iEvo — AI Capability Programme Proposal

Proposal

AI Capability Programme
The Art of the Possible with AI

Leadership Workshop & Practitioner Course for iEvo

IRAJ Evolution Design Co. Pvt. Ltd. (iEvo)

Prepared by Mellone · July 2026 Confidential

MELLONE

Understanding your organisation

Who you are

~2,000 employees ~450 admin/system users Premium contract furniture manufacturer

Where you're starting from

Informal AI use in Costing & Central Ops Mid-migration: iEvo Net → Infor LN 9AI PM agent rolling out

What you actually need

Governance, not awareness Scenarios from real BOQ / drawings / QC work Structured, evaluated capability

Raw material we'll build with

90+ audited problem register Process, people & tech summaries Infor LN rollout calendar

Founders

Rakesh Venugopal

Co-Founder, Product & Strategy

Indian School of Business

"Seed to Series F — strategy, growth & org transformation."

Swadhin Sahu

Co-Founder, Operations & Revenue

IIT Madras IIM Lucknow

"Ed-tech to AI products & services — revenue, growth, analytics & operations."

What we do

AI training for corporates

Upskilling teams and leadership through hands-on, role-relevant AI training

AI implementation & FDE services

Embedding AI solutions directly into client operations, from strategy to execution

AI training in colleges

Building AI fluency in the next generation through campus partnerships

AI products (stealth)

Proprietary AI products currently in development

Programme architecture

Two tracks plus a sustain layer. Track A creates leadership pull; Track B builds practitioner capability; the sustain layer keeps it alive after we leave.

Track A
Leadership Art of the Possible

Audience & format

Founders, Director's Office, CTO, ~15 HODs (20–30 pax). 2 days, in-person.

Primary outcome

Each HOD leaves with 2–3 sponsored AI opportunity hypotheses and a shared governance stance.

Track B
Practitioner Course

Audience & format

~450 admin/system users, phased by department. Fully self-paced, recorded content.

Primary outcome

Every participant produces one evaluated, work-applied AI assignment; certified on pass, not attendance.

Sustain Layer
Train-the-Champion

Audience & format

1–2 nominated champions per department. Half-day train-the-champion session.

Primary outcome

Internal capability to score assignments, unblock learners, and onboard new joiners after we leave.

Champions are nominated by HODs during Track A's closing session — they come from within Track B's first cohorts, not hired externally.

Track A · Day 1

Widening the aperture

Day 1 is built to stretch how leaders think about AI before asking them to commit to anything. Every block closes with leaders producing something themselves, not watching a facilitator produce it for them.

9:30–11:00

Opening: Why Now, Why Us

Taught: AI framed as a shift in how the business operates, not a tool rollout — anchored directly to the Infor LN migration and the 9AI project agent already in motion.

Outcome: A shared reason for the room to take the next two days seriously, set by iEvo leadership itself.

11:15–1:15

The AI Landscape: Opportunities & Competitive Advantage I

Taught: How to instruct an AI tool well — giving it role, context, and constraints — demonstrated live against iEvo-shaped work: reading a client drawing, sanity-checking a BOQ, drafting a project status note, and translating a shop-floor instruction.

Outcome: Leaders operate the tools with their own hands and leave able to judge good AI output from weak AI output in their own domain.

1:15–2:00

Lunch

2:00–4:00

AI Strategy & Business Case Identification I

Taught: 2–3 accounts of manufacturing and project-based businesses at similar scale — what leaders decided, the order they moved in, and where their rollouts stalled.

Outcome: A realistic sense of sequencing and pitfalls, so iEvo's own plan is shaped by what actually failed elsewhere, not vendor highlight reels.

4:15–6:00

Working Session — AI Strategy & Business Case Identification

Taught: A working method for holding the department's own audited pain points against what AI can plausibly touch — separating real openings from wishful ones.

Outcome: Each HOD group leaves with a raw long-list of opportunities from their own function, to be scored overnight ahead of Day 2.

Tools used across Day 1 & Day 2: ChatGPT, Gemini, and the Claude ecosystem. Facilitators may bring in other tools during a session at their discretion, wherever that gets the most out of the room.

Track A · Day 2

Narrowing to commitment

Day 2 turns yesterday's long-lists into a small number of owned bets, backed by rules everyone can operate under, and closes with public commitments in the room.

9:30–11:00

AI Strategy & Business Case Identification II

Taught: A scoring lens for weighing value against feasibility, data readiness, and dependency on the Infor LN rebuild.

Outcome: Each HOD narrows yesterday's long-list to 2–3 hypotheses worth sponsoring.

11:15–12:30

Data Protection / Governance / Compliance / Security / AI Roadmap

Taught: What can safely go into an AI tool and what can't — client data, costs, margins, IP — plus where a human sign-off is non-negotiable.

Outcome: A one-page draft AI usage charter, written by the room, ready for leadership sign-off.

12:30–1:30

Lunch

1:30–3:30

AI Strategy & Business Case Implementation I

Taught: What sponsoring a bet actually requires — reviewing AI-assisted work fairly, and staying alert to escalation or single-approver bottlenecks specific to AI-driven decisions.

