Alertis is an Indian technology company that engineers AI-driven early warning and intelligent monitoring systems to protect critical infrastructure and high-risk environments. It integrates real-time sensor networks with machine learning and predictive analytics to detect anomalies before they escalate into disasters. Specifically, the platform identifies precursors to landslides, floods, structural failures, and forest fires with speed and precision. Moreover, Alertis converts raw data into actionable intelligence. It issues timely emergency alerts, enables continuous smart monitoring, and delivers unified situational awareness across stakeholders. As a result, organizations can mitigate risk proactively and make informed, time-critical decisions. Backed by deep expertise in geotechnical systems and IoT deployments, the company manages the full lifecycle—from system configuration to post-deployment service—thereby strengthening operational safety and long-term resilience across industries.
At the India AI Impact Expo 2026, in an exclusive interaction with The Interview World, Founder and CEO Tanmay Benjwal articulated the strategic vision behind Alertis. He detailed the platform’s core capabilities, explained how it disseminates alerts to local communities before disasters strike, and outlined its cross-sector applications. Furthermore, he clarified the technical safeguards that ensure predictive accuracy. The following are the principal insights from that discussion.
Q: Can you outline Alertis’s key offerings?
A: Witnessing the recurring natural disasters in Uttarakhand compelled me to found Alertis. I established the company with a clear objective: shift disaster management from reaction to prevention.
Alertis develops advanced early-warning systems that issue timely alerts for natural hazards and related calamities. These include landslides, forest fires, and floods. However, the platform extends beyond environmental risks. The same sensor architecture and analytical framework also monitor critical infrastructure, such as dams, bridges, and tunnels, to detect structural stress and emerging vulnerabilities.
Traditionally, disaster response in many contexts has been reactive. Authorities often quantify the loss, lives lost, assets destroyed, infrastructure damaged, only after the event has occurred. Consequently, intervention begins too late. In contrast, Alertis advances a proactive model. We analyse real-time data, identify risk indicators early, and generate alerts before a disaster unfolds. By doing so, we enable timely intervention, reduce damage, and ultimately save lives.
Q: How does your system generate and deliver alerts prior to a disaster event?
A: We begin by identifying high-risk locations that both government authorities and local communities have already flagged. In many of these sites, disasters have recurred over the past five to six years. Therefore, we deploy ground-based sensors precisely at these vulnerable points.
These sensors operate continuously and function entirely off-grid. They draw power from integrated solar panels and high-endurance batteries designed to sustain operations for up to ten years without routine maintenance. Nevertheless, our teams conduct periodic inspections every two to three months to verify field conditions and system integrity. Even in the absence of such visits, the infrastructure remains fully operational.
The system routes sensor data through two parallel channels. First, it transmits data to an on-ground receiver installed within the monitored zone. Second, it pushes data to a centralized server, which users can access through a secure web dashboard. We provide each client with a dedicated login credential, enabling real-time visibility from anywhere in the world.
In addition, we deploy a handheld ground receiver that functions within a one- to three-kilometer line-of-sight radius. This receiver operates on LoRa (Long Range Radio) technology. We intentionally rely on radio frequencies because, during disasters, conventional communication networks typically fail. Mobile signals, whether 2G, 4G, or 5G, often collapse first. As a result, transmitting alerts through standard telecom channels becomes unreliable.
By contrast, our radio-based transmission ensures uninterrupted local communication. The sensors relay data via LoRa to a designated home station within the coverage radius. Even under extreme conditions, this architecture prevents data loss. Furthermore, the ground receiver functions as a router. It captures incoming sensor data and, when internet connectivity is available, forwards it to our central server through broadband or Wi-Fi.
To reinforce system resilience, we embed SD card storage within each unit. Consequently, the system preserves all critical data even during temporary transmission interruptions. Through this layered and redundant architecture, we safeguard data integrity and generate alerts with high reliability and precision.
