PRATI AI helps organizations connect live operational data, model sites, zones, and assets, monitor telemetry and alerts in real time, activate industry packs, extend the platform through plugins, and expose AI and ML services without changing the product core for every tenant.
Prati AI is built for teams that need faster visibility, clearer alert context, and a configurable digital twin platform that can onboard new tenants and industries without custom application code.
Main pain: Need one clear operating picture before fragmented systems turn minor issues into downtime.
Context: Multi-team environments with fast decisions and high operational risk.
Needs from rollout: Outcome-led reporting, live status, and fast executive-to-operator handoff.
Main pain: Need one place to monitor assets, zones, incidents, and service conditions across distributed sites.
Context: Utilities, buildings, industrial infrastructure, and mixed device fleets.
Needs from rollout: Cross-site visibility, responsive layouts, and dependable alerting.
Main pain: Need a platform they can configure for new environments without rebuilding the product each time.
Context: Pilot programs, digital transformation initiatives, and new operational rollouts.
Needs from rollout: Plugin-first architecture, flexible schemas, and pilot-ready scope.
Main pain: Need mission, fleet, payload, and telemetry visibility in one system before response timing slips.
Context: Remote operations, streaming events, and high-tempo field coordination.
Needs from rollout: Live updates, dense telemetry handling, and low-friction review flows.
Explore how Prati AI adapts across industries while preserving one product core. Each view shows the operational context, pack activation pattern, and onboarding path the BRD supports.
Model production sites through sites, zones, assets, telemetry, rules, and active alerts without rebuilding the platform for each plant.
These are the platform capabilities the BRD points to for MVP: tenant setup, normalized telemetry, live alerts, plugin extensibility, industry packs, and AI-ready operational context.
Represent tenants, sites, zones, assets, telemetry, alerts, rules, models, and plugins in one operational system.
A reusable platform model instead of one-off monitoring projects.
Request a pilot brief to map your pack activation, starter assets, telemetry connectors, rules, and AI-ready starting scope.
Prati AI is positioned for focused early deployments. Teams create the tenant, activate the pack, connect telemetry, monitor alerts, and expose AI and ML services from one shared control layer.
Create the tenant, select the right industry pack, and prepare the starter model for sites, zones, and assets.
Connect live operational data through the ingestion paths defined for the MVP and normalize it into one platform model.
Use live telemetry and active alert dashboards to monitor operational status from a command-center style UI.
Use normalized telemetry, rules, and AI/ML context to investigate risk and hand operational findings to the right team.
Schemas, templates, plugins, APIs, tenants, sites, zones, assets, telemetry, rules, models, and packs are the layers that keep Prati AI configurable instead of hardcoded.
Define how assets, zones, environments, and telemetry entities are modeled.
Stand up a tenant, activate an industry pack, install starter assets, and apply default rules without changing the product core.
Use schemas, templates, plugins, and APIs to adapt the platform without rewriting the core product for every customer.
Expose telemetry and alert context to anomaly detection, health scoring, forecasting, model inference, and AI copilot query workflows.
These are the practical questions teams ask when evaluating platform scope, telemetry onboarding, tenant setup, and AI readiness.
An industrial digital twin platform gives teams one live operating picture of tenants, sites, zones, assets, telemetry, alerts, and rules so they can monitor conditions and respond faster.
PRATI AI is designed for manufacturing units, underground labs, analog habitats, drone operations, industrial infrastructure, and smart facilities through configurable schemas, templates, plugins, and APIs.
Yes. The MVP scope includes telemetry ingestion through MQTT, REST API, CSV import, edge adapter registration, and Modbus-oriented bridge registration.
No. PRATI AI is focused on operational visibility first, so teams can start with configurable schemas, dashboards, and live data before investing in heavyweight 3D experiences.
The private pilot program is built to create a tenant, activate an industry pack, install starter assets and default rules, connect telemetry, and prove value through live telemetry, alerts, plugins, and AI-ready operational context.
This pilot brief captures your tenant context, industry fit, telemetry connectors, starter assets, and rule needs so the Prati AI team can prepare a realistic MVP handoff.