2 · AI cluster map
Each color = one AI-identified cluster. ⬡ Yellow dots = deep-dived cells with site scores.
Real-time hyperlocal data for every hex cell in India. Understand audience, affluence, footfall, and environment — then let AI plan your next store, screen, or campaign.
Whether you're placing a store, a billboard, or an ATM — Moments gives you the intelligence layer to decide with confidence.
Find the micro-markets where your next outlet will thrive — not just busy streets, but locations where affluence, footfall, and audience intent converge.
Identify neighborhoods with healthcare intent, health-conscious demographics, and proximity to existing medical infrastructure.
Target salaried professional corridors, office districts, and transit hubs where your financial products reach the right audience.
Plan billboard and DOOH screen placements where traffic density, audience composition, and dwell time maximize campaign reach.
Score land parcels and retail spaces against hyperlocal intelligence — affluence, connectivity, venue density, and environmental factors.
Describe what you're looking for in plain English — or use structured API parameters. "Find premium dining areas near transit hubs" becomes a multi-dimensional query across 14 targeting criteria.
affluence=premium · audience_intent=dining · area_type=transit_hub
Moments matches your criteria against 1.49M+ labels on 98K cells, clusters results by geographic proximity and label similarity, then scores each cluster for your business type.
98,454 cells scanned · confidence-weighted · PostGIS spatial
Get composite site scores (0–100), catchment analysis, multi-site comparison, and explainable evidence. Export GeoJSON, CSV, or integrate via API — ready for your decision process.
Site score A+ · Catchment: 47 viable cells · GeoJSON export
From simple location context to AI-assisted planning — each tier builds on the previous.
No hidden fees. No minimum commitment. Pay only for what you use.
Everything you need to integrate GaliGali Moments into your application.
All API requests require authentication via a Bearer token in the Authorization header.
Obtain your API key from the dashboard after signing up.
Authorization: Bearer mk_live_abc123def456ghi789jkl012mno345pqr678
Base URL: https://api.moments.galigali.in/v1
Rate Limits: Returned in X-RateLimit-Remaining and X-RateLimit-Reset headers.
Errors: Standard HTTP status codes. Error body includes code, message, and request_id.
| Name | Type | Required | Description |
|---|---|---|---|
| lat | number | Required | Latitude (-90 to 90) |
| lng | number | Required | Longitude (-180 to 180) |
| resolution | integer | Optional | H3 resolution: 8, 9, or 10. Default: 9 |
| include_signals | boolean | Optional | Include real-time signals in response. Default: false |
| include_label_meta | boolean | Optional | Include label confidence, source, verification timestamp, and summary metadata. Default: false |
| Field | Type | Description |
|---|---|---|
| zone | string | Sublocality/neighborhood name (e.g., "Bandra West") |
| district | string | Administrative district |
| locality | string | City name |
| affluence | string | luxury | premium | mid_range | budget |
| catchment | string | retail_hub | office_district | residential_colony | transit_corridor | entertainment_zone | hospitality_zone | mixed_use | industrial |
| audience_intent | string[] | shopping | dining | commuting | working | leisure | healthcare | education |
| dwell_time | string | high | medium | low | pass_through |
| footfall | string | very_high | high | medium | low | minimal |
| density | string | high | medium | low | none |
| area_type | string | transit_hub | commercial | mixed | suburban | rural |
| venue_type | string | Dominant venue classification |
| road_type | string | highway | arterial | local |
| air_quality | string | good | satisfactory | moderate | poor | very_poor | severe |
| poi_type | string[] | Notable POIs: mall, airport, hospital, metro, etc. |
| label_summary | object | Total labels, label type count, source mix, average confidence, and newest verification timestamp |
| label_metadata | object | Optional grouped label records with value, confidence, source, and last_verified fields |
| Name | Type | Required | Description |
|---|---|---|---|
| locations | array | Required | Array of {lat, lng} or {h3_index} objects. Max 500. |
| resolution | integer | Optional | H3 resolution for lat/lng inputs. Default: 9 |
| labels_only | boolean | Optional | Return only labels (faster). Default: false |
| Name | Type | Required | Description |
|---|---|---|---|
| label_type | string | Required | Label dimension to search (e.g., "affluence", "zone", "catchment") |
| label_value | string | Required | Value to match (e.g., "premium", "Bandra West") |
| resolution | integer | Optional | Filter by resolution: 8, 9, or 10 |
| limit | integer | Optional | Max results. Default: 100, Max: 500 |
| include_signals | boolean | Optional | Enrich results with current signals |
Type a plain-English brief — Moments AI identifies audience clusters, scores every hex, and explains its reasoning with evidence. Real production data. Real AI.
