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Most homes ventilate the way they always have: reactively, manually, and inefficiently. A window is cracked when the air feels stale. A fan runs continuously because it was turned on once and never revisited. Air purifiers recirculate indoor air, cleaning it but never truly replacing it.
Vyana starts from a different premise. Ventilation should be intentional, measurable, and timed. Not constant. Not guesswork. And not energy-blind. Rather than treating fresh air as an always-on utility, Vyana treats indoor air quality as a control problem—one that can be sensed, predicted, and managed using the same decision logic that powers modern industrial systems.
People spend roughly 90 percent of their time indoors, yet indoor air is often more polluted than outdoor air. Cooking, cleaning, pets, gatherings, and off-gassing materials steadily raise carbon dioxide, particulates, and volatile organic compounds. The intuitive response—bringing in fresh air—comes with a tradeoff: poorly timed ventilation wastes conditioned air and can worsen humidity or allergy exposure.
As homes become tighter and HVAC systems more efficient, that contradiction sharpens. Fresh air is essential, but poorly timed fresh air is costly.
Most existing solutions resolve this tension bluntly. Energy-recovery ventilators run continuously at low flow. Fresh-air systems add outdoor air without explicitly removing stale air. Purifiers improve air cleanliness but do not solve oxygen replacement or CO₂ buildup. The result is either continuous energy spend, partial outcomes, or both.
Vyana’s approach is neither continuous nor additive. It is replacement-based and predictive.
“Replace, don’t dilute” as a system design choice
“Replace, don’t dilute” is not marketing language. It is a design decision.
Instead of running all day, Vyana ventilates in short, targeted exchanges when conditions are favorable. Coordinated intake and exhaust create a controlled air exchange that replaces indoor air in bursts. When conditions are unfavorable, the system remains sealed.
First, define the objective: reduce indicators of stale air—such as elevated carbon dioxide, VOCs, and particulates—without compromising comfort or energy efficiency.
Second, measure continuously: indoor air quality, temperature, and humidity are monitored in real time.
Third, contextualize decisions: outdoor constraints—temperature differentials, humidity, air quality, and time of day—determine whether ventilation will help or hurt.
Finally, close the loop: after each exchange, the system evaluates outcomes and adjusts future decisions.
This is not automation for its own sake. It is analytics applied where intuition consistently fails.
The central question in smart ventilation is not whether air can be moved. It is when, for how long, and under which constraints an air exchange delivers a net benefit.
From an analytics perspective, Vyana operates as a decision pipeline. Indoor signals indicate rising staleness. Outdoor conditions gate whether action is worthwhile. The system selects a low-cost intervention—a brief exchange instead of continuous airflow—and validates whether the intervention achieved the intended improvement.
Even without advanced machine learning, this loop benefits from classical analytics: threshold detection, rate-of-change analysis, time-window optimization, and household-specific baselining. Over time, the system can learn how a particular home responds to weather, occupancy, and seasonality, improving timing and reducing unnecessary cycles.
The outcome is ventilation that feels invisible—because it is well-timed.
Vyana was founded by Arjun Gupta, a computer engineer and product leader who has spent over a decade building data-driven systems where timing and constraints matter as much as raw capacity. That background shows up in Vyana’s design choices: ventilation is treated as a control problem with measurable inputs, explicit gating conditions, and short, optimized interventions rather than continuous operation.
Outside of work, the same bias toward disciplined execution under constraint shows up consistently. Arjun has completed two full marathons and more than seven half marathons, maintains a daily meditation practice centered on attention and consistency, and coaches competitive youth robotics teams that rely on iterative testing, feedback loops, and structured problem decomposition. The throughline is consistent: systems improve when they are observed carefully, tuned deliberately, and measured against outcomes.
Indoor air quality is increasingly linked to sleep quality, productivity, and long-term health. At the same time, HVAC systems account for a large share of residential energy use, and energy costs continue to rise.
Treating ventilation as a prediction problem allows both concerns to coexist. Instead of ventilating more, homes ventilate smarter—favoring cleaner outdoor windows, cooler evenings, and short exchanges that avoid unnecessary reheating or cooling.
For renters and homeowners alike, a replacement-based approach can also lower adoption friction. By avoiding complex ducting and continuous operation, the focus shifts from theoretical efficiency to practical deployment.
The most successful automation is rarely noticed. Thermostats stabilize temperature without asking permission. Lighting systems adjust without explanation.
Vyana aims for the same outcome with air: a home that quietly maintains healthy conditions without dashboards, alarms, or constant user intervention.
Seen through an analytics lens, the insight is straightforward. Indoor air is not a lifestyle problem. It is a system variable. Once measured and controlled properly, it stops demanding attention. In that sense, Vyana is less about air and more about applying modern control logic to one of the last unmanaged systems in the home.
