1. Asset Performance Intelligence

Live NOI per asset — rent roll, escalations, CAM recovery, energy cost and capex — with renewal risk scored per tenant and the lease decision modelled before the negotiation starts. Target: Renewal rate up 5-8 points and CAM leakage recovered within two quarters.

Core buildPrototypeNow

Business impact

90/100

NOI per sq ft

Build effort

52/100

Internal platform

Projected lift

+23%vs pre-agent baseline

NOI per sq ft

Time to value

≤ 90 days

From kickoff

Interactions / month

3,864+21%

Agent-handled

Automation rate

64%-5% human touches

No human in loop

Net value / year

₹2.4Cr

Revenue + cost avoided − run cost

Payback

11 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul92 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How noi per sq ft is produced

  • Eligible interactions4,716
  • Agent-handled3,61677%
  • Qualified outcome1,66646%
  • NOI per sq ft won63638%

Usage by geography

336
228
174
277
328
AhmedabadGandhinagarBengaluruPuneNRI - USA/GCC

Cost mix

Share of run cost

52
  • Inference46%
  • Integration26%
  • Data prep28%
  • Change mgmt13%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11059290777061
Cohort 21039687726257
Cohort 31039485695951
Cohort 41049189746963
Cohort 51079185776652

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
Ahmedabad4149839198
Gandhinagar8766807353
Bengaluru74581008088
Pune4859907787

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision8290+3.1Closing gap
Recall8585+4.4On target
Grounded-answer rate9595+1.8On target
Escalation rate126-2.2On target
P95 latency (s)23-0.4Closing gap
Hallucination flags / 1k21-0.9On target

Service levels

90%

Impact score

48%

Delivery ease

86%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹2.0Cr
  • Cost avoided₹1.5Cr
  • Run cost₹42L
  • Net value₹2.4Cr

Cumulative ROI by quarter

Q1110% peakQ4

Channel mix

  • Voice9%
  • WhatsApp9%
  • Web17%
  • Field app27%
  • Email37%

Language mix

5
  • English35%
  • Hindi36%
  • Gujarati22%
  • Kannada15%
  • Marathi26%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationNOI per sq ftValueTrend
Ahmedabad916
83%
28%₹77L
t0t1t2t3t4t5t6t7
Gandhinagar1,108
89%
23%₹88L
t0t1t2t3t4t5t6t7
Bengaluru754
72%
34%₹54L
t0t1t2t3t4t5t6t7
Pune577
83%
40%₹77L
t0t1t2t3t4t5t6t7
NRI - USA/GCC488
89%
43%₹48L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+11% noi per sq ftAiera Labs
Voice-first vs chat-firstRunning+6% so farGrowth Pod
Retrieval window 8k → 32kWon-6% escalationsPlatform
Human review threshold 0.7Rolled backno liftOps
Vernacular tone packRunning+5 CSAT ptsCX

Risk register

Owned mitigations before scale

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

  • SAP RE-FX

    Lease contracts, escalations, billing and arrears

    Live

  • BMS / IoT metering

    Utilisation, HVAC and energy consumption per floor

    In build

  • Yardi / property management

    Work orders, CAM reconciliation and tenant tickets

    Live

  • Market comps feed

    Micro-market rents and vacancy for benchmarking

    Live

Product brief

Who uses it

Asset managers, CFO, leasing heads

Metric it moves

NOI per sq ft

Why Arvind wins

Occupancy defended before notice periods, not after

Rent-roll data modelRenewal risk modelNOI scenario engine

Guardrails

Applied to every response

  • RERA registration numbers
  • No assured-return claims
  • Broker attribution rules
  • 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 noi per sq ft 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, Residential and geography.

02

Ground the context

Rent-roll data model indexes the 3 Arvind product sets, pricing rules and policy language so nothing is answered from memory alone.

03

Reason and decide

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

04

Act in the workflow

NOI scenario engine 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 noi per sq ft 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, Broker network, Portals, Site walk-in transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Residential · Commercial · Services 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

    Asset managers, CFO, leasing heads hits the moment this product exists for — renewal rate up 5-8 points and cam leakage recovered within two quarters

  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 registration numbers, 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 noi per sq ft is attributed to this product.

Metric tree

What we steer and what we protect

North star

NOI per sq ft

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 registration numbers
  • 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 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
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 Residential

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

Ahmedabad, Residential, human review on every output.

Exit test: NOI per sq ft beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add Gandhinagar 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 Real Estate
  • Approved language pack for English, Hindi, Gujarati, Kannada, Marathi
  • Telephony numbers and consent records for outbound
  • Sign-off on RERA registration numbers
  • 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 Residential?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does noi per sq ft get compared against?
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