Manufacturing · Energy optimization
Your energy bill reflects last month's waste, not this week's
Banao deploys predictive energy optimization models over your existing meter, sensor, and SCADA data — attributing consumption to equipment and shift, catching waste anomalies before the billing cycle closes, and optimizing load schedules against time-of-use tariffs.
This is not a dashboard that shows you usage. It is a model that flags the compressor running hot at 2 AM, the kiln idle for forty minutes mid-batch, and the shift where power-per-unit crept above baseline — before you see it on the invoice.
The first call is free · 45 minutes · no obligation
What we build
What a Banao energy optimization deployment covers
Meters are everywhere; useful signal is not. We turn raw consumption data into equipment-level attribution, anomaly detection, and dispatch optimization.
Equipment-level energy attribution
Site-level meters tell you the total; they do not tell you which furnace, compressor, or press is responsible. Banao disaggregates consumption to equipment level so plant heads know exactly where the cost originates.
Predictive waste alerts
Models trained on your meter and sensor history flag deviations — a furnace running above normal temperature, a compressor running through a scheduled shutdown — before the billing cycle closes and the damage is done.
Load scheduling against time-of-use tariffs
For plants on ToU electricity contracts, shifting non-critical load to off-peak windows is a direct cost reduction. The model builds a feasible shift schedule against your production constraints and grid pricing.
Furnace and kiln combustion tuning
Combustion setpoints that seemed correct at commissioning drift over time. Banao fits a real-time sensor-feedback model that keeps furnace and kiln combustion efficient, not just within the original spec.
Compressed air and utilities monitoring
Compressed air is one of the highest-loss utilities on most manufacturing floors. Models over pressure sensor data identify leak patterns and over-pressurisation without a manual site-wide audit.
Batch and shift benchmarking
Normalising energy per unit of output across batches, shifts, and raw-material grades separates genuine efficiency gains from volume effects — so you target real waste, not noise.
Receipts
Where energy optimization is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Predictive energy analytics across refinery operations
Banao instrumented meter and sensor data across process units to attribute consumption at equipment level and surface anomalies before the billing period closed. Load scheduling recommendations now feed the operations control room rather than the finance review.
Dogfooding
We run AI on our own operations before yours
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own hires; Vikaas handles our own demand generation. A system that survives our internal operations is already stress-tested before it reaches your floor.
We do not model energy efficiency from the outside. The same standard we apply to our internal tools — operational proof before deployment — is the standard we hold every factory AI system to.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When energy optimization AI earns no return
Not every plant has the right data infrastructure or problem profile. We will tell you before you commit budget:
- Coarse metering: if your only meter is the site main, there is nothing to attribute. Sub-metering or sensor retrofits must come first — and the economics change significantly.
- Already optimized: if your plant has a mature ISO 50001 programme and a dedicated energy manager, AI may add marginal value rather than step-change savings. We will say so after the audit.
- Highly variable product mix: if batch parameters change weekly, normalising energy per unit of output is hard, and the model may surface more confusion than signal in the first months.
How we start
How we start — baseline before you build
Energy optimization without a credible baseline is guesswork. We audit your meters first, before any model build.
- 01
AI Discovery Sprint
2 weeks · fixed price
We pull your meter, sensor, and SCADA data, run attribution analysis on your highest-cost equipment, and hand back a baseline waste estimate and payback model — yours to keep regardless of whether you proceed. If you proceed, the Sprint fee credits against the build.
- 02
Build & integrate
Train anomaly and load-scheduling models on your plant's data, integrate with your existing SCADA, EMS, or BMS, and wire alerts into the control room workflow. No rip-and-replace of existing systems.
- 03
Production & optimisation loop
Live deployment with a plant dashboard, shift-level reporting, and a scheduled model review cadence — models retrained as your production mix, tariffs, or equipment changes.
FAQ
Frequently asked questions
What meter or sensor data do you need?
At minimum, sub-hourly energy meter readings at the equipment or circuit level. SCADA or BMS data adds accuracy for furnaces, compressors, and HVAC. The Discovery Sprint maps what you have and identifies any gaps that need filling before a model can be trained.
Does this replace our existing energy management system?
No. Banao integrates alongside your EMS or BMS, adding anomaly detection and predictive modelling to data you already collect. If you have an ISO 50001 programme in place, we layer on top of it rather than replace the programme.
How long until savings appear?
Load-scheduling and alert-driven interventions can show measurable impact within the first billing cycle after deployment. Combustion optimisation and compressed-air improvements typically take two to four months to stabilise and measure cleanly.
Can the model handle multiple sites or production lines?
Yes. A single deployment can span multiple meters, production lines, or plant sites, with cross-site benchmarking. Where sites have meaningfully different process profiles, we treat each as a separate baseline rather than forcing a single model across all of them.
What happens when our production mix changes?
The model is retrained on a scheduled cadence and whenever you flag a significant process change. The shift benchmarking layer normalises energy per unit of output, so mix shifts are accounted for rather than treated as anomalies.
Get started
Find out what your meters are not telling you
In 45 minutes we can scope whether your existing meter data is enough to baseline energy waste — and what the payback model looks like before you commit to a build.
Book a Discovery Sprint