Perimattic
Planner working in a data interface

AI Copilot

Ask a question, get a grounded answer in under two minutes

Planners ask questions in plain language and get grounded, source-traceable answers from live forecast and inventory data — no query language, no report building, no analyst in the loop.

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<2 min

Time to answer any demand planning question with full source traceability

0

Custom queries or report builder skills required to get a grounded planning answer

Live

Answers drawn from live forecast, inventory, and signal data — not cached reports

What does the AI Copilot do in demand planning software?

Demand OS AI Copilot lets planners ask questions in plain language — 'which SKUs are at risk of stockout this week?' — and get traceable answers from live forecast and inventory data. No query language, no report building, no analyst in the loop. Every answer cites the specific data points it drew from, so planners can drill into the source before acting.

The Problem

Why planners spend more time finding the answer than acting on it

45-minute wait for a simple forecast question
Getting an answer to 'what drove the forecast change on SKU 4471?' from a standard planning system requires an analyst, a BI query, a pivot table, or a scheduled report. By the time the answer arrives, the meeting it was needed for has ended.
Query languages planners have not been trained to use
SQL, MDX, DAX, and proprietary report builder interfaces are tools designed for data analysts, not demand planners. When planners cannot query the system themselves, every data question creates an analyst dependency.
Answers that live in reports, not the planning workbench
Insights that require leaving the planning system — opening a BI tool, running a report, exporting to Excel — break the planning workflow. The answer the planner needs should be available where the planning decision is made.
AI assistants that hallucinate rather than ground answers
Generic AI chat tools applied to planning data produce fluent-sounding but unverifiable answers. A planning AI that cannot cite its source is a liability, not an asset — planners need to know where the answer came from.

What the AI Copilot Includes

Grounded, traceable planning answers, in the workbench

The moment a planner has to leave the planning system for an answer — a BI dashboard, a Slack message to an analyst, an Excel macro — the workflow breaks. AI Copilot keeps the question and the answer in the same place the planning decision is made.

COPILOT CAPABILITY
Natural Language Query
Planners type questions in plain English — 'which SKUs have less than 5 days of cover heading into the weekend?' — and the Copilot queries live forecast, inventory position, and open purchase order data to produce a structured answer. No query language required.
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COPILOT CAPABILITY
Source-Traceable Answers
Every Copilot answer cites the specific data points that produced it — the forecast value, the inventory position, the signal that triggered the alert. Planners can drill into the source before acting on the answer.
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COPILOT CAPABILITY
Full Data Layer Access
The AI Copilot has access to the same forecast, inventory, demand sensing, scenario, and audit trail data that appears in the planner workbench — including factor attribution from Explainable AI, answered in one interface.
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In Practice

A planning team eliminates analyst wait time during a peak season crunch

The Situation

A consumer electronics distributor ran its peak planning cycle with a four-person planning team and a two-person analytics team. During peak, the analytics team received 30–40 data requests per week from planners — questions about at-risk SKUs, forecast changes, and replenishment timing. The average turnaround was 4–6 hours, and many answers arrived after the decision window had closed.

What Demand OS Did

The AI Copilot was configured with access to the live forecast, inventory position, open purchase orders, and demand sensing alert data. Planners began asking questions like "which SKUs are at risk of stockout in the next 10 days?" directly in the planning workbench. Each answer cited the specific data points that produced it.

The Outcome

Analyst data request volume from planning dropped by 65% during the following peak cycle. Planners received answers in under 2 minutes rather than 4–6 hours. The analytics team redirected time from data retrieval to scenario modeling and business analysis — work that required human judgment rather than query execution.

Benchmark Results

What planners gain from in-workbench answers

<2 min

to answer any demand planning question with full source traceability

60%

reduction in analyst data request volume when planners can self-serve planning questions

Live

data freshness on every Copilot answer — drawn from the current planning state, not yesterday's export

Frequently Asked Questions

AI Copilot questions answered

What kinds of questions can the AI Copilot answer?+
The AI Copilot can answer questions about any data available in the Demand OS platform: current forecast values and recent changes, inventory positions and days of cover by SKU and location, open purchase orders and expected receipts, demand sensing alerts and the signals that triggered them, scenario assumptions and inventory impacts, and factor attribution from the Explainable AI module. It cannot answer questions about data outside the Demand OS data layer — external market data or ERP data not yet ingested.
How does the Copilot ensure answers are accurate and not hallucinated?+
The AI Copilot uses a retrieval-augmented approach — it queries the live planning data to construct its answer rather than generating a response from model internals. Every answer includes the specific data points it drew from, and planners can drill into the source record from the Copilot response. The system is designed to say "I don't have data to answer that" rather than produce a fluent-sounding but unverifiable response.
Does the AI Copilot learn from planner interactions over time?+
The Copilot improves its query routing and answer formatting based on usage patterns within a customer environment, but it does not use planner interactions to update its underlying model in ways that could change the factual basis of answers. The factual grounding always comes from the live planning data, not from accumulated conversation history.
Can the AI Copilot take actions on behalf of the planner, or does it only provide information?+
In the current implementation, the AI Copilot is an information and analysis tool — it surfaces data, highlights exceptions, and explains patterns. It does not take autonomous actions such as approving forecasts, generating purchase orders, or modifying planning parameters. All planning actions require planner review and confirmation in the planning workbench.
Is the AI Copilot available to all users on the Demand OS platform?+
The AI Copilot is available to all licensed Demand OS users. Access to specific data sets within the Copilot follows the same permission structure as the planning workbench — a user who does not have access to a product category in the workbench cannot query that category through the Copilot.

Get Started

Ready to plan with a grounded copilot?

See the AI Copilot answer questions on your own forecast and inventory data — live pilot in 4–8 weeks. You leave with accuracy benchmarks, a rollout plan, and published pricing.