SAJAG सजग
Smart Asset Judgement & Availability Guard
“Predict the failure. Protect the mission.”
THE PROBLEM
Many military and government fleets are maintained on a calendar rather than on actual condition. Fixed-interval servicing either over-maintains, wasting resources, or under-maintains, missing failures. When equipment fails unexpectedly, the right spares are often far from where they are needed, and the asset sits idle. SAJAG is designed to estimate failure risk from how each asset is actually used, and to forecast which spares will be needed, where and when.
THE SOLUTION
What SAJAG Does
SAJAG is a predictive maintenance and spares-demand intelligence platform in active development. It is designed to estimate failure risk, prioritise maintenance and forecast spares demand using data organisations already hold: service records, breakdown logs, usage and deployment histories. Phase 1 is data-first and sensor-optional.
Predictions are designed to account for terrain and climate, because the same vehicle wears differently at high altitude, in desert heat and in monsoon conditions. Preferred methods are interpretable and CPU-capable, including survival analysis, gradient-boosted models, time-series forecasting and association analysis. Core prediction does not depend on an LLM, and core functions are designed to run without internet access, inside the customer's boundary.
सजग (SAJAG) - Alert. Vigilant. Watchful. "Sajag raho" (stay alert) is a familiar call in Indian military life. SAJAG is designed to stay alert to equipment degradation so fleets stay mission-ready.
CAPABILITIES
Key Features
Asset Health Indicators
Per-asset and per-system health indicators (engine, cooling, transmission, electrical, brakes, suspension), updated as new records are ingested, with historical trends.
Failure Risk Estimation
Survival analysis and classification estimate failure risk over defined time windows per asset and per system, with contributing factors and recommended actions shown.
Terrain-Weather Context
Predictions adjusted for deployment terrain and climate, such as high altitude, desert, monsoon and plains, calibrated on the customer's own operational data.
Spares Demand Forecasting
Spares demand forecasts per unit or formation over planning horizons, mapping predicted maintenance to the spares bill of materials, with dead-stock and periodicity analysis.
Breakdown Pattern Mining
Association analysis surfaces failure patterns that tend to occur together, for engineers to review and validate against their own experience.
Readiness Planning
What-if views that show how servicing a chosen set of assets first is expected to change fleet readiness, with the basis for each estimate visible.
Maintenance Alerts
Critical, warning and advisory alerts with recommended actions, spares lists and an acknowledgement workflow.
Data-First Architecture
Works from existing records digitised through CSV or spreadsheet upload, web forms or a mobile app. Sensors are optional, with sensor integration planned for later phases.
DEPLOYMENT OPTIONS
Product Tiers
SAJAG OnPrem
Deployed on the customer's own server infrastructure, on a local network with no internet dependence. Data stays inside the customer's boundary.
SAJAG Sovereign Cloud
Deployment on a sovereign or government cloud chosen by the customer, where permitted, for evaluation and controlled demonstration.
SAJAG Hybrid
Phase 2Unit-level instances for data collection and local predictions, with a command-level aggregator for cross-unit visibility and demand forecasting.
CUSTOMER CONTROL COMPARED
Why SAJAG?
“Too many vehicles are maintained on a calendar instead of their actual condition. SAJAG is designed to estimate failure risk from how each asset is actually used, in which terrain, at what altitude and in what climate, and to help supply chains stock the right spares in the right place.”
Claims boundary
What SAJAG is designed to do, and what we do not claim
SAJAG is in active development. Prediction quality depends on the customer's data, so performance is established on that data before it is reported.
Designed to
- Estimate failure risk from historical maintenance, usage, terrain and weather data.
- Prioritise maintenance and forecast spares demand from those estimates.
- Start data-first: existing records are enough to begin, and sensors are optional.
- Use interpretable, CPU-capable methods such as survival analysis, gradient-boosted models, time-series forecasting and association analysis.
- Run core prediction without depending on an LLM, inside the customer's boundary.
What we do not claim
- No published prediction accuracy, inference speed or reliability improvement until it is generated from a validated dataset and documented evaluation.
- Example outputs on this page illustrate the format of results, not achieved results.
- Prediction quality depends on the completeness and quality of the maintenance records available.
- SAJAG supports maintenance decisions; it does not replace engineering judgement or the customer's maintenance authority.
Programme fit and evaluation scope are confirmed with each customer before any pilot.
INTEGRATION
Works With
DESIGNED FOR
Who It Serves
Built in India. Designed for Sovereign Nations.
Engineered in India for lawful deployment by sovereign nations and approved organisations. Customer-controlled infrastructure, documented interfaces and export paths, and no mandatory runtime dependence on infrastructure outside your boundary.
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Learn MoreReady to See SAJAG in Action?
Start with a controlled demonstration scoped to your operational problem, security boundary and existing systems. Available to sovereign and approved organisations, subject to applicable export law.