1. RFQ-to-Spec Engineering Copilot

Parses a spec-heavy RFQ, extracts standard, GSM, width and volume, matches it to qualified constructions and past project evidence, and hands the engineer a drafted technical response to approve. Target: Response time from days to hours, engineer hours per RFQ halved.

Core buildPrototypeNow

Business impact

91/100

RFQ response time

Build effort

48/100

Internal platform

Projected lift

+23%vs pre-agent baseline

RFQ response time

Time to value

≤ 90 days

From kickoff

Interactions / month

4,368+20%

Agent-handled

Automation rate

85%-6% human touches

No human in loop

Net value / year

₹2.4Cr

Revenue + cost avoided − run cost

Payback

7 months

Cumulative net positive

Adoption curve

Agent-handled volume vs pre-agent baseline

Jul104 peakDec

Baseline (manual process)

JulAugSepOctNovDec

Readiness scorecard

Six delivery dimensions

DataModelIntegrationAdoptionComplianceOps readiness

Outcome funnel

How rfq response time is produced

  • Eligible interactions4,917
  • Agent-handled3,51772%
  • Qualified outcome1,66747%
  • RFQ response time won79748%

Usage by geography

277
328
354
367
243
USAEUMiddle EastIndia defenceSE Asia

Cost mix

Share of run cost

48
  • Inference49%
  • Integration29%
  • Data prep24%
  • Change mgmt20%

Cohort retention

Share of users still using the product weekly

W1W2W3W4W6W8
Cohort 11089889747162
Cohort 21049882716755
Cohort 31089780726052
Cohort 41029588796757
Cohort 51069187756458

Performance by geography

Indexed 0–100 across five signals

VolumeAccuracyDeflectionCSATRevenue
USA8384787184
EU6669764075
Middle East8194483797
India defence5374687495

Model quality

Live vs target

MetricCurrentTarget30-dayStatus
Precision9190+3.1On target
Recall9085+4.4On target
Grounded-answer rate9495+1.8Closing gap
Escalation rate116-2.2On target
P95 latency (s)43-0.4On target
Hallucination flags / 1k11-0.9On target

Service levels

91%

Impact score

52%

Delivery ease

95%

Uptime (30 days)

Unit economics

₹ per year, indexed to pilot scale

  • Revenue influenced₹3.6Cr
  • Cost avoided₹1.3Cr
  • Run cost₹50L
  • Net value₹2.4Cr

Cumulative ROI by quarter

Q1140% peakQ4

Channel mix

  • Voice27%
  • WhatsApp37%
  • Web16%
  • Field app10%
  • Email9%

Language mix

2
  • English15%
  • Hindi11%

Segment performance

Volume, automation and win rate per market

MarketInteractionsAutomationRFQ response timeValueTrend
USA1,117
83%
24%₹67L
t0t1t2t3t4t5t6t7
EU1,208
69%
21%₹43L
t0t1t2t3t4t5t6t7
Middle East1,254
62%
33%₹71L
t0t1t2t3t4t5t6t7
India defence827
78%
25%₹85L
t0t1t2t3t4t5t6t7
SE Asia1,063
66%
35%₹52L
t0t1t2t3t4t5t6t7

Experiment log

What we tested on this product

ExperimentStatusResultOwner
Prompt v3 vs v2Won+10% rfq response timeAiera 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+7 CSAT ptsCX

Risk register

Owned mitigations before scale

RiskSeverityMitigationOwner
Data freshness in source systemsMediumHourly sync + staleness alertsData Eng
Regulatory language in outputsHighStandards claims (EN/NFPA)Compliance
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

    Product master, costing, capacity and lead times

    Live

  • Test-report repository

    EN, NFPA and ASTM certificates by construction

    Live

  • Salesforce CRM

    Pursuit creation, routing and win/loss capture

    Live

Product brief

Who uses it

Application engineers, technical sales

Metric it moves

RFQ response time

Why Arvind wins

A handful of senior engineers stop being the global bottleneck

Document parsingSpec matching over product libraryRAG on project archive

Guardrails

Applied to every response

  • Standards claims (EN/NFPA)
  • Export control
  • Technical data accuracy

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 USA with human review on every output.

Weeks 7–10

Automate the loop

Hand rfq response time decisions to the agent with escalation thresholds.

Weeks 11–14

Scale across lines

Roll out to 2 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, Protective wear and geography.

02

Ground the context

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

03

Reason and decide

Spec matching over product library scores the situation and picks the next best action — answer, quote, schedule, escalate — with a confidence threshold behind it.

04

Act in the workflow

RAG on project archive 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 rfq response time 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, RFQ inbox, Distributor network transcripts with intent, objection and sentiment labels.

    Streaming

  • Catalogue & pricing

    Protective wear · Composites · Industrial 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

    Application engineers, technical sales hits the moment this product exists for — response time from days to hours, engineer hours per rfq halved

  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 Standards claims (EN/NFPA), 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 rfq response time is attributed to this product.

Metric tree

What we steer and what we protect

North star

RFQ response time

Drivers

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

Guardrail metrics

  • Grounded-answer rate above 95%
  • Escalation rate inside agreed band
  • Standards claims (EN/NFPA)
  • 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 2-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
6 months at pilot volume
Scale unit
One geography × one Protective wear

Rollout waves

Scope and the exit test for each wave

Wave 1 — Prove

USA, Protective wear, human review on every output.

Exit test: RFQ response time beats manual baseline on 200+ conversations.

Wave 2 — Automate

Add EU 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 Advanced Materials
  • Approved language pack for English, Hindi
  • Telephony numbers and consent records for outbound
  • Sign-off on Standards claims (EN/NFPA)
  • Named market champions for floor adoption

Open questions for the business

Answer these to lock the scope

  • Which USA team runs the pilot and who signs off on outcomes?
  • How far back does usable conversation history go for Protective wear?
  • What confidence threshold is acceptable before the agent acts unattended?
  • Which existing report does rfq response time get compared against?
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