I.EVO – AI Capability Programme Proposal

Proposal

AI Capability Programme
The Art of the Possible with AI

Leadership Workshop & Practitioner Course for I.EVO

IRAJ Evolution Design Co. Pvt. Ltd. (I.EVO)

Prepared by Mellone · July 2026 Confidential

MELLONE

Understanding your organisation

Who you are

~2,000 employees ~500 admin/system users Premier Design & Build company for Tier 1 hospitality brands

Where you're starting from

Informal AI use in Costing & Central Ops Mid-migration: I.EVO 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."

Balwinder Singh

Co-Founder, Technology

Motilal Nehru NIT

"Nike & Lowe's to AI for impact – engineering leadership, systems & scale."

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

Training first, then consulting, then build

Track A1 turns leadership into hands-on AI practitioners through live, in-person training; Track A2 then screens and ranks your opportunities into a build-ready shortlist. What gets approved moves into implementation, with practitioner rollout running alongside it.

Track A1

Training

Who: Founders, Director's Office, CTO, ~15 HODs (20–30 pax)

When: 2 days, in-person, entirely hands-on/live

Produces: Leadership and HODs upskilled hands-on, ready to sponsor AI-driven initiatives

Track A2

Consulting

Who: Mellone + I.EVO + 9AI; HOD input at BVR stage

When: Post-workshop, ~8 weeks

Produces: Approved shortlist of 5–10 opportunities – BVR, roadmap & PRD for each

Gate

Steering
committee

Go / no-go

Both run in parallel once approved – timing scoped after Track A concludes

Track B

Practitioner Course

~500 admin/system users, self-paced. Available as a follow-on phase – full curriculum detail in the annexure.

Sustain Layer

Train-the-Champion

1–2 champions per department, half-day session. Runs once Track B is scheduled – internal capability to score assignments & onboard new joiners.

Track C

Implementation

Mellone or another vendor builds the approved use cases. Scope & timing set post-gate. Includes a post-deployment adoption/actuals review.

Programme timeline

How the tracks sequence against each other

Track A runs as a fixed 9-week sequence – the workshop, then consulting. Track C and Track B follow, scoped only once Track A is complete, so they're shown after without a week commitment.

W1

W2

W3

W4

W5

W6

W7

W8

W9

A1 · Training2-day, in person

W1

A2 · ConsultingScreening & BVR

W2–W9

Consulting concludes – Track A complete

Then – scoped after Track A, no fixed week commitment

Track B

Practitioner Course

~500 admin/system users, self-paced. Available as a follow-on phase – full curriculum detail in the annexure.

Track C

Implementation

Mellone or another vendor builds the approved use cases. Scope & timing set post-Track A. Includes a post-deployment adoption/actuals review.

A1 – Training

A2 – Consulting

Track B / Track C – scoped after Track A

Track A1 · Day 1 & Day 2

Think big on Day 1, go deep on Day 2

Day 1 · Think big

10:30–11:30

Opening: Why Now, Why Us

Founder framing – AI as an operating-model shift, anchored to the Infor LN migration and the 9AI project agent.

11:45–1:45

AI Landscape: Opportunities & Competitive Advantage

Prompt engineering fundamentals + 4–5 hands-on demos on I.EVO-like material.

1:45–2:30

Lunch

2:30–4:00

AI Strategy & Business Case Studies

2–3 comparable-business case studies from organisations further along the AI journey.

4:15–6:00

Working Session – AI Use-Case Deep Dive I

Mentor demos live on real I.EVO material – a real BOQ, drawing, or status doc – 2 use cases drawn directly from I.EVO's own operations. Then hands the tool to HODs to try on their own function's problem.

Day 2 · Go deeper

9:30–11:00

AI Upskilling – Business Use Cases & Tool Deep Dive I

Live tool demo on a use case specific to that department's daily work – audience watches real output get produced, then gets a hands-on rep. Post-read + assignment included.

11:15–12:30

Governance, Compliance & AI Roadmap

12:30–1:30

Lunch

1:30–3:30

AI Tool Deep Dive II

Second wave of live demos + hands-on practice, deepening tool fluency on use cases relevant to each function.

3:30–4:15

Governance & AI Usage Charter

HODs finalise the draft AI usage charter together – what can go into a tool, and when to stop and ask.

4:30–6:00

Close & Champion Nominations

Each department nominates 1–2 AI Champions to carry the practice forward, and the room closes with a shared roadmap for what comes next.

