3. Opex-per-KL Proposal Engine

Takes flow, TDS, power tariff and effluent profile and returns opex per KL against the plant's current spend, with capex, rental and BOOT options priced in the same conversation. Target: Proposal in the first meeting instead of two weeks later.

Quick winPilotNow

Business impact

83/100

Capex objection win rate

Build effort

38/100

Customer-facing

Projected lift

+21%vs pre-agent baseline

Capex objection win rate

Time to value

≤ 90 days

From kickoff

Interactions / month

4,032+20%

Agent-handled

Automation rate

87%-8% human touches

No human in loop

Net value / year

₹-23L

Revenue + cost avoided − run cost

Payback

6 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul103 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How capex objection win rate is produced

  • Eligible interactions4,379
  • Agent-handled3,27975%
  • Qualified outcome1,82956%
  • Capex objection win rate won77943%

Usage by geography

3
61
90
105
-18
GujaratMaharashtraTamil NaduMiddle EastAfrica

Cost mix

Share of run cost

38
  • Inference51%
  • Integration8%
  • Data prep12%
  • Change mgmt3%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11059687786655
Cohort 21049187726251
Cohort 31019284735947
Cohort 41049988756858
Cohort 51009387776756

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Gujarat9968508839
Maharashtra5974507035
Tamil Nadu73651004336
Middle East4342944740

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision9390+3.1On target
Recall7185+4.4Closing gap
Grounded-answer rate8795+1.8Closing gap
Escalation rate56-2.2Closing gap
P95 latency (s)23-0.4Closing gap
Hallucination flags / 1k01-0.9Closing gap

Service levels

83%

Impact score

62%

Delivery ease

97%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹3.4Cr
  • Cost avoided₹5L
  • Run cost₹5L
  • Net value₹-23L

Cumulative ROI by quarter

Q1148% peakQ4

Channel mix

  • Voice50%
  • WhatsApp-500%
  • Web-200%
  • Field app300%
  • Email450%

Language mix

4
  • English-17%
  • Hindi-20%
  • Gujarati-21%
  • Tamil8%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationCapex objection win rateValueTrend
Gujarat-217
36%
12%₹-55L
t0t1t2t3t4t5t6t7
Maharashtra-359
25%
15%₹-58L
t0t1t2t3t4t5t6t7
Tamil Nadu-430
20%
16%₹-19L
t0t1t2t3t4t5t6t7
Middle East-15
17%
3%₹0L
t0t1t2t3t4t5t6t7
Africa-258
16%
10%₹-30L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+10% capex objection win rateAiera Labs
Voice-first vs chat-firstRunning+2% so farGrowth Pod
Retrieval window 8k → 32kWon-0% escalationsPlatform
Human review threshold 0.7Rolled backno liftOps
Vernacular tone packRunning+4 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighPollution-board normsCompliance
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

  • Process design library

    Validated unit-process cost and chemical dosing models

    Live

  • Power tariff & chemical price feeds

    State tariffs and consumable rates kept current

    In build

  • Salesforce CRM

    Proposal versions, approvals and win/loss

    Live

Product brief

Who uses it

Plant heads, CFOs of industrial customers, sales

Metric it moves

Capex objection win rate

Why Arvind wins

Kills the capex objection with the customer's own numbers

Process cost modelScenario simulationProposal generation

Guardrails

Applied to every response

  • Pollution-board norms
  • Tender confidentiality
  • Performance-guarantee wording

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 Gujarat with human review on every output.

Weeks 7–10

Automate the loop

Hand capex objection win rate decisions to the agent with escalation thresholds.

Weeks 11–14

Scale across lines

Roll out to 4 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, Treatment and geography.

02

Ground the context

Process cost model indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

Scenario simulation scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

Proposal generation 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 capex objection win 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, Tender portals, Consultants transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Treatment · Services · Retrofits 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 5 geographies via DeployOneSense.

    Daily

Customer & team journey

  1. 1Trigger

    Plant heads, CFOs of industrial customers, sales hits the moment this product exists for — proposal in the first meeting instead of two weeks later

  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 Pollution-board norms, 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 capex objection win rate is attributed to this product.

Metric tree

What we steer and what we protect

North star

Capex objection win rate

Drivers

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

Guardrail metrics

  • Grounded-answer rate above 95%
  • Escalation rate inside agreed band
  • Pollution-board norms
  • 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

    0.3 FTE

  • Conversation designer

    Prompts, tone and 4-language scripts

    1 FTE

  • Data / integration engineer

    CRM, ERP and telephony wiring

    1 FTE

  • ML engineer

    Scoring models, evaluation harness, drift

    0.5 FTE

  • Ops champion per market

    Floor adoption across 5 geographies

    0.2 FTE each

Commercials

How the product pays for itself

Delivery model
Customer-facing
Cost drivers
Inference volume, telephony minutes, integration upkeep
Charge basis
Per handled conversation
Break-even
7 months at pilot volume
Scale unit
One geography × one Treatment

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

Gujarat, Treatment, human review on every output.

Exit test: Capex objection win rate beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add Maharashtra 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 5 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 Envisol
  • Approved language pack for English, Hindi, Gujarati, Tamil
  • Telephony numbers and consent records for outbound
  • Sign-off on Pollution-board norms
  • Named market champions for floor adoption

Open questions for the business

Answer these to lock the scope

  • Which Gujarat team runs the pilot and who signs off on outcomes?
  • How far back does usable conversation history go for Treatment?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does capex objection win rate get compared against?
← Back to portfolio