Isometric render of a smart city catchment with geospatial location markers over stores, streets and autonomous vehicles
Special ProjectAgentic Geospatial IntelligenceReliance Retail

RetailVista.

Reliance Retail's enterprise geospatial intelligence layer. Predicts what a catchment can become. 3 tracks, 24 data categories, 10-dimension scoring on a shared H3 hex index. 68 live opportunities.

Live · Internal Track · Opportunity Explorer on grocery NSO leadsPilot · Google Track · MVP demoed end-to-end on Mumbai catchmentsBuilding · JioGIS data unlock, Foundation modelRoadmap · 1000-city scale
StatusInternal Track live01-May-2026

3

tracks running

68

live opportunities

10

LI dimensions

24

data categories

Retail Vista in action

Status

What's running. What's being built. What's next.

Three tracks, as of 01-May-2026.

Live

Internal Track · UCP / JioMart

Opportunity Explorer.

Built in-house on UCP design language. Anchored on JioMart BAU production data aggregated at hex level. Natural-language workspaces score catchments, flag cannibalisation risk, propose intervention zones. End-to-end agentic decisioning: agents, data and reasoning in one runtime.

Pilot

Google Track · Joint MVP

Agentic Site Intelligence.

Joint build with Google on Vertex AI, BigQuery, Street View and Maps. Cannibalisation, feasibility and site-selection agents validated end-to-end on live Mumbai catchments. Fynd owns Product, requirements, CUJ evaluation and output-quality feedback. Google owns infrastructure and agent orchestration.

Building

JioGIS Track

Data unlock + Foundation model.

Drives unlock of JioGIS layers and Reliance retail datasets. Foundation model development initiated for the spatial reasoning core. Kentrix MMR procurement anchored here as shared enrichment layer that feeds all three tracks once secured.

Opportunity

Modules running today.

Internal Track surfaces deployed on UCP design language. Each module is the Opportunity Explorer flow that takes a catchment from a scored signal to a pipeline decision. Live as of 01-May-2026 on real grocery NSO leads, aggregated on JioMart data.

ModuleStatusCapability
Home · Command Center
Live
KPI summary, Opportunity Intelligence (Comp. Gaps, Whitespace, Active Leads, Approved), Top Opportunities, Leads, Alerts Requiring Attention, Store Rollout Velocity vs target
Explorer · Discovery + Analysis
Live
Filterable scored opportunity list (Site / LI / Whitespace / Competitor sub-scores), synchronised Google Maps, Quick Summary, Full Analysis with 10 LI dimensions, Discover full-screen map, Pipeline Kanban (New → Shortlisted → Site Visit → Approved)
Workspace · AI Co-Pilot
Live
Multi-turn chat for feasibility, catchment, drive-time, brand-network analysis; structured AI outputs (demographics, accessibility, competitor landscape, brand assets); Google Maps + MapLibre engine toggle; POI dataset and clustering layers
Users · Access Management
Live
User table, roles, status, modules, last login, invite flow, permission management
Data & Scoring layer
Live
10 LI dimensions (GDP, Demographics, Accessibility, Property Rates, Wealth, Footfall, Spending, Order Data, Building Density, Competition), 4 sub-scores into 0–100 composite, Confidence and Priority labels (High / Medium / Low)

What operators see.

Six surfaces from the Internal Track. Each catchment is scored across 10 Location-Intelligence dimensions; sub-scores roll into a 0–100 composite with Confidence and Priority labels.

RetailVista Home — Command Center with Opportunity Intelligence panel and Top Opportunities list

Home · Command Center — 68 opportunities, 0 approved, 0 shortlisted. Opportunity Intelligence panel (3 Comp. Gaps, 65 Whitespace). Top Opportunities, Leads feed, Alerts, Store Rollout Velocity vs Q2 FY26 target.

Explorer — scored opportunity list with synchronised Google Maps

Explorer · scored list + map — filterable opportunity list synchronised with Google Maps. Category and vertical filters. Quick Summary popup on map markers.

Explorer — Full Analysis with 10 LI dimensions and composite score breakdown

Full Analysis · 10 LI dimensions — Goregaon East, Score 69, Medium Confidence. Composite 62/100 across GDP, Demographics, Accessibility, Property Rates, Wealth, Footfall, Spending, Order Data, Building Density, Competition.

Explorer — Pipeline Kanban with New, Shortlisted, Site Visit, Approved, Dismissed columns

Pipeline · Kanban — five-stage pipeline (New, Shortlisted, Site Visit, Approved, Dismissed). The decision system-of-record tying every scored opportunity to a real-world outcome the agents learn from.

Workspace — AI co-pilot with feasibility analysis output for Andheri

Workspace · AI Co-Pilot — multi-turn chat. Output: ranked hex IDs with rwi_mean, Total POIs, epoch_mean, height_mean. Strategic rationale and contrast analysis.