Tools used across Day 1 & Day 2: ChatGPT, Gemini, and the Claude ecosystem.

Illustrative Use-Case Menu

A sample of what the room will see live

Chat with your own document

Upload any SOP, catalogue or policy doc and get instant, accurate answers in plain English.

Rough sketch → visual concept

A rough idea becomes a shareable visual in seconds, live on stage.

Hindi/Hinglish technical translation

Precise, two-way translation that preserves technical terms.

HOD numbers → instant ROI calculation

Each HOD sees their own opportunity's ROI calculated live.

Raw tracker/export → instant chart

A messy export becomes a clear, correct chart in under a minute.

Long email thread → summary + risk flags

The risk buried in paragraph 4, surfaced instantly.

Meeting transcript → action items

A messy transcript becomes a clean, owned action list instantly.

Mockup/finish photo → QC checklist

A photo becomes a consistent checklist draft in seconds.

PO vs. drawing quantity cross-check

Watch a real mismatch pattern get caught instantly.

Drawing-change note that survives handoffs

One clear note format that survives the full review chain.

Roles & Responsibilities

I.EVO provides

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

A briefing session with I.EVO'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

Commercial Summary — Track A1 Training

Track A1 – Training

In-Person

₹4L + GST

Flat fee for the 2-day, in-person workshop — Founders, Director's Office, CTO, and 20–30 HODs — entirely hands-on, live tool work against real I.EVO material. Closes with a shared AI usage charter and nominated department champions to carry it forward.

Includes travel & accommodation for mentors and the Mellone team

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

Annexure

Track A2 · Consulting

An 8-week path from 111 problems to 10 build-ready use cases

Four steps, fully sequential – each one narrows the field further before the next begins, so effort only scales up once leadership has bought in.

W1

W2

W3

W4

W5

W6

W7

W8

1 · Opportunity Screening & AI-Fit Scoring

Outcome: AI Possibility Screening Scorecard

W1–W2

2 · BVR Development for Identified Use Cases

Outcome: A detailed BVR per use case

W3–W5

3 · Ranking & Steering Committee Approval

Outcome: Top 10 use cases approved

W6

4 · PRD & Implementation Roadmap

Outcome: 10 build-ready PRDs + roadmap

W7–W8

Screening

BVR development

Ranking & approval

PRD & roadmap

Track A2 · Step 1 of 4 · 2 weeks

Opportunity Screening & AI-Fit Scoring

Who's involved

Mellone, working directly with I.EVO and 9AI.

What happens

All 111 problems get examined in depth to understand what each one actually needs – then sorted into tech/AI use cases vs. items that aren't.

Duration

2 weeks. No formal HOD interviews needed – grounded in the register and institutional knowledge.

Outcome

AI Possibility Screening Scorecard

Track A2 · Step 2 of 4 · 3 weeks

BVR Development for Identified Use Cases

Who's involved

Mellone, working directly with I.EVO's HODs – one-on-one, per use case.

What happens

Every item flagged as a tech/AI use case in Step 1 gets built out into a full Business Value Rationale, grounded in real HOD numbers.

Duration

3 weeks – the most HOD-intensive step in the whole engagement.

Outcome

A complete, formula-driven BVR for every identified use case

Track A2 · Step 3 of 4 · 1 week

Ranking & Steering Committee Approval

111

Problems in the register

40–50

Screened as tech/AI-fit use cases

20

Ranked & presented to Steering Committee

10

Approved for implementation

Who's involved

Mellone ranks and presents; the I.EVO Steering Committee decides.

What happens

All use cases ranked using their BVR output; the top 20 go in front of the Steering Committee for a go/no-go call.

Duration

1 week – the decision point that sets Step 4's workload.

Outcome

Top 10 tech/AI use cases approved for implementation

Track A2 · Step 4 of 4 · 2 weeks

PRD & Implementation Roadmap for the Approved 10

Who's involved

Mellone, building on the approved BVRs – light-touch I.EVO input where needed.

What happens

Each of the 10 approved use cases gets a lightweight PRD and a high-level build roadmap – ready to hand to a build partner.

Duration

2 weeks – closes out Track A and feeds directly into Track C.

Outcome

10 build-ready PRDs + implementation roadmap

Commercial Summary — Track A2 Consulting

Available as a follow-on to Track A1 Training — see the 8-week workflow above for the full breakdown.

