2. Aiera Sales Gallery Concierge

A voice-and-screen concierge at the site office that qualifies the walk-in, runs the masterplan and inventory in the buyer's language, holds a unit and books the next action before a manager is free. Target: 60% of walk-ins qualified, priced and booked without waiting for a manager.

Quick winPilotNow

Business impact

89/100

Walk-in to booking rate

Build effort

38/100

Customer-facing

Projected lift

+22%vs pre-agent baseline

Walk-in to booking rate

Time to value

≤ 90 days

From kickoff

Interactions / month

3,570+13%

Agent-handled

Automation rate

84%-5% human touches

No human in loop

Net value / year

₹32L

Revenue + cost avoided − run cost

Payback

6 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul87 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How walk-in to booking rate is produced

  • Eligible interactions5,006
  • Agent-handled3,70674%
  • Qualified outcome1,65645%
  • Walk-in to booking rate won66640%

Usage by geography

90
-25
47
83
-29
AhmedabadBengaluruPuneSuratNRI - GCC

Cost mix

Share of run cost

38
  • Inference48%
  • Integration13%
  • Data prep16%
  • Change mgmt1%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11079885786562
Cohort 21069687726253
Cohort 31019779746050
Cohort 41079587756663
Cohort 51039387726256

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Ahmedabad9678975792
Bengaluru5060615168
Pune9041439957
Surat7337549053

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision8490+3.1Closing gap
Recall6385+4.4Closing gap
Grounded-answer rate8195+1.8Closing gap
Escalation rate16-2.2Closing gap
P95 latency (s)03-0.4Closing gap
Hallucination flags / 1k-21-0.9Closing gap

Service levels

89%

Impact score

62%

Delivery ease

88%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹2.3Cr
  • Cost avoided₹-7L
  • Run cost₹2L
  • Net value₹32L

Cumulative ROI by quarter

Q1123% peakQ4

Channel mix

  • Voice49%
  • WhatsApp16%
  • Web-11%
  • Field app20%
  • Email27%

Language mix

5
  • English1%
  • Hindi-11%
  • Gujarati-17%
  • Kannada-5%
  • Marathi-14%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationWalk-in to booking rateValueTrend
Ahmedabad-490
30%
-7%₹3L
t0t1t2t3t4t5t6t7
Bengaluru-45
22%
5%₹11L
t0t1t2t3t4t5t6t7
Pune-273
38%
-3%₹-25L
t0t1t2t3t4t5t6t7
Surat63
46%
-7%₹-43L
t0t1t2t3t4t5t6t7
NRI - GCC231
50%
-9%₹-52L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+10% walk-in to booking 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+3 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighRERA disclosureCompliance
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

  • Salesforce CRM

    Lead create, hold, site-visit and booking write-back

    Live

  • Inventory & pricing engine

    Live availability, base price, floor-rise and offer rules

    Live

  • DeployOne Connect telephony

    Inbound and callback in eight languages

    Live

  • Home-loan partner APIs

    Indicative FOIR/LTV eligibility during the conversation

    In build

Product brief

Who uses it

Walk-in buyers, sales managers

Metric it moves

Walk-in to booking rate

Why Arvind wins

Grounded on live inventory and approved RERA language, so it can commit without a human

Voice LLM with tool callingLive inventory + hold APIRERA-safe language guardrails

Guardrails

Applied to every response

  • RERA disclosure
  • No price commitment on call
  • DND scrubbing
  • Call recording consent

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 Ahmedabad with human review on every output.

Weeks 7–10

Automate the loop

Hand walk-in to booking rate decisions to the agent with escalation thresholds.

Weeks 11–14

Scale across lines

Roll out to 5 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, Plotted development and geography.

02

Ground the context

Voice LLM with tool calling indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

Live inventory + hold API scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

RERA-safe language guardrails 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 walk-in to booking 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, Portal leads, Channel partners, Walk-in transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Plotted development · Villas & row houses · Apartments 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

    Walk-in buyers, sales managers hits the moment this product exists for — 60% of walk-ins qualified, priced and booked without waiting for a manager

  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 RERA disclosure, 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 walk-in to booking rate is attributed to this product.

Metric tree

What we steer and what we protect

North star

Walk-in to booking rate

Drivers

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

Guardrail metrics

  • Grounded-answer rate above 95%
  • Escalation rate inside agreed band
  • RERA disclosure
  • 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 5-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
8 months at pilot volume
Scale unit
One geography × one Plotted development

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

Ahmedabad, Plotted development, human review on every output.

Exit test: Walk-in to booking rate beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add Bengaluru 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 5 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 SmartSpaces
  • Approved language pack for English, Hindi, Gujarati, Kannada, Marathi
  • Telephony numbers and consent records for outbound
  • Sign-off on RERA disclosure
  • Named market champions for floor adoption

Open questions for the business

Answer these to lock the scope

  • Which Ahmedabad team runs the pilot and who signs off on outcomes?
  • How far back does usable conversation history go for Plotted development?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does walk-in to booking rate get compared against?
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