AI that grows profit, not just answers questions
Dema connects your commerce data, keeps it correct, and puts your team and agents to work on the decisions that drive profit — every day.
Claude Opus 4.8
AI agents can make mistakes. Please review the results.
Turning commerce data into decisions for category-defining brands
From months to one command
“What took two people three to four months now runs in a single command. We built 20 workflows in three weeks without writing a line of code.”
1 013 013
AI actions this month
More than one million AI actions run in Dema every month, from brands like Acne Studios, Axel Arigato, and Represent.
+40%
Profit growth
Brands using Dema agents grew profit by 40% year over year, on average.
How Dema works
01
Connect your commerce data
Store, ad platforms, logistics, and CRM — modeled into one correct commercial dataset.
Which products drove margin last week?
Claude Opus 4.8
Answer with 3 charts
12s
02
Ask questions in plain language
Ask the way you'd ask an analyst, get an answer that's actually right in seconds.
Monday report delivered
06:02
Budget moves prepared
06:04
Meta DE
Awaiting approval
Sell-through planner
Publish03
Automate and build the rest
Agents run the recurring work on schedule; apps become tools the whole team shares.
Agents
The work that repeats, running before you wake up.
Profit Pilot
Runs Fridays 16:00
Prepare next week's budget moves where profit is incremental, and send them for approval.
Reasoning
“Reported ROAS says scale everything — but brand search is soaking up credit. Check incrementality before moving budget.”
Queried modeled data
Spend, contribution margin, and returns by channel and market · last 28 days
Asked the MMM
Diminishing-returns curves per channel · profit-optimal split for next week
Used skill: budget-move-guardrails
Max ±20% per move, never against low stock cover
Modeled next week's allocation
3 moves · +€19.5k projected weekly profit
3 budget moves prepared
Awaiting approvalMeta · Google Ads
Plan posted to #growth with the numbers behind each move
Apps
The tool your team needs, built by describing it.
Planners, review queues, dashboards that act — full applications on live commercial data, shared with the team and adjusted in chat. Three real builds below.
Apps /Weekly Trading Dashboard···
Ask appWeekly Trading Dashboard
Live modeled data · Refreshes every morning
Shared with 8Week 23 · Jun 1 – Jun 7
OverviewBy marketStock risk
Net revenue
€412K+4%
Contribution profit
€86.4K+6%
Ad spend
€118K−2%
Blended ROAS
3.4×+0.2
Contribution profit
ActualForecastW17W18W19W20W21W22W23W24W25W26
Stock risk by market
3 flaggedUSOuterwear11.4 wksHigh
SEKnitwear9.8 wksMedium
DEDenim6.1 wksLow
Why it works
One foundation under everything.
Answers, agents, and apps run on the same modeled commercial data — so the numbers agree, the workflows survive your organisation, and nothing waits on a rebuild.
Revenue
Contribution Margin 3
Return rate
CM3 = Revenue − COGS − fulfilment − marketing
Defined by your team — not inferred
The AI never has to guess what a metric means.
Your metrics, markets and relationships are modeled before AI touches them, so it reads the definition your team set instead of inferring one from raw tables.
A good prompt lives on one laptop. This runs for the team.
#tradingD
Dema APP
Weekly trading report — 3 movements explained.
YLEMT+5
Teach it a procedure once. It runs the same way every time.
budget-move-guardrails
Pull spend, margin & incrementality
Apply the team's guardrails
Draft moves for approval
Runs identically for all 8 people
Connected to all your data.
OrdersInventoryReturnsMarketingSite sessionsStore sales
One live model — always currentYour team gets good at this in weeks.
Monday trading reportSearch-term hygieneBudget review queueRefund monitorSell-through planner+15 more
Connected to all your data.
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 integrationsEnterprise-ready
Built for production.
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.
EU & US inference
Choose where your data is processed. Model inference and data pipelines run on European or US infrastructure with full residency control.
Never used to train models
Your data is never used to train models. Processing stays in your chosen region and runs in isolated, encrypted sessions.
Always the latest models
New reasoning models roll out as they ship, so your agents keep improving automatically without migration work.
Permissions and approvals
Each agent has its own scope and integrations. Actions can require a named approval before anything changes.
Ask, automate, build
Start from a real example.
Questions answered in chat, agents on a schedule, applications built by describing them — every card is a real prompt from the library, linked to the use case where it runs.
Measurement
Recommendations you can check, not take on faith.
Marketing mix modeling, geo incrementality tests, and causal attribution prove which spend causes profitable growth. When Dema recommends a budget move, the model behind it has been tested against reality — not the ad platform grading its own homework.
“Since we started using Dema, we can optimize our budgets for the most profitable growth in real time.”
Sebastian Öhrn
Founder, Myrqvist
Customer stories
Trusted by category leaders.
Real teams, real numbers — from cutting acquisition costs to doubling profit to automating whole planning cycles.
They tested scaling Google spend — and incremental profit went up with it.
Axel Arigato ran a scaling test on Google and proved every extra dollar returned more than a dollar in incremental profit — then validated Meta and branded search the same way, cutting where spend was saturated.
>100%
Incremental epROAS on Google scaling
9x
Branded search over-attribution uncovered
3
Channels validated with tests
3 channels, 3 scaling testsBring one commercial job