tejas agniLive

A single brain for full data center visibility

Agni turns raw telemetry into clear, prioritized answers — so operators know what's urgent and how to fix it.

Live · Digital twin
Ask Agni

Ask a question. Get a prioritized answer — not another dashboard.

Ask in plain language
Type it like you'd ask a senior operator. No query language, no dashboards.
Agni reasons over everything
Live telemetry, 24+ months of history and your equipment docs — with the working shown.
Answers ranked by urgency
Prioritized, most pressing issue first — with a suggested fix your team can act on.
Ask Agni
Which chillers are trending toward an efficiency decline?
Top 5 alarms right nowHall B supply vs return24-month consumption
3 chillers show a slow approach-temperature drift over the last 9 days. CH-04 is the most urgent — efficiency is down 6.2% vs. its own baseline.
Suggested action: inspect the condenser water loop and re-check the PID setpoint.

The only operations LLM built for data center teams — analyze, troubleshoot and act before issues reach your SLAs.

What operators ask Agni every day

One assistant for a control room's hundred questions

Spot efficiency decline before it shows up on the bill
Monitor temperature setpoints across hundreds of chillers
Filter thousands of alarms down to what actually matters
Predict performance degradation across critical equipment
Chart consumption across the past 24 months in one query
Compare supply and return temps across a whole hall
Chart evaporator water flow for any loop, instantly
Surface new ways to free up power for revenue GPUs

tejas agni

The first LLM for data center operations, helping technicians uncover, prioritize and troubleshoot the most pressing issues.

tejas factory

Coming Soon

Specialized AI agents to proactively monitor and optimize mission-critical data center infrastructure at scale.

CDU - Supply Temperature & IT Load Timeseries Plot
800600400IT Load (kW)454239Supply Temp (°C)45°C Limit015304560Minutes
IT Load (kW) Traditional Control AI Control

Increase reliability

Significantly improve thermal stability and reliability via AI agents that vigilantly manage data center infrastructure on a 24/7 basis. These AI agents detect and diagnose issues that may otherwise remain hidden.

Total Annual Chiller Consumption (kWh)
TSC 20°CTSC 30°CTSC 40°CZone 0Extremely HotZone 1Very HotZone 2HotZone 3WarmZone 4MixZone 5CoolZone 6ColdZone 7Very ColdTotal Annual Chiller Consumption
TSC 20°C TSC 30°C TSC 40°C

Maximize IT capacity

Precision AI control enables the AI factory to operate at higher TCS temperatures while remaining safely within GPU T-limits. This substantially reduces the power required for facility-level cooling — meaning more power available for revenue-generating GPUs.

Agni, in action

One agent for every critical system

See Agni command real facilities — live digital twins, root-cause chat and immersive VR walkthroughs.

Live digital twinAutonomous control

Live digital twin

A real-time twin of the data hall — power, cooling and autonomous control running in closed loop.

Root-cause, rack by rackDiagnostics

Root-cause, rack by rack

Trace a GPU fault through cooling, airflow and BMS — with a guided move-and-correct checklist.

Walk the facility in VRImmersive

Walk the facility in VR

Step inside the twin. Inspect any rack, read live sensors and ask the agent in immersive mode.

Beyond the data centerCritical infrastructure

Beyond the data center

From AI factories to nuclear plants — the same agent commands power and cooling at any scale.

Simulated Benchmark: Thermal Spike Control

CDU Supply Temperature & IT Load Timeseries Plot
800600400300IT Load (kW)4543413937Supply Temp (°C)45°C Limit0102030405060Minutes
AI OFFAI ON (Pre-Training)AI ON (Post-Training)
Absolute Setpoint Deviation (Δ°C)
6543210AI OFFAI ON (Pre-Training)AI ON (Post-Training)
AI OFF
AI OFF Setpoint
AI ON (Pre-Training)
AI ON (Pre-Training) Setpoint
AI ON (Post-Training)
AI ON (Post-Training) Setpoint
45°C Limit

In our internal simulations on a liquid-cooled reference rack, Tejas Agni's control policy reduced thermal spikes related to bursty AI workloads by ~80% versus static setpoints.

How Agni helps

Automated insight, plus an assistant that actually knows your plant

Automated insights

Problems you can see forming

Contextual alerts on issues taking shape — each with a suggested fix, from real-time and historical data together.

CH-04 approach temperature driftingEfficiency down 6.2% over 9 days · suggested: inspect condenser water loop
Hall B return-water temps diverging2 CRAHs trending apart · suggested: check valve sequencing
Headroom to raise TCS setpointWithin GPU T-limits · ~4% facility cooling power recoverable
Knowledgeable assistant

Ask Bodhi anything about your plant

Bodhi pulls live data, history and your equipment docs to answer questions and build charts — in plain language, working shown.

Operator
I'm seeing some oscillations in the temps, can you help me diagnose the issue?
Tejas Agni

It appears the temperature oscillations in your chiller plant are likely due to poorly tuned PID control loops.

To stabilize the plant, follow these steps:

  1. Retune PID parameters: Adjust gains to prevent valve hunting.
  2. Review valve sequencing: Check that actuators operate correctly.
  3. Calibrate pressure sensors: Inspect differential transmitter values.
Chiller Plant Temperatures (Sep 1st - 3rd)
85807506:0012:0018:00
Chilled Water Temp Return Water Temp
Case studies & research

Proof, not promises

What our agents do in simulation and on real plant data — and the research behind it.

Research paper

Physics-grounded reinforcement learning for closed-loop data center cooling

Our method, results and benchmarks — from high-fidelity thermodynamic simulation to safe real-world control.

Tejas AI Research · 2026 · Preprint coming soon
Read the paperLink to be added
AI factory · liquid-cooled rack
0%
fewer thermal spikes

Taming bursty thermal spikes

Agni's learned control policy held setpoints through bursty AI workloads where static control oscillated.

Data hall · cooling plant
0%
cooling energy

Cutting cooling energy

Running closed-loop at higher, safe coolant temperatures freed power for revenue-generating GPUs.

Operations · uptime
0.00%
target uptime

Protecting availability

Continuous monitoring and early root-cause diagnosis kept critical systems inside their limits, 24/7.

Figures from internal simulations on a liquid-cooled reference rack. Full methodology in the forthcoming paper.
How it works

From raw telemetry to a clear next action — in four steps

1

Connect

Stream telemetry from BMS, CDUs, chillers and PDUs. Read-only to start.

2

Contextualize

Agni maps your topology and learns each asset's normal — so an alarm becomes an explanation.

3

Converse

Ask in plain language. Agni answers with charts and a ranked list of what's urgent.

4

Act

Every insight ships with a suggested fix to review and apply — humans in the loop.

tejasai
May 28, 2026Announcement

Introducing Tejas Agni — an AI copilot that helps data center operators find what's urgent, fast

Read more

The new era of data center operations starts with seeing clearly

Book a live session and put Agni in front of your own telemetry. We'll answer your hardest operations questions in real time — and be honest about whether we're the right fit yet.

Request a demo