Outcome: HODs leave clear on their ongoing role once the agency has left the room.

3:30–4:15

Preparation for presentation

4:30–6:00

AI Strategy & Business Case Implementation II — Presentation

Taught: How to pitch a sponsored bet in the time it takes to hold a room's attention.

Outcome: Every HOD presents live to Founders; the Track B department sequence and champion nominations are locked before the room disperses.

Required Track A artefacts

Department opportunity hypotheses

Draft AI usage charter

Nominated champions list

Agreed Track B sequence

Tools used across Day 1 & Day 2: ChatGPT, Gemini, and the Claude ecosystem. Facilitators may bring in other tools during a session at their discretion, wherever that gets the most out of the room.

Track B · Practitioner Curriculum

Six modules per department, split between what's true for everyone and what has to be rebuilt for each function so the practice work is genuinely theirs.

M1

AI Foundations for Our Work

What today's AI tools are good at, where they quietly get it wrong, and the habit of checking before trusting.

STATIC

M2

Responsible Use & Data Rules

The Track A usage charter turned into everyday practice — what can go into a tool, and when to stop and ask.

STATIC

M3

Prompting as a Craft

Role, context, constraints, format — practised on that department's own real tasks, not generic examples.

×6 DEPTS

M4

AI in Your Workflow

Mapping one recurring task to an AI-assisted version — the task is department-specific by definition.

×6 DEPTS

M5

Working with Our Systems

Which Infor LN / 9AI touchpoints matter, and how to escalate when the agent gets it wrong — differs by function.

×6 DEPTS

M6

Capstone Assignment (brief)

Generic instructions to pick a real task, execute it AI-assisted, and document before/after for review.

STATIC

3 hrs

Static content
(M1, M2, M6 brief)

18 hrs

Dynamic content
(M3–M5 × 6 depts)

3 hrs

Applied-lab scenario
walkthrough (6 × 30 min)

Every module () closes with a short quiz — passing it is required to unlock the next module. Quiz design and build sits inside the 6-hour production buffer, not as additional recorded hours.

30 hours total recorded content — including a 6-hour production buffer

Track B · Department AI Labs

Where the recorded modules meet real work, live — and where each department's AI Champion starts stepping into the mentor role they'll carry after we leave.

AI Lab — 4 hrs, once per department

A live, hands-on session run on 1–2 real scenarios sourced directly from that department's section of iEvo's problem register — depth over coverage, not a tour of possibilities.

Co-facilitated: mentor + that department's AI Champion

Office Hours — 1 hr/week, for 4 weeks

Ongoing doubt-clearing as participants work through their self-paced modules and start their capstone — this is the lightweight async-support mechanism iEvo asked for, keeping people from getting stuck mid-assignment.

Mentor-led, with the Champion present as second responder

Rollout — 3 waves, 2 departments in parallel

tentative, subject to iEvo scheduling

Wave 1 · Weeks 1–4

Costing & Tendering
BD (Domestic & Intl)

Wave 2 · Weeks 5–8

PMC / Central Ops
Design / Hanmac

Wave 3 · Weeks 9–12

Production / QC / Dispatch
Finance / HR / IT

24 hrs

Live AI Lab
(6 depts × 4 hrs)

24 hrs

Office hours
(6 depts × 4 wks × 1 hr)

~12 wks

Total rollout,
all 6 departments

The wave grouping and 12-week timeline above is our suggested starting point — final sequencing will be finalised jointly with iEvo against the Infor LN rollout calendar and department readiness.

Track B · Evaluation Framework

Evaluation isn't a survey at the end — it's built into the same touchpoints participants already move through. Certification is earned on capstone pass plus the data-rules test, never on attendance alone.

1. Reaction

Relevance & confidence per module / lab

Short pulse survey after each recorded module and after the live AI Lab

2. Learning

Prompting skill & data-rules comprehension

Gating quiz after every module (pass required to unlock the next) plus a final prompting-task + data-rules scenario test before capstone eligibility

3. Application

Real-work behaviour change

M6 capstone scored against a shared rubric, two-layer review; 30/60/90-day usage check-ins per department

4. Result

Early business indicators

Department-selected metric (e.g. drawing-clarification time, TSOW prep hours), baselined before that department's cohort starts

Standard capstone submission format

1. Prompt(s) used 2. Output produced 3. Human edits made to the output 4. Time comparison vs. the manual method 5. Data-rules self-check

Two-layer review on the capstone

Champion scores domain correctness.

Mellone moderates AI-usage quality.

Agency-moderated for the first two cohorts, then champion-led.

Reporting cadence — including per-cohort completion-rate data — will be jointly defined with iEvo, and delivered as part of the per-cohort scorecard.

Sustain Layer

The programme has to survive after we leave. Champions are the mechanism — and their preparation starts inside Track B itself, not in a separate room afterward.

Who champions are

1–2 per department, nominated by HODs during Track A's closing session — drawn from within that department's own Track B cohort, not hired in externally.

