Your AI training run is 72 hours in. $180,000 in compute time invested.
Power sags 3% for 200 milliseconds. Training crashes. You start over.
Your grid connection thinks 200ms doesn't matter. Your AI model thinks it's catastrophic.
The power quality problem nobody talks about
Traditional manufacturing tolerates voltage variations. Smelters run through minor sags. Assembly lines recover from brief dips.
AI datacenters don't tolerate anything.
Power quality requirements:
- Manufacturing: ±10% voltage variation acceptable
- Standard datacenters: ±5% variation, 1-second ride-through
- AI training clusters: ±2% variation, zero tolerance for sags
One voltage event = checkpointing failure = lost training run.
Grid power in India: 15-30 events per month in industrial areas. Each one a risk.
Why AI workloads are different
Traditional datacenter load:
- Web servers: Graceful degradation
- Databases: Checkpoint every second
- Storage: Redundancy handles failures
- Recovery time: Seconds to minutes
AI training load:
- GPUs: 100% utilization, synchronized across thousands of nodes
- Training state: Too large to checkpoint frequently (multi-TB)
- Node failure: Entire training run fails if one GPU drops
- Recovery time: Restart from last checkpoint = hours lost
A $2 million H100 cluster running 24/7 generates $50,000+ per day in value. Power downtime = direct revenue loss.
The 5MW spike problem
Your AI cluster starts a new training job. Power demand jumps 0→5MW in 12 seconds.
Your grid connection contract says 10MW steady. Grid operator sees sudden ramp. Violation notice. Demand charges explode.
What happens:
- GPU cluster spins up all nodes simultaneously
- Power draw ramps from idle (2MW) to full load (7MW) in seconds
- Grid connection sees demand spike above contracted rate
- Automatic penalty: 2x demand charges for the month
- Cost: ₹50 lakh extra on your next bill
Traditional power infrastructure can't buffer this. By the time diesel gensets spin up, the spike already happened.
Our solution: Millisecond battery response + AI prediction
24/7 autonomous power management:
- Predictive load forecasting: AI agents predict training job start 30 seconds before execution
- Battery pre-positioning: Energy storage charges to absorb the coming spike
- Seamless transition: Battery delivers power during ramp-up, grid sees smooth curve
- Grid compliance maintained: No violations, no penalty charges
Response time: 4 milliseconds. Faster than your grid connection can measure the spike.
Real numbers: Hyperscale datacenter in Karnataka
Before (grid-only power):
- Power quality events: 18 per month
- Training run failures: 3-4 per month
- Lost compute time: ₹1.2 crore/year
- Demand charge penalties: ₹85 lakh/year
- Total cost of poor power quality: ₹2.05 crore/year
After (Ziani AI-managed power + battery):
- Power quality events reaching AI cluster: 0
- Training run failures from power: 0
- Lost compute time: 0
- Demand charge penalties: 0
- Annual savings: ₹2.05 crore
Battery system paid for itself in 14 months.
Why your current setup won't work
Grid power alone:
- Voltage regulation: Too slow (seconds)
- Frequency response: Inadequate for AI loads
- Reliability: 99.5% uptime = 43 hours downtime/year
- Demand spikes: No buffering capability
Diesel gensets:
- Startup time: 8-12 seconds (too slow)
- Frequency stability: ±2 Hz variation during load changes
- Not rated for continuous operation at AI datacenter duty cycles
- Carbon emissions kill your ESG metrics
UPS systems:
- Designed for backup, not power quality
- Can't handle 5MW ramps without oversizing 3x
- No predictive capability
- Can't reduce demand charges
What AI datacenters actually need
Continuous power quality management:
- Sub-cycle voltage regulation (< 20ms response)
- Active harmonic filtering
- Real-time load prediction
- Automated demand charge optimization
- 99.999% uptime (5 minutes downtime per year)
Plus operational integration:
- API integration with Kubernetes scheduler
- Pre-emptive battery positioning before training jobs
- Dynamic power allocation across workloads
- Real-time cost optimization
This isn't a UPS. This is an AI operating system for your power infrastructure.
The infrastructure shift
Manufacturing plants need reliable power. 99.5% uptime is acceptable.
AI datacenters need perfect power. 99.999% is the starting point.
Traditional approach:
- Buy power from grid
- Add diesel backup
- Accept occasional downtime
- Budget for lost production
Hyperscale approach:
- Own your power infrastructure
- Active management, not passive backup
- Zero tolerance for outages
- Power quality as competitive advantage
What this costs
15MW AI datacenter power infrastructure:
Traditional setup (grid + diesel + UPS):
- Capital: ₹8-12 crore
- Monthly power cost: ₹90 lakh
- Demand charges: ₹15-25 lakh/month (with penalties)
- Unplanned downtime cost: ₹2-3 crore/year
Ziani managed infrastructure (solar + battery + AI):
- Capital: ₹35-40 crore (off-balance-sheet via PPA)
- Monthly power cost: ₹65 lakh (locked for 25 years)
- Demand charges: ₹0 (battery manages all spikes)
- Downtime: 5 minutes per year maximum
Total 5-year cost:
- Traditional: ₹70 crore + downtime losses
- Ziani: ₹39 crore, zero downtime
You save ₹31 crore. And you never lose another training run.
Technical requirements we meet
Power quality (IEEE 1547-2018 compliance):
- Voltage regulation: ±1% at point of coupling
- Frequency: 49.9-50.1 Hz (20x tighter than grid)
- Harmonics: < 3% THD (total harmonic distortion)
- Response time: < 4ms to any load change
Availability (datacenter Tier III+):
- Uptime: 99.999% (5.26 minutes downtime/year)
- MTBF: 2,200 hours (mean time between failures)
- MTTR: 4 hours (mean time to repair)
- Redundancy: N+1 for all critical components
Integration:
- REST API for workload management systems
- Kubernetes plugin for power-aware scheduling
- Real-time telemetry (1-second granularity)
- SCADA integration for facility management
The regulatory angle
Open access + renewable energy credits:
- RECs for corporate sustainability targets
- Open access compliance (< 1MW or > 1MW both handled)
- No wheeling charges with on-site generation
- Banking available for excess generation
Your CFO cares about this. Green power + demand charge elimination = 25-30% total energy cost reduction.
Who needs this
If you're running:
- AI training clusters (LLMs, computer vision, generative models)
- High-frequency trading infrastructure
- Scientific computing (molecular dynamics, climate modeling)
- Real-time rendering farms
- Any compute workload where interruption = unacceptable loss
You need power infrastructure that matches your compute infrastructure.
What happens next
Site analysis: 48 hours
- Load profile analysis
- Power quality baseline measurement
- Grid connection assessment
- Battery sizing and solar capacity calculation
Engineering design: 2 weeks
- Electrical single-line diagram
- Battery and solar system specifications
- Integration with existing infrastructure
- ROI model with demand charge optimization
Installation: 4-6 months
- Off-balance-sheet financing via PPA
- Zero upfront capital
- Commissioning with full load testing
- Handoff to autonomous AI operations
We operate it. You run your AI workloads.
The bottom line
Traditional power infrastructure assumes interruptions are acceptable.
For AI datacenters, one interruption costs more than perfect power infrastructure.
You're spending ₹5-7 crore per year on power. We'll save you ₹2 crore annually while eliminating downtime.
Your training runs finish. Your models deploy. Your business scales.
We handle the power.