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Agriculture · Commodity price forecasting

The sell-or-store call costs money when it is a guess

Banao builds commodity price-forecasting models for agricultural traders, FPOs, and food processors — trained over mandi arrivals, rainfall, satellite crop-condition data, and export-quota signals to put a defensible forward number on where a commodity trades in the coming days and weeks.

The output reaches the right desk before the market opens: a per-commodity, per-mandi forward view that a procurement head or aggregator can act on that morning, not a historical chart delivered three days later.

What a Banao price-forecasting deployment includes

A forecast that changes a sell-or-store decision has to be right more often than the trader's instinct, and it has to arrive before the decision window closes. These are the components that make that happen.

Mandi arrival and price feed

A live, structured ingest from agmarknet and regional mandi data sources — arrivals, trade volumes, and daily prices — cleaned and timestamped, so the model trains and runs on what actually hit the floor, not headline averages.

Satellite and weather-driven supply model

NDVI crop-condition passes, rainfall anomaly, and heat-stress indices processed as supply-side signals — so a dip in NDVI across wheat-belt districts shows up in the forecast before it shows up in arrivals data.

Export and policy signal layer

Export quotas, import-duty changes, MSP notifications, and buffer-stock releases wired into the model as structural drivers — the policy events that move a mandi floor price overnight.

Short-range actionable forecast

A 3-to-21-day forward price range by commodity and mandi, updated daily — wide enough to cover real decision horizons, short enough that the error budget stays within the margin a trader can work with.

Trader and FPO alert system

Push notifications when a commodity forecast crosses a threshold the trader or aggregator sets — so the sell-or-store decision does not require someone to open a dashboard every morning and read a number.

Model accuracy and error reporting

A weekly accuracy log per commodity — so the trader knows exactly where the model has earned trust, which markets are harder to call, and when to weight their own judgement higher.

Where price forecasting is running

Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified in the field.

A grain aggregator and trading house

Daily mandi price forecast across four commodities

  • ··%forecast accuracy within 5% of outturn price
  • ··%sell-side timing improvement
  • ··daysadvance notice of significant price moves

The aggregator made sell-or-store calls on paddy, maize, and two pulses based on the mandis their procurement team visited that week. Banao built a forecasting pipeline over four years of agmarknet data, satellite crop passes, and rainfall records, delivering a daily forward view before the first mandi opened.

We run AI on our own decisions before we build it for yours

Banao is a ~300-person engineering company that runs on its own AI before shipping it to clients. InterviewGod screens every engineering hire. Vikaas drives our demand generation. Those systems face real commercial consequences every week — which is the only way to harden a model before it reaches a client's operation.

A price-forecasting model that influences where crop moves has to earn that right. We hold it to the same bar we hold every system we depend on ourselves.

  • InterviewGodScreens every Banao engineering candidate before interview.
  • VikaasRuns Banao's own demand-gen pipeline and lead qualification.

When commodity price forecasting is the wrong investment

Price models are bought on hope and abandoned when accuracy disappoints. We would rather set the right expectations before a build starts:

  • Thin mandi data: a commodity that trades in two or three mandis with irregular reporting gives a model too little signal. We will tell you the data is not there before quoting a build.
  • Single-season buyers: if a trader buys one commodity once a year, the forecast window is short and the ROI maths rarely works. A subscription to a commodity desk is cheaper.
  • Highly local pricing: some crops price primarily on personal buyer relationships and last-minute negotiation, where a quantitative model adds little over an experienced broker.

How we start — price the forecast before you build it

We start by auditing your actual data before quoting a model — the accuracy you can achieve depends entirely on the history you have.

  1. AI Discovery Sprint2 weeks · fixed price

    We assess your available mandi, satellite, and policy data, run a baseline forecast on a subset of commodities, and hand back an accuracy estimate and ROI model — yours to keep. If you proceed, the Sprint fee credits against the build.

  2. Build

    Data pipeline first: agmarknet ingest, satellite pass processing, policy-signal integration. Then the forecast model, calibrated to your commodities and mandis, with a trader-facing dashboard and alert system.

  3. Production and ongoing calibration

    Live deployment with a weekly accuracy log and model update as each season's data comes in. The model reports its own error so traders know when to weight it and when to override.

Frequently asked questions

For most field crops and mandis, three to fourteen days is the reliable window — beyond that, weather and policy uncertainty compound faster than the model can compensate. We quote the accuracy by commodity and horizon in the Discovery Sprint so you know the useful range before committing to a build.

It can start on public sources — agmarknet, IMD weather data, satellite passes, and published government policy notifications. Proprietary trading data and warehouse receipts improve accuracy and can be added once the public-data baseline is validated.

We build models for the specific commodities a client trades — paddy, wheat, maize, pulses, oilseeds, cotton, perishables. Each commodity has different signal drivers and data density, which is why the Discovery Sprint audits your specific crop mix rather than quoting a generic model.

Accuracy depends on the commodity, the mandi, and the data history available. Established mandis with four or more years of reliable arrival data typically reach within 5–8% of the outturn price for a 7-day horizon. We publish the model's own accuracy log weekly so traders see exactly where it earns its keep.

The forecast exports via a daily data feed or push notification — most teams start with an alert on a phone and a dashboard. Where an ERP or trading platform has an API, we wire the forecast directly so it surfaces in the workflow the procurement team already uses.

Find out what your sell-or-store calls are actually worth

Bring your commodity mix and your worst price miss from last season. In 45 minutes we will map the AI opportunity and the ROI behind it.

Book a 45-min scoping call