Track A2 – Consulting

8 Weeks

₹5L + GST

Covers the full path from the 111-item register to 10 build-ready use cases: opportunity screening & AI-fit scoring, BVR development with I.EVO's HODs, ranking & Steering Committee approval, and PRD + implementation roadmap for the approved 10.

Includes travel & accommodation for the Mellone team

Opportunity Screening

From Problem Register to Implementation Opportunities

This step does not identify new problems — it takes the problems already logged in the audited Problem Register (plus anything fresh an HOD wants to add) and screens, scores, and ranks each one into a short-list of implementation project opportunities for Track A and Track B.

Input — Problem Register (already audited) 4-Step Screen & Rank Output — Ranked Implementation Opportunities

1

Screen

Pick an item already logged in the Problem Register, or add one not yet on the list. HOD is named as sponsoring owner.

→

2

Score

Rate 4 Complexity factors and 4 Benefit factors, 1–5 each, against a written scale — not a blank number.

→

3

Place

The two averages plot the opportunity on a Complexity × Benefit quadrant.

→

4

Rank & Route

Quadrant sets a Priority Index and routes the item into the Track A or Track B implementation pipeline — the short-list sorts on that automatically.

Complexity — how hard to build

Averaged 1–5 across 4 factors

Build Time — hours of prompting → needs the iEvo Net rebuild

Build Cost (ROM) — ~₹0 → ₹5L+ spend

Data & Systems Readiness — already clean → doesn't exist yet

Dependency Risk — standalone → blocked on Infor LN / the rebuild

Benefit — how much it matters

Averaged 1–5 across 4 factors

Time Saved — negligible → frees up a meaningful share of an FTE

Cost Avoided / Value — negligible → ₹10L+ a year

Quality / Risk Reduction — negligible → removes a compliance-level risk

Strategic Reach — one task → cross-functional / whole lifecycle phase

Placement rule — Benefit ≥ 3.5 is "high", Complexity ≤ 2.5 is "low"

Quick Win

High benefit, low complexity → Track B implementation candidate

Major Bet

High benefit, high complexity → Track A leadership-sponsored initiative

Fill-In

Low benefit, low complexity → optional practice material only

Park

Low benefit, high complexity → not pursued this cycle

Ranking formula

Priority Index = Benefit − Complexity

The short-list sorts highest-to-lowest on this single number — Quick Wins naturally rise to the top, Parked items sink to the bottom, no manual re-sorting needed as more opportunities get screened.

Business Value Realization (BVR)

One-page implementation value case

Filled once per use case — Track A HODs, twice (intra + cross-functional); Track B participants, once — so the "Business Value & Quantified Impact" score in each rubric is backed by a real number, not a judgment call.

Suggested framework — not yet approved. The structure, formulas, and scoring bands below are Mellone's proposal for I.EVO to review. The rate card (₹/hour by role) is a required input from I.EVO Finance/HR and is not filled in until supplied. Nothing on this page should be treated as final until I.EVO leadership signs off.

Opportunity / Register ID

e.g. PR-2, or new

Owner (HOD / Participant)

Track

A / B

Date

1Cost to Build

Line	Formula	Amount (₹)
Build effort	hrs invested × loaded rate/hr
Tooling / licensing	incremental cost beyond approved stack
One-time implementation	data prep + integration + testing
Ongoing maintenance	annualized upkeep + champion time
Total Cost to Build

2Value Generated (annualized)

Line	Formula	Amount (₹ / yr)
Time Saved	hrs saved/wk × 52 × loaded rate/hr
Cost Avoided	direct spend avoided (rework, penalty, expediting)
Quality / Accuracy Gain	error rate × cost/error × frequency (COPQ)
Total Annual Value

ROI

(Value − Cost) ÷ Cost × 100 = ___ %

Payback Period

Cost ÷ (Value ÷ 12) = ___ months

Confidence in this estimate

High

Medium

Low

Pre-deployment, this is a projection — see the tracking sheet on the next page for how it gets checked against reality.

3Quantification Discipline — must all be checked before this counts toward the rubric score

Every value line shows its formula, not just a final number

Every assumption (rate, frequency, error cost) is stated in writing

The loaded rate used comes from I.EVO's approved rate card, not guessed

No line is left "intangible" — everything is converted to a number

BVR — Post-Deployment Tracking

Projected vs. actual, 30 / 60 / 90 days

The BVR page's numbers are a projection made before the implementation goes live. This sheet is the same opportunity, checked three times after go-live, to see if the projection actually held up.

