Actuent
dema.ai

Automate your commerce operations

Connect every commercial signal in real time, turn it into trustworthy intelligence, and build commerce agents, apps, and analysis that drive profitable growth.

55
Partly agent-ready
Agent-readiness score out of 100
  • ✓ Clear description of the site
  • ✓ Several pages described
  • ✓ Actions agents can take
  • ✓ 3 or more actions
  • ○ Publishes its own LAWP (/.well-known/lawp.json)
  • ○ Actions agents can execute
  • ○ Business details (address, hours, phone)

How to improve this score

Up to +45 points:
+15 · Publishes its own LAWP (/.well-known/lawp.json)
Publish your starter lawp.json at /.well-known/lawp.json. WordPress: install the Actuent plugin. Cloudflare: use the one-click Worker.
+15 · Actions agents can execute
Give an action an endpoint on your domain so agents can do it for the user, not just link to it. See LAWP Actions.
+15 · Business details (address, hours, phone)
Add schema.org business data to your homepage (the starter snippet). Google uses it too.
Starter lawp.json for dema.ai
Made from what Actuent already knows. Edit it, then publish it at https://dema.ai/.well-known/lawp.json. Check it with the LAWP Checker, or edit it in the LAWP Generator.
{
  "lawp_version": "0.3",
  "domain": "dema.ai",
  "name": "Automate your commerce operations",
  "language": "en",
  "pages": {
    "/": {
      "title": "Automate your commerce operations | Dema",
      "content": "Connect every commercial signal in real time, turn it into trustworthy intelligence, and build commerce agents, apps, and analysis that drive profitable growth."
    },
    "/apps": {
      "title": "Apps",
      "content": "Working applications built by describing the workflow: planners, review queues and operating tools on live modeled commercial data, shared with the team."
    },
    "/chat": {
      "title": "Chat",
      "content": "Ask commercial questions in plain language and get answers from live modeled data through one semantic layer with team-defined metrics."
    },
    "/agents": {
      "title": "Agents",
      "content": "Automated workflows described in plain language: scheduled reports, keyword exclusions, sheet syncs, feed segments. They run on live data and execute in connected systems with approval."
    },
    "/integrations": {
      "title": "Integrations",
      "content": "Ad platforms, e-commerce platforms, POS, ERP and warehouse connections."
    },
    "/causal-attribution": {
      "title": "Causal Attribution",
      "content": "Causal factor attribution: explicit weights applied to ad platform and MTA claims, derived from incrementality experiments, set per channel, funnel stage and market. Reported on contribution margin and new customers, not revenue."
    },
    "/self-serve-analytics": {
      "title": "Retail Analytics (self-serve)",
      "content": "Explore any metric, dimension or time frame with real-time profitability built in. No SQL."
    },
    "/agentic-data-platform": {
      "title": "Agentic Data Platform",
      "content": "The underlying platform: unified commercial data model, agents, and the governance layer they run inside."
    },
    "/incrementality-testing": {
      "title": "Incrementality Testing",
      "content": "Geo-based experiments that measure what spend actually caused. Results calibrate both the marketing mix model and the causal attribution weights."
    },
    "/marketing-mix-modeling": {
      "title": "Marketing Mix Modeling",
      "content": "Continuous cookieless measurement of the whole mix from aggregate data, with response curves and an optimiser that runs on contribution margin."
    },
    "/product-feed-management": {
      "title": "Product Feed Management",
      "content": "Segment and prioritise the product feed on profit, so higher-margin products receive more spend."
    }
  },
  "actions": [
    {
      "id": "book",
      "name": "Book",
      "input": {
        "type": "text",
        "required": false
      },
      "intent": [
        "book",
        "booking",
        "reserve",
        "appointment",
        "schedule"
      ],
      "description": "Book an appointment, table or reservation"
    },
    {
      "id": "view_pricing",
      "name": "View pricing",
      "input": {
        "type": "text",
        "required": false
      },
      "intent": [
        "pricing",
        "prices",
        "plans",
        "cost",
        "how much"
      ],
      "description": "See plans and prices"
    },
    {
      "id": "sign_in",
      "name": "Sign in",
      "input": {
        "type": "text",
        "required": false
      },
      "intent": [
        "sign in",
        "log in",
        "login",
        "account"
      ],
      "description": "Log in to an account"
    },
    {
      "id": "careers",
      "name": "View jobs",
      "input": {
        "type": "text",
        "required": false
      },
      "intent": [
        "careers",
        "jobs",
        "hiring",
        "work with us"
      ],
      "description": "See open positions"
    },
    {
      "id": "contact",
      "name": "Contact",
      "description": "Send a message to Automate your commerce operations",
      "intent": [
        "contact",
        "message",
        "email",
        "get in touch"
      ],
      "input": {
        "type": "object",
        "required": true,
        "fields": [
          {
            "name": "name",
            "type": "string",
            "required": true
          },
          {
            "name": "email",
            "type": "email",
            "required": true
          },
          {
            "name": "message",
            "type": "string",
            "required": true
          }
        ]
      }
    }
  ]
}
Starter schema.org snippet
Replace the example values, then paste into your homepage's <head>.
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "Automate your commerce operations",
  "url": "https://dema.ai",
  "telephone": "+00 0000 0000",
  "priceRange": "€€",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "Street 1",
    "postalCode": "0000",
    "addressLocality": "City",
    "addressCountry": "DK"
  },
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": [
        "Monday",
        "Tuesday",
        "Wednesday",
        "Thursday",
        "Friday"
      ],
      "opens": "09:00",
      "closes": "17:00"
    }
  ]
}
</script>

What AI agents can do here

Book
Book an appointment, table or reservation
bookbookingreserveappointmentschedule
View pricing
See plans and prices
pricingpricesplanscosthow much
Sign in
Log in to an account
sign inlog inloginaccount
View jobs
See open positions
careersjobshiringwork with us

Pages

Automate your commerce operations | Dema /
Connect every commercial signal in real time, turn it into trustworthy intelligence, and build commerce agents, apps, and analysis that drive profitable growth.
Apps /apps
Working applications built by describing the workflow: planners, review queues and operating tools on live modeled commercial data, shared with the team.
Chat /chat
Ask commercial questions in plain language and get answers from live modeled data through one semantic layer with team-defined metrics.
Agents /agents
Automated workflows described in plain language: scheduled reports, keyword exclusions, sheet syncs, feed segments. They run on live data and execute in connected systems with approval.
Integrations /integrations
Ad platforms, e-commerce platforms, POS, ERP and warehouse connections.
Causal Attribution /causal-attribution
Causal factor attribution: explicit weights applied to ad platform and MTA claims, derived from incrementality experiments, set per channel, funnel stage and market. Reported on contribution margin and new customers, not revenue.
Retail Analytics (self-serve) /self-serve-analytics
Explore any metric, dimension or time frame with real-time profitability built in. No SQL.
Agentic Data Platform /agentic-data-platform
The underlying platform: unified commercial data model, agents, and the governance layer they run inside.
Incrementality Testing /incrementality-testing
Geo-based experiments that measure what spend actually caused. Results calibrate both the marketing mix model and the causal attribution weights.
Marketing Mix Modeling /marketing-mix-modeling
Continuous cookieless measurement of the whole mix from aggregate data, with response curves and an optimiser that runs on contribution margin.
Product Feed Management /product-feed-management
Segment and prioritise the product feed on profit, so higher-margin products receive more spend.

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Last updated 26 Sep 2026

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