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A satellite-to-ground intelligence system that maps the living planet as one continuous, queryable grid — and lets AI reason across every domain at once.
Anvīkṣaṇa fuses satellite, atmospheric, ocean and ground-sensor signals onto a single Discrete Global Grid — then embeds them in a knowledge graph and lets autonomous agents reason across every domain at once. Built on open standards: OGC CSAPI and DGGS, IEEE P4011, W3C Web of Things, ISO/TC 211.
The team behind Anvīkṣaṇa combines aerospace engineering, deep machine learning and geospatial AI research with active participation in international standards bodies — OGC, IEEE GRSS and W3C. Aganitha Space Technologies is built as a standards-first, research-anchored deep-tech venture, with IIT Bombay (CSRE) as primary research partner.
Each module is an autonomous lens on the planet — independent, yet sharing one grid, one graph and one orchestration layer.
Solar-yield forecasting and air-quality intelligence mapped continuously across the grid. Operational outputs feed renewable siting decisions and pollution-event early warning. Holder of the Solar Impulse Foundation Efficient Solution label.
Solar Impulse LabelledAutomated carbon measurement, reporting and verification at grid-cell resolution. Satellite-derived biomass and flux data converted into auditable, regulator-ready ledgers. Designed for national inventories and voluntary carbon markets.
Auto MRVAtmospheric intelligence and multi-hazard cascade modelling. Tracks how a single event — drought, wildfire smoke, cyclonic depression — propagates across coupled Earth systems and into sectoral risk exposure.
Cascade engineOcean and coastal intelligence spanning sea-surface temperature, chlorophyll, sea-level trend and shoreline change at meter resolution. Supports blue-economy planning, fisheries management and coastal resilience assessment.
Coastal watchA distributed terrestrial IoT mesh — LoRa and BLE edge nodes — streaming ground-truth onto the DGGS grid in near-real time. Closes the gap between satellite revisit intervals and the conditions that actually matter on the ground.
Edge meshOn-board inference at the edge of orbit. AI models run directly on satellite silicon — processing imagery before it descends to ground stations. Latency collapses; bandwidth requirements fall. Target platform: ISC-R1 class silicon.
On-orbit AISignal enters as raw observation and rises through the stack — discretised, related, reasoned over, and finally surfaced as decisions. The grid is the substrate at every layer.
Raw signals from space, atmosphere, ocean and ground enter as noise. They leave as decisions. Four layers make that happen.
Standards bodies · Government recognition · Research partners · Industry accreditation
Independent validation across clean tech, deep tech, GeoAI and space — from Switzerland to Gurugram.
Updates on deployments, standards contributions, partnerships and platform milestones. For press enquiries, write to contact@aganithaspace.com.
Press kit, technical briefs and media assets
Request press kitRequest a live walkthrough of Anvīkṣaṇa — running on real grid data, across every domain. Or write directly to discuss investment, partnerships and research.
From sovereign agricultural risk to orbital edge inference — the platform is built for institutions that need Earth intelligence at operational scale.