Reliance Retail's enterprise geospatial intelligence layer. Predicts what a catchment can become — never just reflects its past. Three tracks running in parallel. UCP, Customer Listening, JioGIS, store/online data, government and third-party sources unified onto a shared H3 hexagonal index.
Powered by the Retail Vista Agent → · 4-stage opportunity engine · 10-dimension scoring · conversational workspace · /agents/ catalog.
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.
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 + agent orchestration.
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.
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.
| Module | Status | Capability |
|---|---|---|
| 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) |
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.
68 opportunities · 0 approved · 0 shortlisted · Opportunity Intelligence panel (3 Comp. Gaps · 65 Whitespace · 0 Leads · 0 Approved) · Top Opportunities (Goregaon East 69 · Dark Store Monginis 62 · Sarai Rohilla 57 …) · Leads feed · Alerts Requiring Attention · Store Rollout Velocity vs Q2 FY26 target.
Filterable opportunity list (Site / LI / Whitespace / Competitor sub-scores) synchronised with Google Maps. Category and vertical filters. Search. Quick Summary popup on map markers. The scoring surface ground teams use to prioritise visits.
Goregaon East · Score 69 · Medium Confidence · Site 52 · LI 60 · Whitespace 90 · Competitor Gap 100. Composite 62/100 across GDP · Demographics · Accessibility · Property Rates · Wealth · Footfall · Spending · Order Data · Building Density · Competition. Each dimension labelled with its data source. Competitor presence: Swiggy Instamart 0.3km · Blinkit 0.4km · Zepto 0.7km · JioMart Quick Absent.
New (100) → Shortlisted → Site Visit → Approved → Dismissed. Region filter. Initialising progress indicator. Counts move with operator action. The decision system-of-record tying every scored opportunity to a real-world outcome the agents learn from.
Multi-turn chat. Example: "Please perform feasibility analysis for Andheri for a new Reliance Digital store." Output: ranked hex IDs with rwi_mean (relative wealth), Total POIs, epoch_mean, height_mean. Strategic rationale + key metrics + contrast analysis (why other areas were not chosen). Google Maps and MapLibre engine toggle.
Member table · role · status · modules · last login. Invite flow · permission management · regenerate password for pending invites. Five active members across the Internal Track build team today; designed to scale to RIL business sponsors per format as adoption rolls out.
Sources → Spatial Aggregation → GIS Visualisation → Agentic Orchestration → Activation. Agents read live signals, run guarded playbooks, write outcomes back for continuous learning. A question asked once becomes a callable agent skill across all three tracks.
UCP · JioGIS · Retail store master · Customer locations / demographics / behaviour / intent · Government data (Census, NCRB, RBI, RERA, NFHS) · Third-party POI (Kentrix). See §04.
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).
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.
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). See §06 for per-skill status.
Outputs to ALP · Granary · JioMart routing · brand-team workflows. New stores · Pricing · SCM Optimisation · Customer Listening as the activation cells from cornerstone deck.
Sources (UCP · JioGIS · Customer Listening · Google Earth · Reliance datasets · Kentrix · Government · Third-party) → Ingest (Geoson / CSV / Kafka / REST APIs) → Transform / Process / Serve (Databricks · BigQuery · Data Security & Governance · Agentic Orchestration: New Store Opening · Customer Onboarding · Pricing Promotion · Land Parcel Availability · Transport Optimization · Catchment Analysis) → Analyze (Conversational · Maps · Control Tower · Simulation · BI) → Use Cases (New Stores · SCM · Customer Listening · Pricing/Promotions · Land Parcels · TAM · Inventory).
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). Volatility ranges from real-time to as-is.
| # | Category | Source | Refresh | Granularity | Variables |
|---|---|---|---|---|---|
| 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 (stores · DCs · DSs · WHs) | 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+ |
Sources Layer (UCP + Customer Listening · JioGIS · External / Public) → Spatial Aggregation Layer (H3) → GIS Visualisation Layer (per-hex score colouring across India) → Agentic Orchestration Layer (Customer Sentiment · Pricing Promotion · New Store Opening) → Activation Layer (New Stores · Pricing · Transport & SCM Optimisation · Customer Listening).
Per-hex Attractability score (0-100 · Low / Medium / High / Very High) across MMR. Sample popup (Ghatkopar 642): Attractability 53/100 · Jio Penetration 43% · Connectivity 47% · UPI Growth +37% YoY · Competitors 4 · Real Estate ₹13.4K/sqft · Rent ₹201/sqft · Cannibalisation Risk 9% · Population 315K.
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.
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.
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.
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 across every Reliance vertical.
A dedicated RetailVista organisation operating across Reliance and Fynd. Engineering integration with Google folds into a single execution plan. Decisioning lives where the data lives — agent skills callable across every track.
Each customer rendered as a hex-anchored Digital Twin of consumption — wallet, channel mix, household, fibre, mobile. Drives personalised offers at decision time. Sits on Layer 04 Agentic Orchestration once UCP customer lat/long unlocks at scale.
5G + 4G network coverage, dark stores, RIL Neighbourhood stores and Enterprise Premise Connectivity rendered on a single GIS reference. Delivery, expansion and infra teams route off the same substrate.
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. Anchors the household Digital Twin and the JioGIS unlock.
Use cases mapped to the Activation Layer and Agentic Orchestration cells of the cornerstone architecture. First set is Live (NSO + dark-store drive time + catchment intelligence). Remaining cells are Building or Roadmap as agents inherit the data unlock from the JioGIS track. The platform is a single standard across formats — Grocery and Fresh through Digital, Trends, Strip Mall and Dark Store.
| Use case | Status | What it does |
|---|---|---|
| New Store Opening | Live | Score every catchment · pre-score before ground visit · cannibalisation, demand and feasibility checked before any approval. Internal Track on real grocery NSO leads · Google Track Pilot on Mumbai catchments. |
| Catchment Analysis | Live | Hex-level catchment scoring across formats. Workspace AI co-pilot answers feasibility, catchment, drive-time and brand-network questions in natural language. |
| 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. Inputs: UCP · Customer Listening interfaces. |
| Pricing & Promotion | Roadmap | Hex-level price elasticity and promotion effectiveness. Activation-layer cell from cornerstone deck. |
| Customer Listening | Roadmap | Spatial overlay of voice-of-customer signals. Activation-layer cell. |
| Transport & SCM Optimisation | Roadmap | Optimise transport routes against the spatial backbone. Activation-layer cell. |
| Land Parcel Availability | Roadmap | Land parcel surfacing for Strip Mall and large-format expansion. Agentic Orchestration cell. |
| Inventory Availability | Roadmap | Hex-level inventory presence vs demand signal. Activation-layer cell. |
| Total Addressable Market (TAM) | Roadmap | Per-format TAM at hex resolution. Activation-layer cell. |