Chat
Why did margin fall? Which products are slowing down? Where does paid stop being profitable? Ask in plain language and get the answer from live, reconciled commercial data — with the numbers behind it.
Every answer on the same modeled data as your reports
AI agents can make mistakes. Please review the results.
Trusted by commercial teams at leading brands
Sound familiar?
The question is simple. What stands between you and the answer is a ticket queue, three competing definitions, or an AI that has never seen your real numbers.
Maja Mon 09:12
Can someone pull margin by market for the QBR? 🙏
The QBR is Wednesday
Sara 09:02
Finance sheet says CM was 14.2% last week
Jonas 09:04
The dashboard has it at 18.7%?
🧵 4 replies
Karin 09:07
Last month’s deck said 16.1%…
Three tools, three definitions, one meeting
What’s our contribution margin by market?
Your contribution margin is a healthy 23.4% across all markets — nicely above benchmark!
✕ The export is 12 days old
✕ Returns and freight aren’t in the file — it guessed
What you can ask in Dema
Every card is a real question from the prompt library. Open the use case behind it to see the full answer — what it checks, what comes back, and how teams act on it.
Why it works
Chat is only useful when the number underneath can be trusted — the same reconciled profit figure, the definitions your team set, and an agent that already knows how you work.
Orders, spend, COGS, fulfilment and returns are reconciled into one profit figure. Marketing, finance and merchandising stop arguing about which export is right.
CM3 = Revenue − COGS − fulfilment − marketing
Defined by your team — not inferred
Organization instructions
106/1500“Profit means CM3. Compare weeks Monday to Sunday.”
Your instructions
“Show weekly trends by default.”
budget-move-guardrails
Runs identically for all 8 people
Orders, inventory, returns, marketing and omnichannel data connect once and stay maintained. Nobody on your team owns the pipeline.
And dozens more via APIs and direct integrations.
View all integrationsFrom the very first prompt
“Dema worked perfectly right out of the box. It's the first tool that truly understands our data from the very first prompt.”
One foundation, three ways to work
Chat, Agents, and Apps all run on the same modeled commercial data — the answer, the workflow, and the application never disagree.
Anomaly scan completed
06:00
Report delivered to Slack
06:02
Budget change prepared
06:04
Meta DE
+€9.8k/wk
Status
Awaiting approval
Describe the report, monitor, or routine once. An agent runs it on schedule, delivers the result, and executes approved changes.
Explore Agents
Sell-through planner
Team app
Reorder Olive Overshirt
240 units
Markdown Rust Tee
−15%
Publishes to the storefront
PublishPlanners, dashboards, review queues — describe the workflow and get a working application on live data, shared with the team.
Explore Apps
Enterprise-ready
Where your data is processed, how it is protected, and what an agent is allowed to change are controls you set — not defaults you inherit.
Choose where your data is processed. Model inference and data pipelines run on European or US infrastructure with full residency control.
Your data is never used to train models. Processing stays in your chosen region and runs in isolated, encrypted sessions.
New reasoning models roll out as they ship, so your agents keep improving automatically without migration work.
Each agent has its own scope and integrations. Actions can require a named approval before anything changes.
Questions
A generic assistant reasons over whatever file you pasted, as of whenever it was exported, with definitions re-explained each time. Dema answers from live modeled commercial data with your metrics defined once — and flags missing data instead of guessing.
Every answer runs through the same semantic layer: orders, costs, returns, inventory, marketing, and customer behavior aligned before AI touches them. Two people asking the same question get the same number.
Anything across the commercial picture: why margin moved, which products are slowing down, where paid growth stopped being profitable, how return costs track against plan — with drill-downs by market, channel, product, and customer cohort.
Missing sources and broken definitions get flagged instead of quietly published. The agent tells you what it would need, rather than inventing a confident number.
Yes. Any investigation can be scheduled as a recurring agent or turned into a shared application — the same data and definitions carry over, so nothing gets rebuilt.
No. You ask in plain language and the semantic layer handles the joins, definitions, and correctness underneath. Analysts can still go deeper when they want to.
Bring one commercial job