Users — access management table

Users · access management — member table, role, status, modules, last login. Five active members across the Internal Track build team today.

Path to L4

How RetailVista becomes L4 agentic.

Currently at L3. Three parallel tracks close the gap to L4.

TrackStateTarget rungWhat this track unlocks
Internal · UCP / JioMart
Live
L4
Opportunity Explorer, 68 scored leads, agentic decisioning end-to-end.
Google · Joint MVP
Pilot
L4
Cannibalisation, feasibility and site-selection agents on Vertex AI, Mumbai catchments.
JioGIS
Building
L4
Spatial reasoning core, Foundation model, feeds all 3 tracks once secured.
Architecture

From raw signals to activated decision.

Five layers: Sources, Spatial Aggregation, GIS Visualisation, Agentic Orchestration, Activation. Agents read live signals, run guarded playbooks, write outcomes back for continuous learning.

Live

Layer 01 · Sources

24 categories.

UCP, JioGIS, Retail store master, Customer locations/demographics/behaviour/intent, Government data (Census, NCRB, RBI, RERA, NFHS), third-party POI (Kentrix).

Live

Layer 02 · Aggregation

H3 hex index.

Hex-first aggregation, no PII — events snapped to H3 cells; only aggregated metrics per hex reach models. One spatial truth, many zooms (resolution 7, 5.16 km²; resolution 9, 0.10 km²).

Pilot

Layer 03 · GIS visualisation

Maps + heat overlays.

Per-hex score colouring on Mumbai catchments today. Google Maps + MapLibre engine toggle. Street View for last-mile. Pan-India per-hex Attractability heat is in build.

Mixed

Layer 04 · Agentic orchestration

Agent skills.

Six skills mapped: 3 Live (New Store Opening, Catchment Analysis, Dark-store Drive Time), 1 Building (Customer Sentiment), 2 Roadmap (Pricing Promotion, Land Parcel, Transport Optimisation).

Building

Layer 05 · Activation

Hand-off surfaces.

Outputs to ALP, Granary, JioMart routing, brand-team workflows. New stores, Pricing, SCM Optimisation, Customer Listening as the activation cells from the cornerstone deck.

RetailVista architecture — Sources to Ingest, Transform, Process, Serve, Analyze to Use Cases

Architecture diagram — five stages from source to outcome. Sources (UCP, JioGIS, Customer Listening, Google Earth, Reliance datasets, Kentrix, Government, Third-party) through Ingest, Transform/Process/Serve, Analyze, to Use Cases (New Stores, SCM, Customer Listening, Pricing/Promotions, Land Parcels, TAM, Inventory).

Data

Every signal that lands on the hex.

The shared data inventory feeding all three tracks. Reliance proprietary (UCP, store master), Jio (GIS, telco), Government (Census, NCRB, RBI, RERA, NFHS, SECC) and third-party POI (Kentrix).

CategorySourceRefreshGranularityVariables
01 · Land base & public dataset
JioGIS
As-is
Lat / Long
36 states, 105M buildings, 137M households, 21.7M POIs, 0.6M villages, 25.6K cities, 3.8M km
02 · Buildings master (Residential / Commercial)
JioGIS
As-is
Lat / Long
1.3M km Fiber, 0.3M eNodeB OnAir
03 · Owned Retail facilities
Retail store master
Daily
Lat / Long
40,000+
04 · Customer locations
UCP · Jio
Real-time
Lat / Long
500M+
05 · Customer demographics
UCP · Jio
Real-time
Lat / Long
20+
06 · Customer digital behaviour
UCP · Jio device
Daily
Lat / Long
10+
07 · Customer purchase intent
UCP transactional
Real-time
Lat / Long
150+
08 · Customer interests & propensities
UCP · Jio · Media inferred
Daily
Lat / Long
700+
09 · Civic Infrastructure
Govt (Mission Antodaya, ODP)
As-is
200m – 1km
240+
10 · Commercial · Services
POI (Kentrix)
30 days
Lat / Long
38
11 · Commercial Retail
POI (Kentrix)
30 days
Lat / Long
110+
12 · Crime statistics
Govt (NCRB)
As-is
500m
110+
13 · Demography
Govt (SECC, Census) + GeoIQ
180 days
200m – 2000m
230+
14 · Environment
Govt (Aridity, IMD)
As-is
200m – 500m
2
15 · Finance
POI (RBI data)
30 days
Lat / Long
23
16 · GeoIQ Indices
GeoIQ engineered
180 days
500m
16
17 · Geographical
GeoIQ engineered
180 days
Region
4
18 · Healthcare
Govt (NFHS, Census, SECC) + POI
As-is / 30 days
200m – 1000m
140+
19 · Infrastructure
POI (Kentrix), OSM, Public
30 days / 1 yr
Lat/Long, 200m – 1000m
80+
20 · Leisure & Hospitality
POI (Kentrix)
30 days
Lat / Long
24
21 · MSME
Govt (third-party)
3 months
500m
100+
22 · Mobility & Footfall
Third-party
30 days
Hex 8
20
23 · Real Estate
Govt (RERA), Public listings
30 / 90 days
500m, Lat/Long
3+
24 · Socio-economic
Govt (SECC, Census) + GeoIQ
180 days
200m – 2000m
900+
RetailVista Platform Layers — five-tier diagram

Platform Layers v0.1 — Sources (UCP + Customer Listening, JioGIS, External/Public), Spatial Aggregation (H3 hex index), GIS Visualisation, Agentic Orchestration, Activation.

