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.
Somavathi Engine™ is the intelligence beneath the living system — plant understanding, digital twins, bounded decision logic, robotics, safety review and traceable evidence in one loop.
The intelligence beneath the living system
Somavathi Engine™
The MahaaAi intelligence layer connecting plant understanding, digital twins, bounded decision logic, robotics intelligence, safety review and traceable evidence.
01
Sense
Plants, soil, climate, robot, vision, water and energy.
02
Understand
Plant state, environmental context, spatial perception, mission context.
03
Digital twin
Plant twin, environment twin, robot twin, mission twin.
04
Reason
Plant needs, operational constraints, resource availability, risk.
05
Decide
Prioritized action, robot allocation, timing, resource planning.
06
Safety review
Bounded autonomy, policy checks, human override, fail-closed behavior.
07
Act
Robot action, irrigation, inspection, manipulation, environmental response.
08
Verify
Observe outcome, compare expected vs actual, capture evidence.
09
Learn
Update models, improve future decisions, preserve traceability.
NARI™ intelligence
Nature-aware reasoning.
NARI is the temperament of the stack: prefer biological resilience over chemical correction, plant-scale truth over field averages, and a safe state over an unexplained act.
Fail-closed safety
RAKSHA authorizes execution. If an action cannot be bounded, logged and reversed, the robot holds. Autonomy is a privilege the system can revoke.
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™
Safety review and authorization
Applies safety constraints, policy checks and fail-closed behavior before autonomous physical actions are authorized.
ANKUR™
Growth and outcome intelligence
Tracks plant development and verified outcomes to improve future decisions and support continuous learning.
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.
- Interactive Simulation
- Synthetic Data
- Read-Only Public Demo
- No Physical Robot Control
- Physical Validation in Progress
The interactive Control Tower demonstrates synthetic simulation workflows. It does not expose private GitHub files, source code, credentials, physical actuator controls or live production telemetry.