Can it read messages in different languages and formats?

Yes. The apps read emails, PDFs and portal messages in the common business languages and flag the ones they are not sure about for a person to check.

How quickly is the first app in production?

A first version of a simple app runs within hours, a complete one within days, depending on the systems you connect. In the AI Bootcamp your teams build their first apps with us in five days and roll them out.

Does Operaide work with our existing systems?

Yes. There is no connector catalogue to wait for: each connection is built for your system through REST or MCP, including ERP, CRM, EDI, portals, older and in-house systems. Connections are set up once in the platform, and your apps read and write through them with the access rights you set, without secrets in the code.

Does our data leave the company?

Only if you decide so. Operaide runs on your own infrastructure, and for each step of an app you choose the AI model: a local model on your own hardware for data that must not leave, or a frontier model in your cloud tenant. An app can also use a local model to remove personal data before anything reaches a cloud model. Every run is traced, so you can see which data went where.

Which data does the forecast need?

Your sales order history from the ERP, the longer the better. Promotions, prices, listings and holidays make it more precise.

How do we know the forecast is right?

Every forecast is measured against what actually sold, article by article. Your planners see the error next to each number and how it develops.

Does it replace our planning system?

No. The forecast goes into your ERP or planning tool, and your planners stay in charge of every number.

Domain experts build no-code in the App Builder, and developers extend the apps in code in Operaide Studio. IT keeps control of AI models, data and access. If you like, our engineers build the first version with you, for example in the AI Bootcamp.

Which AI models can we use?

Commercial, European and self-hosted AI models: OpenAI, Anthropic, Mistral, Azure, Google, Amazon Bedrock, OpenRouter and more, or open-source models via Ollama or LM Studio. Your administrators configure them, and every step of an app can use its own AI model, chosen for its task.

The right stock in every warehouse.Forecast per item from your order history.

Spare parts, components or finished goods. AI forecasts demand per item and location from your ERP order history, so reorder points follow real demand.

Talk about your process More for Supply Chain & Logistics

Trusted by leading enterprises

The challenge

Out of stock on one item, too much of the next.

Demand is planned in spreadsheets with one safety factor for everything. Peaks and projects are spotted late, and the warehouse fills with slow movers.

Replenishment_DC_North_v5.xlsxLast edited by someone, 2 weeks ago

F2fx=AVERAGE(ERP_Export!C2:C13)*1.2

	A	B	C	D	E	F	G
1	Item	Stock	Nov	Dec	Jan	Safety stock	Note
2	Pump seal kit PS-14	310	120	130	140	200	Sales: +500 for project
3	Bearing 6204-2RS	95	180	190	170	200	out of stock wk 41
4	Filter cartridge FC-200	1,240	300	280	260	200
5	Valve body VB-80	860	10	8	12	200	slow mover?
6	Hydraulic hose 1 m	420	150	#REF!	150	200
7
8

Plan vs actual demand 2025, DC NorthPlanActual

JanFebMarAprMayJunJulAugSepOctNovDec

ERP_ExportStock_DCReplenishmentOld (do not use)+

The solution

Every item forecast, per warehouse.

Clear forecasts set your reorder points. Your planners decide on projects and peaks.

Demand forecast1,240 items · 5 to review

Items

Nov 2026 to Apr 2027 · from ERP order history

Pump seal kit PS-14Project announced, forecast +320
Filter cartridge FC-200Seasonal, as planned
Bearing 6204-2RSBelow safety stock in December
Hydraulic hose 1 mAs planned
Valve body VB-80Slow mover, lower the reorder point

Pump seal kit PS-14Your decision

Customer projectMaintenance season

JunJulAugSepOctNovDecJanFebMarApr

ActualForecastRange

Why this forecast

Customer project+320 units
Maintenance season+15 %
Trend vs last year+4 %

Your decision

Sales input: +500 units for a customer project
Forecast: +320 units, project spread over three months

Released forecasts set reorder points and purchase orders in the ERP.

Release forecastAsk sales

The solution

Every item forecast, per warehouse.

Clear forecasts set your reorder points. Your planners decide on projects and peaks.

Demand forecast1,240 items · 5 to review

Items

Nov 2026 to Apr 2027 · from ERP order history

Pump seal kit PS-14Project announced, forecast +320
Filter cartridge FC-200Seasonal, as planned
Bearing 6204-2RSBelow safety stock in December
Hydraulic hose 1 mAs planned
Valve body VB-80Slow mover, lower the reorder point

Pump seal kit PS-14Your decision

Customer projectMaintenance season

JunJulAugSepOctNovDecJanFebMarApr

ActualForecastRange

Why this forecast

Customer project+320 units
Maintenance season+15 %
Trend vs last year+4 %

Your decision

Sales input: +500 units for a customer project
Forecast: +320 units, project spread over three months

Released forecasts set reorder points and purchase orders in the ERP.

Release forecastAsk sales

Impact

In stock. Not overstocked.

Measured with you, in your process.

Without Operaide

Demand is planned in spreadsheets from experience, and peaks and drops are spotted late.
Items run out and customers wait.
Slow movers fill the warehouse.

With Operaide

AI forecasts demand from your sales order history in the ERP.
Peaks and projects are in the forecast before stock runs low.
Reorder points follow demand, so less capital is tied up in stock.

Why Operaide

Agentic AI is easy to start. Hard to run.

Operaide runs it in your company, under your control.

See the platform

Runs where your data is

On your own infrastructure, with the AI models you approve.

Every step traceable

Each decision is logged with its inputs. IT and audit see the full trace.

Grows with you

Live in days. From one automation to a complete application, on one platform.

FAQ

Questions about demand forecasting.

Want this for yourself?

Tell us about your process. In a demo we show you Operaide and how it would run it.

Talk about your process

More use cases

The right stock in every warehouse.Forecast per item from your order history.

Out of stock on one item, too much of the next.

Every item forecast, per warehouse.

Every item forecast, per warehouse.

In stock. Not overstocked.

Agentic AI is easy to start. Hard to run.

Runs where your data is

Every step traceable

Grows with you

Questions about demand forecasting.

Want this for yourself?

More for Supply Chain & Logistics.

Delivery monitoring

Delivery documents

All for Supply Chain & Logistics

The right stock in every warehouse.Forecast per item from your order history.

Out of stock on one item, too much of the next.

Every item forecast, per warehouse.

Every item forecast, per warehouse.

In stock. Not overstocked.

Agentic AI is easy to start. Hard to run.

Runs where your data is

Every step traceable

Grows with you

Questions about demand forecasting.

Can it read messages in different languages and formats?

How quickly is the first app in production?

Does Operaide work with our existing systems?

Does our data leave the company?

Which data does the forecast need?

How do we know the forecast is right?

Does it replace our planning system?

Who builds the apps?

Which AI models can we use?

Want this for yourself?

More for Supply Chain & Logistics.

Delivery monitoring

Delivery documents

All for Supply Chain & Logistics