Unified analytics across geospatial, drone, AI, and enterprise data — transforming complex operational environments into decisive, real-time intelligence.
Just as iridescent light reveals the complete visible spectrum, Iridaxis reveals the complete intelligence spectrum within your data.
Iridaxis is a full-spectrum technology intelligence company incorporated in India. Our name fuses two powerful concepts — 'Iridescent', the property of producing multiple colours across different viewing angles, symbolising multi-dimensional insight; and 'Axis', the structural backbone around which complex systems rotate and are measured.
Iridaxis is that axis for modern organisations: the central intelligence infrastructure around which geospatial data, AI models, drone imagery, embedded sensor feeds, and enterprise systems align and generate meaning.
We are built for complexity. Our clients operate in environments where data arrives from multiple sources simultaneously — aerial platforms, field sensors, satellite imagery, enterprise software, and autonomous systems.
To be the intelligence axis for data-complex organisations — providing the full spectrum of analytics, autonomy, and decision intelligence that modern operations demand.
To architect and deliver multi-dimensional intelligence platforms that integrate geospatial, aerial, visual, and enterprise data into a single, coherent operational picture.
The first multi-sensor AI platform purpose-built for litchi orchards — not a generic crop tool adapted for horticulture.
No LiDAR, no expensive proprietary hardware. Satellite + drone + AI on cloud — cost-effective for Indian smallholder FPOs.
Specialist agents for each analytical function — reducing hallucination risk and improving auditability.
High-risk recommendations always enter an agronomist review queue. Governed advisory, not autonomous prescription.
Field outcomes recalibrate models each season. Every harvest makes the system more accurate.
Built for Bihar's fragmented orchard structure today — designed for multi-crop, multi-geography scale tomorrow.
Three integrated stages — data fusion, AI processing, and governed delivery — closing the loop from field to forecast.
Satellite imagery, drone feeds, weather APIs, field sensors, and enterprise systems are ingested into a unified cloud-native data lake — versioned, georeferenced, and structured.
Specialist agents analyse imagery, assess phenology, score disease risk, model yield forecasts, and generate multilingual advisory — each auditable and replaceable independently.
Outputs route through a confidence-based governance layer. High-risk advisories enter human review. Seasonal outcomes recalibrate all upstream models for continuous improvement.
End-to-end intelligence infrastructure — from raw sensor data to decisive operational action.
Intelligent platforms and agentic AI pipelines that decompose complex problems into specialist agent workflows.
Satellite imagery, NDVI/NDWI analysis, and terrain intelligence at scale — from Sentinel-2 to RESOURCESAT-2.
Crop-specific phenology intelligence, disease risk scoring, yield forecasting, and GenAI farm advisory.
Drone mission planning, orthomosaic processing, georeferencing, and patch-level intelligence from aerial assets.
Object detection, anomaly scoring, canopy classification, and multi-spectral image analysis pipelines.
DGCA-compliant flight planning, NPNT mission filing, route optimisation and coverage completeness tracking.
Time-series models, risk-adjusted forecasts, and confidence-band estimation for operational planning.
Dashboards, alert systems, procurement intelligence reports, and institutional data APIs.
Edge computing integration, sensor fusion, and autonomous system data processing pipelines.
Retrieval-Augmented Generation grounded in curated domain knowledge bases. Local-language advisory in Hindi, Maithili, English.
Architecture design, technology roadmaps, and precision agriculture research partnerships.
Cloud-native data lakes, geospatial databases, versioned data lineage, and API infrastructure on AWS/GCP.
Six flagship solution areas — each designed as an integrated system, not a point tool.
Iridaxis builds agentic AI platforms that decompose complex analytical workflows into specialist, bounded agents — each auditable, replaceable, and continuously improving from field outcomes.
Litchi Orchard Intelligence System — India's first litchi-specific multi-sensor agentic AI platform for precision agriculture.
From Sentinel-2 NDVI maps to drone orthomosaic superimposition — Iridaxis delivers patch-level geospatial intelligence on a unified orchard or asset grid.
