Data ingestion
Upload utility CSVs, meter exports and basic production schedules. Validate the data before modeling.
AI energy and acoustic intelligence for manufacturing SMEs
VoltNudge AI
Reduce electricity peaks, operating cost and CO2e with human-approved recommendations from meter, schedule and optional acoustic machine-state data.
CSV-ready MVP
8-12 week pilot
Acoustic AI
- Build
- Founder-led MVP
- Stage
- MVP build
- Pilot
- 8-12 weeks
- Focus
- Peak load reduction
Target outcome
Lower peaks, lower bills, cleaner reporting
1
One product for energy efficiency, acoustic AI and SME productivity.
2
Built for operators who need measurable impact, not another vague dashboard.
3
Starts with software and CSV data. No hardware replacement required in MVP.
Inside the product
A clear operating view, not another abstract analytics page
The first version focuses on the practical loop a factory can actually use: import data, detect machine states, forecast peaks, approve safe nudges, and verify the result.
Connect utility, tariff and schedule data
Flag peak-risk windows before they become expensive
Use acoustic signatures to explain machine state and idle patterns
Turn recommendations into an operator-approved action log
Example operator view
Energy forecast
Peak risk
High
Action window
14:00-17:00
Confidence
Model score
01
Stagger compressor restart by 18 minutes
02
Move flexible batch job outside peak tariff window
03
Review standby load on Line B before evening shift
Product
Practical AI for factories that need savings fast
VoltNudge AI is not a passive dashboard. It turns meter, tariff, production and optional acoustic machine-state data into specific operational nudges, then verifies what actually changed after the action.
Peak forecasting
Predict costly load peaks and highlight the machine or schedule patterns behind them.
Nudge engine
Rank safe schedule changes by savings, risk and operational feasibility for human approval.
Acoustic intelligence
Use machine sound signatures to identify running, idle and abnormal states when energy data needs context.
Measurement and verification
Compare baseline and post-action results and generate decision-ready evidence packs.
SME fit
Designed for bakeries, CNC shops, plastics, packaging and food production lines.
Reporting
Export savings in kWh, peak kW, cost and CO2e for operators, pilots and partners.
Input
Meter exports, tariffs, schedules, audio samples
AI layer
Forecast peaks and rank safe nudges
Output
Operator actions and verified savings
Data model
Start with the data most factories already have
The MVP does not need machine replacement. It can begin with exports and simple templates, then become more automated as each site proves value.
Meter data
15-minute or hourly electricity usage, peak intervals and site-level demand profile.
Tariff data
Peak prices, demand charges, time-of-use windows and utility billing structure.
Production schedule
Batch timing, line windows, flexible jobs, maintenance periods and shift constraints.
Context signals
Weather, carbon intensity and operational notes that explain unusual load behavior.
Acoustic signals
Optional machine sound samples to classify idle, running and unusual operating states.
Technical approach
Five layers that make the recommendations explainable
VoltNudge AI is designed to show why a recommendation exists, what constraint it touches, and how the result will be measured after the operator approves it.
Baseline model
Builds a normal-load profile from historical meter data so future savings are compared against a clear baseline instead of a guess.
Peak forecast
Predicts likely peak windows from usage patterns, tariff windows, shifts and production timing.
Acoustic state detection
Uses optional sound patterns to identify machine state, idle periods and anomalies that meter data alone may not explain.
Constraint filter
Removes actions that would violate production rules, shift boundaries, maintenance windows or operator-defined constraints.
Verification report
Compares baseline and actual performance, then summarizes energy, peak demand, cost and CO2e.
Best first pilot
What makes a site a strong fit
- At least 4-12 weeks of meter or utility export data.
- Visible peak charges, time-of-use tariffs or high energy volatility.
- Machines with recognizable sound states such as idle, running, compressor start or abnormal vibration.
- Some flexible work: batch jobs, compressor timing, cooling, cleaning or non-critical starts.
- A decision owner who can approve small schedule changes.
Not a fit yet
Where VoltNudge should wait
- Sites with no accessible meter or billing history.
- Sites where recording any machine audio is not permitted.
- Fully continuous processes where every load is safety-critical.
- Teams that need autonomous control before validating recommendations.
- Projects where savings cannot be measured after the action.
Problem
Most smaller factories see the bill after the damage is already done
- Peak demand charges are hard to predict from scattered data.
- Production teams do not want energy tools that interrupt operations.