Suggested framework — not yet approved. Pending I.EVO sign-off, and pending confirmation of who owns the 30/60/90 check-in (Champion, HOD, or Mellone) and how it connects to the separately-proposed governance/kill-switch layer.

	Projected Value (from BVR page)	Notes / Corrective Action
30DAYS	₹ / month, pro-rated from annual value	Early signal only — usage habit forming, not full run-rate yet
60DAYS	₹ / month, pro-rated from annual value
90DAYS	₹ / month, pro-rated from annual value	Decision point — see below

On or above projection

Confirmed for production / scale-up. Feeds the leadership prioritization grid as a proven case.

Below projection, recoverable

Owner revises the approach with Champion support; re-checked at next 30-day mark.

Materially below, day 90

Candidate for discontinuation — this is the trigger point for the separately-proposed governance "kill switch," not yet approved either.

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
(covering M1, M2, M6)

18 hrs

Dynamic content
covering M3, M4, M5 across 6 depts

Every module () closes with a short quiz – passing it is required to unlock the next module. Quiz design and build sits inside the 3-hour production buffer, not as additional recorded hours.

25 hours total recorded content – including a 3-hour production buffer

M3–M5 · What "Dynamic" Actually Means

Not a Udemy course with a company logo on it

Every department is taught the same five-part prompting skeleton — but the content inside it is pulled from that department's own audited pain points, so the practice work is a real task, not a stand-in example.

What stays constant — the skeleton, taught once to everyone

01

Role

02

Context

03

Constraints

04

Format

05

Iteration

What changes — the real task each department practises on

Costing & Tendering

Audit ref: T-13, T-14

Naive

"Cost this BOQ."

Dynamic

Prices must come from Hitesh's costing sheet; catalogue items pull the Library's locked price; flag anything missing instead of guessing.

PMC / Central Ops

Audit ref: P-5, P-7

Naive

"Summarise this thread."

Dynamic

Restate the open decision as a single yes/no question with a deadline and named owner — inside the 24–48h SLA, before it gets ducked again.

Design / Hanmac / PCD

Audit ref: PR-6, PR-7

Naive

"Fix this drawing note."

Dynamic

State what changed AND what didn't, so it survives all 4 hops of the Pytha→PCD→Designer→PCD→Pytha loop without a phone call.

Same three examples shown for Costing, BD, PMC, Design, Production, and Finance/HR/IT in full — each one built from that department's own problem-register items, not a generic template with the department name swapped in.

Track B · Department AI Labs

In-person

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 – 1 dedicated day per department, in person

A live, hands-on session run on 1–2 real scenarios sourced directly from that department group's section of I.EVO's problem register – depth over coverage, not a tour of possibilities. Delivered as 4 hours per department group, split into two 2-hour sessions across the day.

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

On-site at I.EVO – not delivered remotely

Office Hours – 2 hrs/week, 3 sessions

Common to all departments, run together, and matched to what's just been taught – Week 1 covers M1–M3, Week 2 covers M4–M6, Week 4 supports the capstone push. This is the lightweight async-support mechanism I.EVO asked for.

Mentor-led, with Champions present as second responders

Rollout – 5 weeks, all departments simultaneously

tentative, subject to I.EVO scheduling

Week 1 M1–M3 + Office Hr

Week 2 M4–M6 + Office Hr

Week 3 AI Lab Week

Week 4 Office Hr + Capstone Sub.

Week 5 Capstone Evaluation

Week 3 – one department per day

MON

Costing & Tendering

TUE

BD (Domestic & Intl)

WED

PMC / Central Ops / Installation

THU

Design / Hanmac / PCD

FRI

Production / QC / Dispatch / SMC / PPEC

SAT

Finance / HR / IT / MIS

24 hrs

Live AI Lab
(6 dept groups × 4 hrs)

6 hrs

Office hours
(3 sessions × 2 hrs, common)

5 wks

Total rollout,
all 6 departments together

This 5-week schedule is our suggested starting point – final day assignments and sequencing will be finalised jointly with I.EVO.

Track B · Capstone Structure

One capstone, flexible team

Every Track B participant completes one applied capstone, built on a real, sponsored task from their own work – solved individually, or as a group formed either within or outside their department, whichever fits the task best.