H3 hex visualisation — Mumbai Attractability scoring with Ghatkopar popup

H3 hex visualisation, Mumbai — per-hex Attractability score (0–100) across MMR. Sample: Ghatkopar 53/100, Jio Penetration 43%, Competitors 4, Cannibalisation Risk 9%.

Vision

Where RetailVista is going.

A pan-India geospatial backbone running across 1000 cities, weighted on Google's agentic stack, built around per-household Digital Twins and the full network reference for every Reliance operation.

Roadmap

01 · Scale

1000 cities.

Pan-India spatial coverage at city level. Today's pilot is anchored on Mumbai across the Internal Track and Google MVP. Scale gates on JioGIS data unlock, Kentrix MMR enrichment, and foundation-model maturation.

Pilot

02 · Primary surface

Google as the pan-India agentic surface.

Engineering and partnership weight shifts to the Google joint track. Cannibalisation, feasibility and site-selection agents already validated end-to-end on Mumbai catchments. The Internal Track continues as the agentic capability benchmark.

Building

03 · Foundation

Full-stack geospatial foundation.

A 12-layer foundation platform spanning sources, aggregation, GIS, agentic orchestration and activation. The current 5-layer architecture is v0.1; the full stack closes the gap between raw signal and agent-ready decisioning.

Building

04 · Operating model

Joint execution, dedicated org.

A dedicated RetailVista organisation operating across Reliance and Fynd. Engineering integration with Google folds into a single execution plan. Agent skills callable across every track.

Roadmap

05 · Customer Digital Twin

Per-customer Digital Twin, CDAP-native.

Each customer rendered as a hex-anchored Digital Twin of consumption: wallet, channel mix, household, fibre, mobile. Drives personalised offers at decision time.

Roadmap

06 · Network reference

One GIS layer for every Reliance operation.

5G + 4G network coverage, dark stores, RIL Neighbourhood stores and Enterprise Premise Connectivity rendered on a single GIS reference.

Roadmap

07 · Household identification

100M households identified, path to 180M owned.

All-India street-map resolution to identify 100M households from the Broadband and Air Fibre footprint. Sequenced path to the next 75–100M, ending at 150–180M owned-home relationships.

Roadmap

Every business question becomes an agent skill.

Use cases mapped to the Activation Layer and Agentic Orchestration cells of the cornerstone architecture. First set is Live; remaining cells are Building or Roadmap as agents inherit the data unlock from the JioGIS track.

Use caseStatusWhat it does
New Store Opening
Live
Score every catchment; pre-score before ground visit; cannibalisation, demand and feasibility checked before any approval.
Catchment Analysis
Live
Hex-level catchment scoring across formats. Workspace AI co-pilot answers feasibility, catchment, drive-time and brand-network questions.
Dark-store Drive Time
Live
Drive-time isochrones for q-commerce. First set of use cases per cornerstone executive summary.
Customer Sentiment Analysis
Building
Customer Listening data agentically aggregated to hex level.
Pricing & Promotion
Roadmap
Hex-level price elasticity and promotion effectiveness.
Customer Listening
Roadmap
Spatial overlay of voice-of-customer signals.
Transport & SCM Optimisation
Roadmap
Optimise transport routes against the spatial backbone.
Land Parcel Availability
Roadmap
Land parcel surfacing for Strip Mall and large-format expansion.
Inventory Availability
Roadmap
Hex-level inventory presence vs demand signal.
Total Addressable Market (TAM)
Roadmap
Per-format TAM at hex resolution.
AI summary
RetailVista is Reliance Retail's enterprise geospatial intelligence layer, built by Fynd, that predicts what a catchment can become. Signals from UCP, JioGIS, government sources, and third-party POI data are aggregated onto a shared H3 hex index across 24 data categories, then scored across 10 Location-Intelligence dimensions into a 0–100 composite score. Three tracks are running in parallel: an Internal Track live in production on grocery new-store-opening leads, a joint Google Track piloting agentic site intelligence on Vertex AI in Mumbai, and a JioGIS track unlocking the underlying data and foundation model. The roadmap extends this to a pan-India backbone across 1000 cities, per-household Digital Twins, and a single network reference for every Reliance operation.

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