SSmart Automation
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AI-powered sales and demand forecasting

Order too much and your cash freezes in inventory. Order too little and you lose sales and customers. AI models forecast demand per item, accounting for seasonality, trends, promotions — even the weather.

Sound familiar?

  • Purchasing is planned by gut feeling or by last month's numbers
  • The warehouse is full of slow movers while bestsellers run out mid-season
  • Staffing plans don't match the actual workload
  • Spreadsheet forecasts are stale before anyone finishes reading them

How AI solves it

  1. 01

    An AI model trains on your sales history and accounts for seasonality, trends, promotions and external factors.

  2. 02

    Forecasts are built per SKU and per location — not as one blended average.

  3. 03

    The system proposes purchase volumes and flags shortage and overstock risks.

  4. 04

    Forecasts refresh automatically as new data arrives.

Concrete scenarios

Season purchasing

A 1–3 month forecast per item, seasonality and promotions included.

Staff planning

Load forecasts by day and hour — shifts planned around the expected flow.

Shortage risk

The system flags items that will run out before the next delivery — in advance.

Promotion evaluation

What a discount will do: demand forecast in "with promo" and "without" scenarios.

How the system works

  1. 1

    History collection

    sales, stock, promotions, seasonality

  2. 2

    Model training

    on your data, per item

  3. 3

    Forecast

    volumes per SKU and location

  4. 4

    Recommendations

    purchasing, shortage and overstock risks

  5. 5

    Refresh

    the model retrains as new data arrives

Input and output

What we need from you

  • A year or more of sales history (ERP, CRM, POS — exports are fine)
  • The promotion calendar and product catalog

What you get

  • A forecast per item and location
  • Purchase volume recommendations
  • Shortage and overstock risk alerts

What we integrate with

  • ERP
  • accounting systems
  • Excel / Google Sheets
  • POS systems
  • Power BI
  • APIs

Results

  • 85–95% forecast accuracy depending on the niche
  • 20–30% less overstock
  • Fewer missed sales due to out-of-stock items
  • Purchase planning takes hours, not weeks

Case study

A retail chain: purchasing was planned "by last month", shelves went empty at seasonal peaks, and after the season the warehouse sat overstocked.

We trained a model on three years of sales history with seasonality and promotions; the system issues weekly purchasing recommendations and flags risks per item.

  • About 90% forecast accuracy on top sellers
  • Warehouse overstock down by a quarter
  • Purchase planning takes hours instead of two days

Pricing and timelines

We don't publish a price list: the range of tasks is too wide for one honest number. Instead — a transparent model:

  1. 01

    Consultation — free

    30 minutes: we walk through the process, estimate the economics and scope the first phase.

  2. 02

    Estimate and plan

    Scope, timeline and cost fixed before the start — no hidden items.

  3. 03

    Prepayment to start

    A fixed advance covers tooling and environment setup. Infrastructure costs (server, APIs) are billed separately when your solution needs them.

  4. 04

    Final payment on delivery

    The closing payment comes after the agreed scope is delivered.

Guaranteed: the first working phase ships 2 weeks after the start. The rest follows a plan fixed in advance.

Cloud or your perimeter

Cloud deployment

Fast start: models via cloud APIs, the system on our or your VPS. Fits when perimeter requirements are standard.

On-premise deployment

Models and data fully inside your perimeter — on your hardware or a dedicated server. For healthcare, finance and anyone whose data can't leave.

Hybrid

Sensitive data is processed locally, the rest in the cloud. A balance of cost and requirements.

Risks and limitations

  • Forecasts need history: with less than a year of data accuracy is lower — we show it honestly in a backtest before rollout.
  • Rare and brand-new items forecast worse — separate rules apply to them.
  • A forecast is a tool, not an oracle: sharp external shocks (a new competitor, supply disruptions) won't be predicted.

Common questions about this task

Our data lives in Excel — will that work?

Yes. Exports are enough to start; automated data collection is set up as the project goes.

How will you prove the forecast works?

A backtest: we train the model on older data and validate it against a period whose outcome is already known — accuracy is visible before launch.

Does the forecast refresh itself?

Yes, on a schedule: the model retrains and recalculates recommendations without human involvement.

Want the same result?

Send a request — in 30 minutes we'll analyze your process and tell you how much time and money automation will save.

Free consultation