Digital twins
Every greenhouse, aisle and selected plant has a living mirror. Interventions can be simulated before they touch tissue.
Technology
Physical AI,
governed end to end.
NARI™ is MahaaAi’s top-level Nature-Aware Robotic Intelligence layer, connecting plant understanding, Digital Twins, reasoning, Somavati governance, robotics and traceable evidence. Somavati provides the governed safety, decision and orchestration subsystem within NARI.
NARI™ · MahaaAi Intelligence Grid™
NARI Intelligence Lifecycle
NARI is MahaaAi’s top-level Nature-Aware Robotic Intelligence layer, connecting plant understanding, Digital Twins, reasoning, Somavati governance, robotics and traceable evidence.
Somavati is the governed safety, decision and orchestration subsystem inside NARI, providing policy checks, confidence bounds, fail-closed controls and human override.
01
Sense
Observe the physical environment through available field, robot and sensor inputs.
02
Understand
Convert observations into contextual environmental understanding.
03
Twin
Update the relevant Digital Twin representation of plant, environment, robot or mission state.
04
Reason
Evaluate context, relationships, evidence and potential actions.
05
Govern
Apply Somavati safety, policy, confidence and decision guardrails before physical action.
06
Act
Execute approved actions through robotic and automation capabilities.
07
Learn
Capture outcomes and evidence to improve subsequent decisions under human oversight.
NARI™ intelligence
Nature-aware reasoning.
NARI is the temperament of the stack: prefer biological resilience over unnecessary synthetic inputs, plant-scale truth over field averages, and a safe state over an unexplained act.
Fail-closed safety
RAKSHA applies explicit constraints and fail-closed authorization before an autonomous physical action may proceed. Current evidence is a software-level Safety Gate in simulation; physical validation is planned.
Traceable evidence
Sense-to-learn is an audit trail, not a slogan. Growers, researchers and regulators can ask why a plant was watered, pruned, or left alone.
Character intelligences
Specialists inside the loop.
DHARA™
Plant and environmental understanding
Fuses soil, climate, phenology and crop signals into a living representation of plant need.
DRISHTI™
Vision and observation
Combines RGB-D, multispectral and spatial perception to observe plants, fruit, aisles and operating environments.
JALA™
Water and irrigation intelligence
Connects plant demand, soil moisture, climate and irrigation outcomes to support precision water decisions.
KSHETRA™
Mission planning and allocation
Transforms plant and environmental understanding into bounded operational plans across robots, tasks, energy, water and time.
RAKSHA™
Independent safety review and authorization
Applies safety constraints, policy checks and fail-closed behavior before autonomous physical actions are authorized. Operates with Somavati governance. Current evidence is a software-level Safety Gate in simulation; no functional-safety certification is claimed.
ANKUR™
Growth and outcome intelligence
Tracks plant development and verified outcomes to improve future decisions under governed learning and human oversight.
JALA™ · intelligent water
Every millilitre, accounted.
Soil moisture, transpiration, weather and reuse capacity compose a watering plan at plant scale. Rain is harvested, filtered, mineral-balanced and returned. Nothing in the loop is ornamental.
- Synthetic Simulation
- Read-Only Demonstration
- No Physical Robot Control
- Physical Validation Planned
The interactive Control Tower is a labeled synthetic demonstration of a Control Tower operational view mapped to the 7-stage NARI Intelligence Lifecycle. Demonstration telemetry is generated from synthetic simulation unless explicitly labelled as physical-test data. It is read-only and does not control physical robots. It does not expose private GitHub files, source code, credentials, physical actuator controls or live production telemetry.