How they're prepared

Co-facilitating their department's live AI Lab alongside the mentor is the apprenticeship — they're already applying rubric-thinking to real cases before being asked to run it solo.

Half-day train-the-champion session

How to score capstone assignments against the shared rubric

How to unblock and motivate participants through a fully self-paced course

How to onboard new joiners into the recorded content going forward

After Wave 1's two departments, champions take over capstone scoring independently — Mellone steps back to moderation-on-request rather than moderating every submission.

At close: iEvo receives editable source content, with champions able to generate new-joiner assignments from the same rubric template. This half-day session is included in Year 1 as part of the core Track B fee; an optional AMC retainer covers content refresh and champion re-enablement from Year 2 onward.

Roles & Responsibilities

iEvo provides

Sanitised problem register (90+ audited issues) plus process, people, and technology audit summaries, shared under NDA

A briefing session with iEvo's programme owner before scenario production begins

Leadership artefacts from Track A: usage charter, department hypotheses, and champion nominations

A sequencing calendar aligned to Infor LN rollout milestones, so cohorts land as the relevant workflows go live

Confirmation of the final approved-tools list after Track A's governance session, before recorded production locks

Logistics: venue, participant scheduling, and a single programme owner as our counterpart throughout

Mellone delivers

A detailed 2-day Track A facilitation plan, live demo list, and workshop materials

The full Track B curriculum pack: module recordings, department scenario modules, capstone rubric, and evaluation instruments

Mentors for every live AI Lab and office-hours session, co-facilitating alongside each department's AI Champion

Editable source content handed over at close, so champions can generate future new-joiner assignments from the same templates

Commercial response: facilitator profiles, references from comparable manufacturers, fixed pricing per track, and the train-the-champion option

Meet Some of our Mentors

Practitioners first — every mentor has built and delivered AI work across sectors before teaching it.

Divij Bajaj

Data & Applied Scientist II, Microsoft · AI Educator & Consultant, Thinklytics · Ex-VMware

~7 years building and productionising ML/GenAI systems at enterprise scale; published author on LLMs and Generative AI.

Prior clientele sectors

Government Fintech Pharma

Jitesh Dugar

Founder, Mediajade (Authorised Zoho Partner) · Top 10 Global n8n Creator · AI & Automation Specialist

Builds custom AI-powered automations end-to-end across CRM, workflow, and orchestration tools; prior Senior Product Manager background at Wati and Drivezy.

Prior clientele sectors

Healthcare Hospitality Education Gaming Finance Trading EdTech SaaS

Sukin Shetty

Enterprise AI Architect · Vice President of AI, Kambaa Inc. · Creator, Nemp Memory · AI Educator

Designs agentic AI systems and enterprise AI architecture; trained 10,000+ individuals across corporate workshops and technical bootcamps; background in manufacturing operations.

Prior clientele sectors

Retail BFSI NGOs Education Institutions IT Sector Startups Manufacturing Supply Chain

Commercial Summary

Track A — Leadership Workshop

In-Person

₹5L + GST

Flat fee for the 2-day, in-person leadership workshop — Founders, Director's Office, CTO, and 20–30 HODs.

Track B — Practitioner Course

Online

₹18L + GST

Covers all ~450 participants across all 6 departments — recorded curriculum, live AI Labs, office hours, and evaluation.

~₹4,000 per participant (pre-GST)

₹18,00,000 ÷ 450 participants — for a multi-week, evaluated, capstone-certified AI capability programme across every department.

Optional — AMC: Content Refresher Retainer + Train-the-Champion

₹5L + GST / year, from Year 2

Annual refresh of recorded content and re-enablement of champions (e.g. for new joiners replacing an outgoing champion), for organisations that want ongoing support beyond the initial handover. Year 1's train-the-champion session is already included in the core Track B fee — this retainer applies from Year 2 onward only, with no double-costing in Year 1.

Momentum · Why Mellone

AI Nexus for Leaders, Mauritius

A high-touch AI training programme curated specifically for industry and government leaders — strategic AI adoption, governance frameworks, and decision-making under uncertainty, delivered to senior officials and executives across sectors.

AI training deployment — 5 colleges, India

In progress

Campus-wide AI fluency programme — proven ability to run structured curriculum across multiple cohorts in parallel.

Forward-deployed engineering partnership

In progress

FDE talent embedded within a leading AI lab in Mauritius — hands-on implementation depth, not just training delivery.

A mentor bench built for this brief

Mentors with prior sector exposure spanning government, manufacturing, supply chain, BFSI, and enterprise AI architecture — not generalist trainers.

Programme feedback — 5-point scale

4.6/5

Service & delivery

4.8/5

Mentors & instruction

4.6/5

Content & curriculum

4.8/5

Likelihood to recommend

4.6/5

Overall programme

AI confidence 1.4 → 3.6 (+2.2 lift), across prior cohorts

Thank you

We'd love to bring this to iEvo — leadership conviction, practitioner capability, and a sustain layer that keeps working long after we leave.

IRAJ Evolution Design Co. Pvt. Ltd. (iEvo) · AI Capability Programme

hi@mellone.ai

www.mellone.ai

MELLONE