Soffen AI

Soffen AI

AI that shows
its work.

Not one giant language model. A set of small, specialised engines that run on your device, predict what is likely to happen, and explain every recommendation in numbers you can check.

What “offline AI” means

Forecasts that work
without the internet.

Core forecasting and recommendations run on the device. A cloud LLM is not required for the inventory forecast — which keeps behaviour predictable and your data private.

Local statistical forecasting

Moving and weighted averages, recent trend and day-of-week effects.

Small local models

Lightweight on-device models only where they measurably help.

Rules engine

Safety stock, case packs, minimum orders and supplier constraints.

Local product data

Your own sales history — not someone else's averages.

If a language model is used, its only job is to turn structured results into natural language. It does not invent inventory numbers, and on-device and cloud AI are always labelled so you know when information leaves the device.

Architecture

Eight steps from data to decision.

Each engine has one job. If any of them is unavailable, inventory, sales, suppliers, orders and reports keep working.

  1. 01

    Local database

    Products, sales, deliveries and adjustments on your device.

  2. 02

    Data normalisation

    Fills gaps, flags missing days and unexplained adjustments.

  3. 03

    Feature engine

    Recent averages, trend, day-of-week effects, lead times.

  4. 04

    Forecast model

    Expected daily demand for each product.

  5. 05

    Stockout engine

    When stock is likely to run out.

  6. 06

    Reorder engine

    How much to order, rounded to case packs.

  7. 07

    Confidence engine

    How reliable each prediction is — and why.

  8. 08

    Insight generator

    Plain-language insights you can act on.

The result reaches you as an insight, a recommendation, or an answer from the assistant.

Explainability

Every recommendation
answers “why?”

Trust comes from evidence. Each recommendation lists the numbers it was built from, and you can accept, edit or dismiss it. Nothing is ever ordered silently.

Recommended order

24 units

Coca-Cola 500ml · ABC Distribution

Why?

  • Current stock is expected to fall below safety stock in 2 days.
  • Supplier lead time: 3 days.
  • Expected demand during lead time: 19 units.
  • Safety stock: 8 units.
84%High confidence

Confidence

Never presented as certainty.

Every prediction carries a confidence level based on measurable factors. When the data is thin, Soffen says so — and lowers its confidence.

Water 1.5L

91%High confidence

90 days of consistent sales, reliable counts.

Orange juice 1L

73%Medium confidence

Demand spiked last week; recent trend less stable.

Protein bar (new)

41%Low confidence

Only 5 days of reliable sales data. Initial estimate uses comparable products in the category.

Confidence goes down when there is…

  • Little sales history
  • Inconsistent sales
  • Frequent stock adjustments
  • Missing days
  • Unusual demand spikes
  • Uncertain supplier lead time

Forecast horizons

1, 3, 7, 14 and 30-day forecasts. Short-term forecasts generally receive higher confidence than long-term ones. Example for a product with steady sales:

Typical confidence by horizon: 1 day about 95%, 3 days 88%, 7 days 80%, 14 days 68%, 30 days 52%.

Insight types

Seven kinds of insight.

Short, specific, actionable. Written the way a careful store manager would say it.

  • Stockout risk

    “Likely to run out in 3 days.”

  • Demand increase

    “Sales are 18% above the recent baseline.”

  • Demand decrease

    “Sales have declined for 14 days.”

  • Overstock

    “Current inventory is significantly above recent demand.”

  • Slow moving

    “This product has had very low sales over the last 30 days.”

  • Expiry risk

    “24 units may remain when this product reaches its expiry date.”

  • Supplier timing

    “Current stock may not last until the next expected delivery.”

  • Not enough data

    “I don't have enough data to make a reliable prediction yet.”

Try it

The forecast, in your hands.

Target stock = daily demand × (lead time + 2-day review) + safety stock. The order is the difference from current stock, rounded up to a full case.

Choose a product

Forecast inputs for Coca-Cola 500ml
18 units
6.2 units/day
3.0 days
8 units

How the order is calculated

Target = demand × (lead time + 2-day review) + safety stock
Order = target − current stock, rounded up to a case of 6

6.2 × (3 + 2) + 8 − 18 = 21 → 24 units

Coca-Cola 500ml

Critical

Expected stockout

~2.9 days

at 6.2 units/day

Recommended order

24units

4 cases of 6 · ABC Distribution

AI confidence

84%High confidence

Based on 30 days of sales data.

Likely to run out ~0.1 days before a delivery ordered today could arrive.

01530Today2d4d6d8d10d12d14dSafety stock 8Reorder point 27Delivery · day 3 (+24)12 left on day 5 · next order arrives⚠ Stockout ~2.9d

  • Projected stock
  • With recommended order
  • Safety stock
  • Reorder point
  • ⚠ Stockout

Coca-Cola 500ml: 18 units in stock, selling 6.2 per day. Expected stockout in ~2.9 days. Supplier lead time 3 days, safety stock 8 units, reorder point 27 units. Recommended order: 24 units, arriving on day 3. Likely to run out ~0.1 days before a delivery ordered today could arrive. Confidence 84%.

Tip: hover, tap, or focus the chart and use ← → to read exact values.

Why this recommendation?

  • Current stock18 units
  • Average sales6.2/day
  • Recent demand+18% vs 30-day avg
  • Supplier lead time3 days
  • Demand during lead time19 units
  • Safety stock8 units

Guardrails

What the AI will never do.

  • Invent numbers

    Stock levels, sales, lead times, prices and forecasts come only from your store data.

  • Order silently

    Recommendations are suggestions. You review, edit and approve every purchase order.

  • Change prices

    Expiry suggestions never change prices unless you explicitly set up an integration.

  • Overwrite inventory from a guess

    Future shelf scanning will always ask you to review before stock changes.

  • Claim to “learn” without learning

    Accepted, edited and dismissed recommendations are stored locally; we'll only say the model learns when it actually uses them.

  • Become a single point of failure

    If AI is unavailable, the store keeps running and your inventory data stays safe.

Know what's next.
Before it's gone.

Start with your own products or explore the demo store. No account, no card, no internet required.