Free global satellite programmes integrated — delivering continuous, cost-effective monitoring at 5–30m resolution.
End-to-end drone data platforms from DGCA-compliant mission planning to orthomosaic processing and patch-level feature extraction.
Full-stack drone data management — from pre-flight planning to post-processing intelligence output.
Object detection, canopy classification, anomaly scoring, and disease risk identification from drone and satellite imagery using state-of-the-art CV models.
Multi-stage computer vision pipeline from raw imagery to actionable field intelligence.
Low/base/high forecast ranges with confidence scoring — from yield estimation to demand planning — built on field-calibrated models that improve each season.
Conceptual formula used in the LOIS platform:
Dashboards, institutional data APIs, advisory delivery channels, and procurement intelligence reports — connecting field intelligence to enterprise decision-makers.
From litchi orchards in Bihar to oil pipelines — Iridaxis delivers sector-specific intelligence platforms that understand your operational environment.
Precision orchard intelligence, yield forecasting, disease early-warning, and GenAI farm advisory — starting with India's litchi belt.
Pipeline corridor monitoring, leak detection from satellite, infrastructure inspection via drone, and field operations intelligence.
Urban land use mapping, infrastructure condition monitoring, traffic pattern intelligence, and city-scale geospatial analytics.
Forest cover change detection, water body monitoring, soil health mapping, and environmental compliance analytics.
Route optimisation, harvest-linked demand forecasting, cold chain planning, and end-to-end supply chain visibility.
A cloud-native, sensor-agnostic, modular intelligence architecture designed to scale from pilot to production.
GDAL, GeoPandas, PostGIS, QGIS. ESA Copernicus, USGS EarthExplorer, NASA Earthdata, ISRO Bhuvan. REST APIs and streaming ingestion for enterprise systems.
LangChain agentic orchestration, OpenAI GPT-4o / Anthropic Claude for GenAI, PGVector/Pinecone for RAG retrieval, PyTorch for custom CV models.
Mumbai region deployment for Indian data residency. AWS Step Functions for agent orchestration. CloudWatch monitoring. Pay-as-you-go with no capital hardware dependency.
Role-based access control. Encrypted data at rest and in transit. Farmer data privacy with explicit consent management. Audit trail on all advisory outputs.
Sensor-agnostic patch grid model works regardless of which satellite or drone provides the data. Modular agent design — add new agents without disrupting the pipeline.
Feedback Learning Agent captures seasonal outcomes and routes them back to recalibrate all upstream models. Every harvest makes the system more accurate.
A cross-disciplinary team combining geospatial science, AI engineering, agronomy, and enterprise technology.
Driving the vision of full-spectrum intelligence for data-complex organisations across India and beyond.
Satellite data pipelines, NDVI analytics, and drone orthomosaic processing for precision field intelligence.
Architect of the 10-agent LOIS pipeline — from RAG advisory systems to closed-loop model recalibration.
Litchi crop science expertise grounding AI outputs in field reality — human-in-the-loop governance.
Perspectives from the agricultural community, research partners, and early advisors on the LOIS platform.
For the first time, we have an orchard-level digital record that connects satellite health data to our FPO's harvest forecast. The yield prediction gives us real confidence when planning cold-chain logistics.
The agentic pipeline architecture is the right approach for agritech — decomposing the problem into auditable specialist agents means we can validate each step independently. This is how responsible AI should be built.
Being able to receive advisory messages in Maithili, linked directly to my orchard blocks rather than generic region-wide advice, is something I didn't think was possible. This is intelligence that actually reaches us.
Whether you're a farmer, FPO, enterprise, or investor — we'd love to hear from you.
IRIDAXIS GLOBAL PVT LTD
New Delhi, India
CIN: U62099DL2024PTC435521
www.iridaxis.com
iridaxisglobal@gmail.com
Muzaffarpur District, Bihar, India
Precision Agritech — LOIS MVP Pilot
21st April 2026
Stage: MVP / Research Pilot