Q: Does your platform support early detection of earthquakes and other natural disasters in plains or low-risk geographies?
A: At present, we do not detect earthquakes. Even the most advanced seismic early-warning systems provide only a few seconds of notice, and they require highly specialized, capital-intensive infrastructure. Therefore, we have chosen to focus on hazards where we can deliver sustained, high-precision monitoring and meaningful lead time.
Instead, we deploy ground-based and environmental sensing technologies that monitor critical precursors. These include rainfall intensity, ambient humidity, soil moisture, crack displacement, slope movement, and water levels in rivers or reservoirs. By continuously tracking these parameters, we identify instability patterns before they escalate into landslides, flash floods, or structural failures.
This approach proves especially effective in hilly terrain. Mountain ecosystems are geologically fragile and hydrologically complex. In contrast, plains rarely face landslides or forest fires at the same scale; floods remain the primary risk there, and those floods typically originate upstream in mountainous regions. Consequently, strategic deployment in key mountain locations, such as those in Himachal Pradesh and Uttarakhand, becomes critical. Most major rivers flow outward from these regions. Therefore, upstream monitoring directly strengthens downstream preparedness.
For example, we install our Automatic Weather System (AWS) alongside flood-level and river-stage monitoring units at designated river points. These systems collect localized data in real time and transmit it directly to our platform for analysis and alert generation.
Conventional advisories often rely on broader regional forecasts. Agencies such as the India Meteorological Department (IMD) may issue rainfall alerts for a district or region. However, in mountainous geography, microclimatic variations significantly influence hydrological outcomes. Rainfall may occur in one valley but not in another. Each tributary feeds into a different river system, one into the Ganga basin, another into the Yamuna basin. If precipitation shifts even slightly in location, river behaviour changes accordingly. As a result, generalized forecasts can misrepresent localized risk.
By contrast, our hyperlocal monitoring architecture eliminates this ambiguity. We capture site-specific data, correlate it with terrain dynamics, and generate precise, location-based alerts. In doing so, we reduce forecasting error and enhance predictive reliability in complex mountain environments.
Q: How do you ensure the accuracy of your predictions?
A: Our on-ground sensors provide precise, location-specific rainfall intelligence. Therefore, we do not rely solely on generalized forecasts. If an advisory predicts 10 mm of rainfall, we verify the actual outcome on site, whether it measures 8 mm, 20 mm, or any other value. This real-time validation strengthens situational accuracy and eliminates dependence on broad estimations.
In addition, we deploy a structured, time-series monitoring framework across multiple elevations along a river basin. We install sensor modules at strategic upstream points, such as near Badrinath, Yamunotri, or Gangotri, and then at successive confluence points downstream, continuing through Rudraprayag, Rishikesh, and Haridwar. This layered architecture allows us to track hydrological progression as water travels from glacier-fed origins to densely populated plains.
Hydrodynamics vary significantly across elevations. In upper reaches, where river channels are narrow and shallow, even moderate rainfall can trigger a sharp and immediate rise in water level. For example, a 10 mm rainfall event upstream may increase the river stage by 5 cm at Gangotri or Yamunotri. However, as the river descends and widens, the same inflow distributes across greater depth and width. Consequently, a 5 cm rise upstream may translate into only a 1 cm rise at Rishikesh or Haridwar under normal flow conditions.
By continuously correlating rainfall intensity with downstream stage variation, we establish predictive thresholds. Suppose a 5 cm rise at Rishikesh approaches a predefined flood-warning benchmark. In that case, we can project the likely escalation curve. If upstream data indicates an additional 40 cm surge, we can estimate, with lead time of several hours, the probability and timing of flood impact downstream.
As a result, authorities can issue targeted evacuation advisories, clear vulnerable ghat areas, and restrict riverbank access before water levels reach critical limits. This anticipatory action safeguards human life and minimizes disruption. In essence, our multi-level, time-series monitoring system transforms upstream data into actionable downstream foresight.