- Enterprise systems are too heavy and expensive for SMEs.
- Decision makers need impact they can measure in a short pilot window.
Solution
VoltNudge AI gives practical recommendations, then proves the result
- Import meter, tariff, production and optional acoustic machine-state data.
- Forecast peaks and identify avoidable waste.
- Recommend safe schedule adjustments.
- Verify the savings and export the report.
Pilot deliverables
What a factory receives after the first run
Energy baseline
Clean profile of the site's normal electricity load and peak windows.
Machine-state map
Optional acoustic view of running, idle and unusual operating states.
Peak map
Which schedule patterns, machines or shifts are likely driving costly peaks.
Action shortlist
Ranked, human-approved nudges with expected savings and operational risk.
Savings report
Before/after view in kWh, peak kW, cost and CO2e for internal decisions.
Impact
Why the story is credible
The value is simple: real energy pain, a narrow technical product, a fast pilot, and measurable outcomes that can be repeated across manufacturing sites.
What partners see
Climate impact, AI novelty, SME productivity, and a clear measurement plan.
What the pilot builds
Data pipeline, acoustic state detection, forecasting, recommendation logic, pilot support and verification.
Where it fits
Energy operations, SME decarbonization, industrial analytics and cloud-native AI infrastructure.
Energy cost reduction
Peak load forecasting
Manufacturing SMEs
Human-approved AI
Acoustic state detection
CO2e reporting
Fast pilot deployment
Use cases
Built for sites where small timing changes matter
Industrial bakeries
Batch ovens, refrigeration and predictable production windows.
CNC shops
Machine starts, compressor loads and flexible job sequencing.
Packaging lines
Line scheduling, standby patterns and shift-level load peaks.
Food processors
Cooling, batch processing and high-cost energy windows.
Pilot plan
Short enough to run fast, real enough for customers
The pilot is deliberately narrow: prove that the data can produce useful recommendations before adding hardware, live integrations or automatic control.
Discovery
Collect sample meter and schedule data, validate the first pain points and define success criteria.
MVP
Build ingestion, basic forecasting, a first recommendation model and a clean reporting flow.
Pilot
Run one or more sites through the workflow, compare baseline and post-action performance, and document the results.
Commercialization
Convert successful pilots into paid subscriptions and partner channels with energy consultants or associations.
Inputs needed
Meter export, tariff structure, rough production schedule, optional machine audio samples and one short operations interview.
Operator effort
About 2-3 short review sessions during the first analysis cycle.
Decision output
A ranked action list with expected impact, confidence and operational risk.
Roadmap
From data upload to site-level energy and acoustic co-pilot
Manual-data prototype
CSV templates, baseline modeling, first acoustic classifiers, first peak forecasts and report structure.
Pilot workflow
Recommendation ranking, operator review, pilot action log and savings comparison.
Connected deployment
Automated imports, site dashboards, advisor access and integration with energy partners.
Evidence
What the first pilot can credibly prove
2-3
pilot sites targeted in the first round
8-12
week pilot window with human-approved recommendations
kWh / kW / CO2e
the three core impact measures
Founder
Founder-led energy AI project
Founder: Степан Оносов
Email: hello@voltnudgeai.xyz
Public contact: hello@voltnudgeai.xyz
Early conversations are handled directly with pilot partners, energy advisors and product collaborators. Customer data access is agreed separately for each pilot.
Contact
Ready for pilots and partnerships
The cleanest public inbox is hello@voltnudgeai.xyz. Use it for pilot partners, energy advisors and early product conversations.
Does the MVP require new hardware?
No. The first version works with existing CSV exports and schedule data.
Does it control machines automatically?
No. The first version recommends actions for human approval.
What data is enough to start?
Meter or utility exports, tariff details and a rough production schedule are enough for a first analysis. Acoustic samples are optional and add machine-state context.
Why add acoustic AI?
Machine sound can reveal idle, running or abnormal states that meter data alone may not explain, making recommendations more practical for operators.
How are savings checked?
The pilot compares a baseline profile against actual post-action performance in kWh, peak kW, cost and CO2e.
What stage is the startup?
Founder-led, MVP-focused, pilot-oriented and ready for early technical validation.
What is the main buyer?
Small manufacturers with flexible loads and a real energy-cost pain point.
What makes this credible?
Climate impact, AI novelty, SME value, short pilot cycles and measurable outcomes.