab is governed by systems, not manual follow-up.

Automated Processes

• Scheduled prompt publishing

• Demo sign-up and slot allocation

• Attendance and submission tracking

• Tool credit usage monitoring

• Eligibility checks for awards and renewal

Enforcement Rules

• No demo submission → no renewal

• No repository → no recognition

• Repeated inactivity → automatic removal

All enforcement actions are system-triggered and non-negotiable.

9. AI Tools & Resource Access

The Lab provides shared access to a rotating set of AI tools, which may include:

• Large language model APIs

• Vector databases

• Agent frameworks

• Search and data ingestion tools

Usage Policy:

• Credits are capped per member

• Usage is monitored

• Abuse results in automatic access revocation

10. Recognition & Incentives

Innovator of the Month

• Awarded based on:

• Technical execution

• Originality

• Practical relevance

• Demo quality

Benefits:

• Public feature across RIL platforms

• Certificate of recognition

• Additional tool credits

• Priority access to paid RIL initiatives

11. Governance Model

Roles & Responsibilities

Program Manager

• Owns program operations and reporting

• Oversees automation and tooling

AI Lab Lead

• Facilitates demos

• Releases prompts

• Enforces program rules

RIL Leadership

• Strategic oversight

• Monthly judging participation

• Direction setting

12. Success Metrics

The program is evaluated monthly using the following metrics:

• Percentage of active members submitting demos

• Number of shipped prototypes

• Tool usage efficiency

• Member retention beyond two cycles

• Number of projects graduating into RIL initiatives

Failure to meet benchmarks over two consecutive cycles triggers a program review.

13. Review & Iteration

This document is reviewed quarterly. Program structure, tooling, and incentives may evolve based on data and outcomes.