Innovation Lab

Agent experiments for Arvind Fashions — from idea to shipped, measured against pilot baselines.

Lab ideas

6

Tailored to this business

Avg impact

78/100

Weighted on revenue lift

Experiments running

4

Building, piloting or shipped

Shipped

1

Live in production

Impact vs effort

Where to spend the next sprint

Quick winsBig bets
Impact ↑Effort →

Horizon split

6
  • Now3
  • Next2
  • Later1

Experiment pipeline

  • Idea33%
  • Building0%
  • Piloting50%
  • Shipped17%

Technology building blocks

Most reused across lab ideas

1
1
1
1
1
1
1
1
Purchase-historyBrandReturn/exchangeChurnMargin-awareGenerativeOn-deviceReal-time

AI product concepts

5 platform bets for Arvind Fashions

Cross-Brand Fit Twin

Prototype

One fit profile per customer that travels across U.S. Polo Assn., Arrow, Tommy Hilfiger and Calvin Klein, so the size is right the first time and the return never happens.

For Shoppers across all brands, ecommerce, planning

Fit embeddingsCross-brand size graphReturn-reason feedback loop

Edge: Only the portfolio owner can normalise fit across four brands and every channel

Drop & Allocation Intelligence

Concept

Predicts sell-through by SKU, size and city in week one and reallocates stock between stores, warehouses and marketplaces before markdown season decides for you.

For Merchandising, planning, supply chain

Early-signal forecastingAllocation optimiserMarkdown engine

Edge: Margin recovered at full price, where the money actually is

Store Clienteling OS

Pilot

Every associate opens the day with a ranked list: who to message, what to suggest, what is in stock in this door or the next one — sent on WhatsApp, attributed back to the store.

For 1,300+ store associates, retail ops

Propensity modelsInventory graphWhatsApp clienteling

Edge: Store-level customer data no marketplace ever sees

Generative Campaign Studio

Pilot

Brand-safe imagery, reels and copy per drop, per city, per language — generated in hours, guardrailed against brand and licensor rules, and re-ranked by what actually performs.

For Brand marketing, ecommerce merchandising

Image and video generationBrand + licensor guardrailsPerformance feedback loop

Edge: Licensed brands need approval-safe creative at speed — this is both

Trend-to-Tech-Pack Engine

Concept

Turns runway, search and marketplace trend signals into a costed line plan and a tech pack draft, so a concept reaches the vendor in days rather than weeks.

For Design, sourcing, merchandising

Trend signal miningGenerative design boardsCosting + tech-pack drafting

Edge: Design speed backed by Arvind's own fabric and vendor base

Idea catalogue

6 plays designed for Arvind Fashions

Size & fit deflection agent

Now

Size and return questions are the single biggest contact driver and the biggest return cause.

What we build: Fit agent that reads past purchase history and brand-level size charts to recommend a size, with an exchange flow that never needs an agent.

Purchase-history embeddingsBrand size-chart graphReturn/exchange APIs
Aiera ConverseAiera Context
Impact
Effort

Tracks Contacts deflected · payback 4 weeks

Lapsed loyalty win-back engine

Now

Millions of loyalty members go quiet after one season and get the same blast as everyone else.

What we build: Churn-propensity scoring per member with a personalised win-back offer generated per brand, budget-capped by margin.

Churn propensity modelMargin-aware offer optimiserGenerative copy per brand voice
Aiera PredictAiera Engage
Impact
Effort

Tracks Reactivated members / month · payback 6 weeks

Store-associate co-pilot

Next

Associates across 1,300+ stores can't see stock elsewhere or recall the current offer grid.

What we build: Voice-first co-pilot on the store tablet: stock locator, offer rules, clienteling prompts and one-tap ship-from-store.

On-device speechReal-time inventory graphClienteling recommendations
Aiera ConnectAiera Context
Impact
Effort

Tracks Sales saved from stock-outs · payback 9 weeks

Drop-day broadcast orchestrator

Now

New drops go out as one blast; fatigue rises and opt-outs follow.

What we build: Segment-aware WhatsApp broadcast that staggers sends by predicted responsiveness and auto-stops on saturation.

Send-time optimisationFatigue modellingConsent ledger
Aiera EngageAiera Dash
Impact
Effort

Tracks Revenue per broadcast · payback 3 weeks

Gifting concierge

Next

Festive gifting shoppers need curation, not a catalogue, and abandon at choice overload.

What we build: Conversational concierge that asks three questions and returns a gift shortlist across brands with delivery-date confidence.

Preference elicitation LLMCross-brand catalogue rankingDelivery ETA model
Aiera ConverseAiera Predict
Impact
Effort

Tracks Festive AOV · payback 7 weeks

Returns-abuse & quality signal detector

Later

Repeat return behaviour and product defects hide inside free-text return reasons.

What we build: Classify every return reason, flag defect clusters to sourcing and score abusive return patterns for policy action.

Text classificationAnomaly detectionQuality feedback loop
Aiera SenseAiera Brain
Impact
Effort

Tracks Return rate · payback 12 weeks

Idea

2 experiments

  • Size-exchange deflection

    If we deploy size-exchange deflection, response time improves measurably within 30 days.

    +19%Fatima Nair

  • Drop-day WhatsApp broadcast agent

    If we deploy drop-day whatsapp broadcast agent, response time improves measurably within 30 days.

    +15%Anita Patel

Building

0 experiments

    Piloting

    3 experiments

    • Store-associate co-pilot

      If we deploy store-associate co-pilot, response time improves measurably within 30 days.

      +30%Meera Desai

    • Lapsed-customer revival

      If we deploy lapsed-customer revival, retention improves measurably within 30 days.

      +13%Anita Nair

    • Stock-locator across stores

      If we deploy stock-locator across stores, response time improves measurably within 30 days.

      +13%Rahul Kulkarni

    Shipped

    1 experiments

    • Gifting concierge for festive season

      If we deploy gifting concierge for festive season, retention improves measurably within 30 days.

      +13%Vivek Rao