Individual

Completed solo, entirely by one participant, on a task they own.

Team – within department

3–4 participants from the same department take on one shared task together.

Team – outside department

3–4 participants spanning two or more departments team up on a shared task.

Where capstone ideas come from

Problem Register Opportunity Screening & Ranking HOD Consolidates & Assigns Track B Capstone Shortlist

Capstone tasks aren't picked freely by participants – they're drawn from the same ranked opportunities produced by Opportunity Screening. Each HOD consolidates their function's ranked list and hands Track B a curated set of sponsored opportunities to build from.

HOD role in Track B — RACI

A

Accountable

HODs are formally embedded in Track B, not just Track A

Each HOD is the Accountable owner of their function's capstones – consolidating and assigning the ranked opportunities, and signing off that submitted work maps to a real sponsored task. Champions are Responsible for day-to-day delivery support; Mellone is Consulted on rubric and evaluation; Founders stay Informed on outcomes.

Recognition is tied to involvement, not attendance – an HOD's engagement across their function's Track B capstones feeds into their own standing on the Belt Competency Ladder, not a side commitment sitting outside the incentive structure.

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. Baseline

Relevance & confidence per module / lab

Short pulse survey before and 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

Capstone evaluation

M6 capstone scored against a shared rubric, two-stage review

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-stage 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 I.EVO, and delivered as part of the per-cohort scorecard.

Track B · Capstone (M6) Evaluation Rubric

Two stages: hard gates that must all pass before scoring begins, then five weighted dimensions scored 1–4. Mellone will supply one sample capstone project, one sample submission and one sample evaluation to make the process clear.

Stage 1 – Eligibility Gates

Pass / fail · all four required before scoring

G1 · Task Authenticity (Champion) – real work, pre-approved before the capstone starts.

G2 · Submission Completeness (Agency) – standard format supplied in full.

G3 · Data-Rules Compliance (Agency) – any charter violation is an automatic Not Certified.

G4 · Prerequisite Assessment (Agency) – Level 2 pre/post assessment already passed.

Stage 2 – Scored Dimensions

1–4 scale · weights sum to 100%

Domain Correctness & Business Value
Champion

30%

Prompting Craft
Agency

20%

Verification & Human-in-the-Loop
Agency

20%

Data-Rules Compliance
Agency

15%

Efficiency & Time Impact
Champion + Agency

15%

Belt Competency Ladder

Status that means something Suggested — pending approval

Two tracks share one five-belt sequence – White, Yellow, Green, Blue, Black. White and Yellow are Track B-only; Green and Blue are shared. Black sits above Blue in both tracks – not a rung either track's structure guarantees, but a separate, discretionary tier.

Track A – Leadership

Track B – Practitioner

White

Track ANot part of the Leadership ladder

Track BEntry tierDepartment-level participants can start here – or higher, per HOD discretion.

Yellow

Track ANot part of the Leadership ladder

Track BIntermediate progress marker – attended the department's live, in-person AI Lab.

Green

Track ABVR completeCapstone submitted, BVR fully & honestly filled out – not a judgment on the result.

Track BBVR completeCapstone submitted, BVR fully & honestly filled out – not a judgment on the result.

Blue

Track ABVR passesThe value case itself clears the BVR threshold – not just completed, scored well.

Track BBVR passesThe value case itself clears the BVR threshold – not just completed, scored well.

Black

Track ABVR confirmedTop-band projection at submission, confirmed by Day-90 tracking – automatic, not nominated.

Track BBVR confirmedTop-band projection at submission, confirmed by Day-90 tracking – automatic, not nominated.

Green = quantified it honestly. Blue = the case is good. Black = it came true — awarded weeks after graduation once Day-90 tracking confirms it, not at close-out. Only discretion left anywhere on this ladder: an HOD's call on resource/time to attempt a tier, never on quality.

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 two departments, gradually the champions take over capstone scoring independently – Mellone steps back to moderation-on-request rather than moderating every submission.

At close: I.EVO 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.

Commercial Summary — Track B

Available as a follow-on phase — see annexure for the full curriculum.

Track B – Practitioner Course

Online

₹20L + GST

Covers all ~500 participants across all 6 departments — recorded curriculum, 6 days of in-person department AI Labs (one per department group), office hours, and evaluation.

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.

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

Thank you

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

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

hi@mellone.ai

www.mellone.ai

MELLONE