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02 Service · AI · Predictive
Forecast · next 14 daysm³/day
D+12,140
D+42,460
D+72,880
D+142,710

Forecast yield.
Before the
feedstock arrives.

An AI engine that learns each plant's behaviour and forecasts gas output 7 to 14 days ahead. Procurement plans against forecasts. Engineers spot drift early. Owners stop being surprised by the monthly invoice.

The signature surface

Six inputs. One forecast.

The AI engine learns from six input streams collected at every BioSarthi-monitored plant. It outputs a 14-day forecast that updates daily and quantifies its own confidence.

Trained on operating data from BioSarthi-monitored plants across Indian feedstocks — press mud, paddy straw, FOG, MSW, manure.

Feedstock mix
Daily kg by waste type, 12-month rolling baseline
Weather window
Temperature, humidity, monsoon flag, IMD feed
Digester telemetry
pH, temp, gas flow, 30-second cadence
Retention time
HRT and solids loading, derived
Maintenance state
Last clean, scrubber state, agitator hours
Forecast confidence
P50 / P10 / P90 bands, 7d, 14d horizon
How it works

Inputs to forecast, in four stages.

The AI sits behind the monitoring layer. It does not require new hardware, only enough operating history to learn a plant's behaviour. Most plants reach reliable forecasts within 60 days.

A.01
Ingest history
12 months of feedstock logs, gas yield, downtime. Imported once.
A.02
Learn baseline
Per-plant model trained on local feedstock and seasonality. 60-day learning.
A.03
Forecast daily
P50 / P10 / P90 bands for next 7 and 14 days. Updated every morning.
A.04
Drift alerts
Forecast vs actual diverges, the engine flags the cause. Operator acts.
What's covered

A complete forecast, one tile each.

Each tile answers a question someone in your business asks every week. Procurement, engineering, finance, compliance, all reading from the same forecast.

F.01
Daily gas yield
next 14 days
P50 / P10 / P90 bands. Per-plant accuracy reported transparently in your dashboard.
F.02
Methane percentage
next 7 days
Drift forecast against feedstock mix. Early warning for digester upsets.
F.03
Feedstock requirement
weekly plan
Kg by waste stream needed to hit yield target. Procurement reads this.
F.04
Downtime risk
rolling 14d
Likelihood of unplanned outage based on telemetry trends.
F.05
Revenue forecast
monthly
Yield × tariff × contracted offtake. Finance reads this for cashflow.
F.06
Compliance gap
rolling 30d
Predicted shortfall against SATAT contract. Acted on before penalty.
Sizer · interactive

What's a forecast worth at your scale?

Drag to set your monthly gas output. We size the cashflow visibility, downtime risk avoided, and procurement savings the forecast unlocks.

Monthly gas output
500tonnes CBG
502000
Forecast horizon
14 days
Forecast accuracy
93.6%
Procurement saved
₹14.2L / mo
Downtime avoided
2.1 days / mo

Audit-ready forecasts.

Forecasts are explainable and exportable. Each forecast comes with the input weights that drove it. Patent-pending model architecture, trained exclusively on Indian feedstock.

Explainable
Each forecast names the top three inputs that drove it. No black boxes.
P50 / P10 / P90 bands
Confidence intervals on every prediction. Plan against the band, not the point.
Trained on Indian feedstock
Press mud, paddy straw, FOG, MSW, manure — feedstock libraries we keep adding to.
API + CSV export
Forecasts pushed to your ERP daily. Nothing trapped in our cloud.
GOBARdhan / SATAT-aligned
Forecast formats accepted by cluster reviews and compliance teams.
Versioned models
Every forecast tagged with the model version. Reproducible audits.
vs. the alternatives

Spreadsheets. Generic ML.
Or this.

Dimension
BioSarthi
DIY / In-house
Generic vendor
No system
Forecast horizon
14 days, daily refresh
Last month × growth
7 days, weekly
Monthly close
Trained on Indian feedstock
Yes, deployed plants
n/a
Generic timeseries
n/a
Confidence bands
P50/P10/P90
Single number
Point forecast
None
Explainability
Top-3 input attribution
n/a
Black box
n/a
Drift detection
Auto vs telemetry
Manual
Manual
None
Time to first forecast
60 days
Day one
16+ weeks
n/a
Patent coverage
Patent-pending
None
None
None
From decision to dispatch

A forecasting rollout, end to end.

Five phases from first data import to forecasts feeding your ERP. If you already have BioSarthi monitoring, week 1 collapses.

01
01 / Data import
Week 1
12 months of historical operating data. CSV or API.
02
02 / Model fit
Week 2–3
Per-plant training. Cross-validated on held-out months.
03
03 / Shadow run
Week 4–8
Forecasts produced daily but not acted on. Accuracy benchmarked.
04
04 / Go-live
Week 9
Forecasts feed ERP, procurement, compliance reports.
05
05 / Continuous tune
Ongoing
Models retrained monthly. Drift watched, accuracy reported.
Snapshots

What it looks like, in the dashboard.

Visual landmarks of a forecasting deployment, the views your procurement, engineering, and finance teams open every morning.

Forecast curve
14-day yield with P10/P50/P90 bands
Forecast vs actual
Last 30 days, residuals plotted
Procurement plan
Weekly feedstock kg by stream
Drift alert
Top-3 inputs driving divergence
In their words

Voices from live plants.

"The forecast is the procurement plan now. We stopped over-buying press mud the moment we trusted the 7-day band."
Procurement lead
CBG plant, live 2025
"First time finance has a number for next month they actually believe. Cashflow planning got real."
MSA BioEnergy
Operator partner, live 2025
"When forecast diverges from actual, the engine tells us which input changed. We caught a feedstock substitution before it cost us."
Plant engineer
Punjab, monitoring + AI since 2024
Frequently asked

Things owners ask first.

Twelve months ideal, six months workable. Below six months we run a generic Indian-feedstock baseline and tune as your plant data accumulates.
No. Each plant gets its own model. The architecture is shared, the parameters are local. A press-mud plant in Punjab and an MSW plant in Pune learn different things.
Targeting single-digit mean absolute error on the 14-day horizon. Actual per-plant accuracy is reported transparently in your dashboard — we don't hide the misses.
Not strictly. We can ingest your existing telemetry. But monitoring + AI together is the supported configuration.
The model detects the shift via the drift signal, flags it, and retrains on the new mix within two weeks.
Your operational data stays yours. Encrypted at rest, exportable on termination.

Want forecasts for your fleet?

30 minutes with our data team. Walk out with a forecast accuracy estimate for your plants, an integration plan, and a clear next step.