3. Vision QA & Defect Intelligence

Vision models classify defects at end-of-line and in the finishing audit, cluster them by line, operator and operation, and escalate the repeat cause before the buyer claim arrives. Target: Rejections down 25% and rework hours cut per million SAM.

Core buildPrototypeNext

Business impact

84/100

Rejection rate

Build effort

56/100

Internal platform

Projected lift

+21%vs pre-agent baseline

Rejection rate

Time to value

2 quarters

From kickoff

Interactions / month

4,158+18%

Agent-handled

Automation rate

75%-6% human touches

No human in loop

Net value / year

₹15L

Revenue + cost avoided − run cost

Payback

6 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul99 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How rejection rate is produced

  • Eligible interactions5,087
  • Agent-handled3,48769%
  • Qualified outcome1,73750%
  • Rejection rate won64737%

Usage by geography

-29
-85
-113
-127
EthiopiaIndiaUSA buyersEU buyers

Cost mix

Share of run cost

56
  • Inference39%
  • Integration22%
  • Data prep4%
  • Change mgmt10%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11069689817155
Cohort 21079585746158
Cohort 31049180695949
Cohort 41019389746860
Cohort 51089180706153

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Ethiopia9153945444
India7174474184
USA buyers7153756142
EU buyers6552774645

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision9390+3.1On target
Recall6885+4.4Closing gap
Grounded-answer rate8695+1.8Closing gap
Escalation rate36-2.2Closing gap
P95 latency (s)03-0.4Closing gap
Hallucination flags / 1k-11-0.9Closing gap

Service levels

84%

Impact score

44%

Delivery ease

97%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹2.1Cr
  • Cost avoided₹3L
  • Run cost₹-11L
  • Net value₹15L

Cumulative ROI by quarter

Q1133% peakQ4

Channel mix

  • Voice35%
  • WhatsApp-2%
  • Web15%
  • Field app18%
  • Email32%

Language mix

3
  • English-19%
  • Hindi-6%
  • Amharic-14%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationRejection rateValueTrend
Ethiopia-409
30%
1%₹-27L
t0t1t2t3t4t5t6t7
India-5
42%
-5%₹-44L
t0t1t2t3t4t5t6t7
USA buyers-253
48%
6%₹-52L
t0t1t2t3t4t5t6t7
EU buyers73
31%
-2%₹-16L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+8% rejection rateAiera Labs
Voice-first vs chat-firstRunning+-1% so farGrowth Pod
Retrieval window 8k → 32kWon--1% escalationsPlatform
Human review threshold 0.7Rolled backno liftOps
Vernacular tone packRunning+2 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighBuyer code of conductCompliance
Field-team adoptionMediumIn-app nudges + weekly coaching drillsSales Ops
Model cost driftLowRouter to smaller models for routine intentsPlatform

Where it sits in the portfolio

Tap another dot to switch product

Quick winsBig bets
Impact ↑Effort →

Systems it touches

  • Line cameras / edge devices

    Inline capture at sewing and finishing

    In build

  • QMS / AQL audit system

    Defect codes, audit results and disposition

    Live

  • MES

    Line, operator and operation attribution

    Live

Product brief

Who uses it

Quality heads, industrial engineering, buyers

Metric it moves

Rejection rate

Why Arvind wins

Patterns visible in hours instead of after the claim lands

Vision defect classificationClustering + root causeEscalation workflow

Guardrails

Applied to every response

  • Buyer code of conduct
  • Audit trail
  • Labour compliance statements

Delivery plan

14-week path to production

Weeks 1–3

Ground the data

Connect 3 product sets, historical conversations and CRM outcomes.

Weeks 4–6

Pilot with one team

Run in Ethiopia with human review on every output.

Weeks 7–10

Automate the loop

Hand rejection rate decisions to the agent with escalation thresholds.

Weeks 11–14

Scale across lines

Roll out to 3 languages and all geographies.

How it works

End-to-end, from signal to learned outcome

01

Capture the signal

Every Voice and WhatsApp interaction, plus CRM and order events, is streamed in and tagged to a customer, Bottoms and geography.

02

Ground the context

Vision defect classification indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

Clustering + root cause scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

Escalation workflow writes back into CRM/ERP and fires the customer-facing action in English or Hindi, so the team sees it where they already work.

05

Learn from outcome

Won/lost and rejection rate outcomes flow back nightly; prompts, thresholds and routing are retuned every fortnight.

Data it runs on

Sources, contents and refresh cadence

  • Conversation history

    Voice, WhatsApp, Buyer portal, Vendor calls transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Bottoms · Tops · Programs with specs, availability and approved commercial language.

    Hourly

  • CRM outcomes

    Stage moves, win/loss reasons and owner activity per enquiry.

    15 min

  • Operations systems

    ERP order, stock, dispatch and service records used to answer status questions.

    Hourly

  • Market signals

    Demand and competitor movement across 4 geographies via DeployOneSense.

    Daily

Customer & team journey

  1. 1Trigger

    Quality heads, industrial engineering, buyers hits the moment this product exists for — rejections down 25% and rework hours cut per million sam

  2. 2Agent acts

    Aiera responds within seconds on Voice, grounded in the live catalogue and the customer's own history.

  3. 3Human in the loop

    Anything below the confidence threshold, or touching Buyer code of conduct, routes to a named owner with full context attached.

  4. 4Close the loop

    Outcome is written back, the customer gets a summary on WhatsApp, and rejection rate is attributed to this product.

Metric tree

What we steer and what we protect

North star

Rejection rate

Drivers

  • Agent-handled share of eligible interactions
  • First-response time and resolution time
  • Qualified outcomes per 100 conversations in Ethiopia
  • Repeat engagement within 30 days

Guardrail metrics

  • Grounded-answer rate above 95%
  • Escalation rate inside agreed band
  • Buyer code of conduct
  • Run cost per conversation below target

Squad to build it

Who is needed and for what

  • Product owner (Arvind)

    Scope, adoption and the business case

    0.5 FTE

  • Conversation designer

    Prompts, tone and 3-language scripts

    1 FTE

  • Data / integration engineer

    CRM, ERP and telephony wiring

    2 FTE

  • ML engineer

    Scoring models, evaluation harness, drift

    0.5 FTE

  • Ops champion per market

    Floor adoption across 4 geographies

    0.2 FTE each

Commercials

How the product pays for itself

Delivery model
Internal platform
Cost drivers
Inference volume, telephony minutes, integration upkeep
Charge basis
Per market licence + usage
Break-even
5 months at pilot volume
Scale unit
One geography × one Bottoms

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

Ethiopia, Bottoms, human review on every output.

Exit test: Rejection rate beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add India and Hindi; agent acts without review above threshold.

Exit test: Escalation rate under 10% for four straight weeks.

Wave 3 — Extend

All 3 product sets and 4 channels, wired into CRM reporting.

Exit test: Adoption above 70% of eligible interactions.

Wave 4 — Compound

Roll across 4 geographies and share the model with sibling Arvind businesses.

Exit test: Net value positive at run-rate; owned by business-as-usual team.

Dependencies to unblock

Needed before wave 1 starts

  • API access to CRM and ERP for Arvind Garmenting
  • Approved language pack for English, Hindi, Amharic
  • Telephony numbers and consent records for outbound
  • Sign-off on Buyer code of conduct
  • Named market champions for floor adoption

Open questions for the business

Answer these to lock the scope

  • Which Ethiopia team runs the pilot and who signs off on outcomes?
  • How far back does usable conversation history go for Bottoms?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does rejection rate get compared against?
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