Explore real H3 opportunity heat, ranked shortlists, evidence, site scores, and comparisons over the production Moments intelligence grid.
// Production API: GaliGali Moments /v1 // Select a business scenario to inspect the underlying API payloads.
A phased approach to building the most comprehensive hyperlocal data API suite in India.
Production-grade API gateway with M2M authentication, API key management, per-client rate limiting, and usage metering.
Exposes the Location Context API (Tier 1) and Real-Time Conditions API (Tier 2) to external consumers via
api.moments.galigali.in. Backed by 14,230 H3 cells across Mumbai metro with 4 signal types
and 25 derived semantic labels. Data freshness ranges from active-corridor 15-minute traffic to hourly weather to 3-hourly AQI.
Delivered: 6 production endpoints, 3-tier Redis/inheritance/PG caching, 7 database tables, API key management UI in iot-admin, billing hooks, automated signal poller with label derivation engine, CloudWatch observability. All endpoints load-tested and instrumented.
Adds the Audience Intelligence API (Tier 3) enabling programmatic audience matching across the hex grid. Consumers can query multi-dimensional targeting criteria (affluence + intent + dwell_time + density + footfall) and receive matching cell sets with confidence-weighted scores. GeoJSON heatmap generation for spatial visualization. Audience profile aggregation per cell with composite scoring (0–100). Full billing integration for per-query metering.
Delivered: 3 production endpoints, PostGIS multi-label intersection engine with 14 targeting dimensions, confidence-weighted match scoring, GeoJSON polygon serialization (RFC 7946), audience profile with composite scoring across 4 weighted dimensions, plan-gated result limits (50/200/1000), bounding box spatial filtering. 1.49M+ labels across 98K cells fueling real-time targeting.
Introduces the Site Evaluation API (Tier 4) — a composite scoring engine that fuses all signal layers into decision-ready site assessments. Produces a 0–100 score with transparent breakdown across 6 dimensions: footfall potential, affluence match, category fitness, competition density, accessibility, and dwell potential. Multi-site comparison API for franchise/expansion decisions. Catchment analysis with configurable radius and PostGIS ST_DWithin spatial queries.
Delivered: 3 production endpoints, composite scoring engine with 5 configurable business-type profiles (retail, F&B, healthcare, office, logistics), weighted dimension fusion, multi-site ranking with dimension leaders, PostGIS radial catchment analysis (ST_DWithin, up to 5km), grade assignment (A+ to D), GeoJSON catchment polygons, Redis caching (15-min eval TTL, 30-min catchment TTL), plan-gated radius limits, ₹50/evaluation billing.
The capstone phase adds temporal analytics and real-time streaming. Leverages the monthly-partitioned
hex_cell_state table (90-day rolling history) to expose signal trends — AQI patterns by hour/day,
traffic seasonality, weather-driven behavior shifts. Corridor analysis aggregates intelligence along any
road/transit route. WebSocket streaming via API Gateway enables live dashboards and smart city control rooms.
Key deliverables: Time-series aggregation queries on partitioned PostgreSQL tables, corridor query using PostGIS ST_DWithin along LineString geometries, API Gateway WebSocket API + Lambda connection manager, subscription management (which cells and signals to monitor), threshold-based alert notifications, enterprise SLA monitoring. Priced as enterprise subscription with custom volume agreements.
Built on AWS with Aurora PostgreSQL, ElastiCache Redis, H3 spatial indexing, and auto-scaling ECS Fargate.
Uber's H3 at resolutions 8–10. Parent-child hierarchy with automatic inheritance and 0.9× confidence decay per hop.
Redis L1 (sub-ms) → inheritance L2 → PostgreSQL L3. P99 latency under 50ms for cached queries.
ECS Fargate 4→50 tasks. Lambda 100 reserved concurrency. Aurora Serverless v2 scales 2→32 ACU.
EventBridge-driven Lambda polls 4 signal types at 15min–30day intervals. Auto fan-out chunking.
Monthly range-partitioned hex_cell_state. Automatic partition management — 90-day rolling window.
AWS WAF, Cognito M2M OAuth, timing-safe API keys, VPC-isolated databases, TLS everywhere.
Get your API key and start building with hyperlocal intelligence today.