# EMPLA product notes We're building Empla to help expansion teams and AI agents decide where to open next. The platform will use demographic, mobility, competitor and catchment-area data to rank locations and explain why they fit. It will also help teams find relevant properties. Expansion teams deciding where to open their next storefront, and the AI agents they work with. In development. The product preview is scripted; the functional MVP and MCP interface are not publicly available yet. Website copy reviewed: 2026-10-27. This is not a dataset refresh date. ## Two ways to work ### The Empla workspace In development. A map-based workspace where expansion teams can ask questions about locations, compare their fit and investigate properties. ### Your own AI agent MCP interface in development. The planned MCP interface will let your chosen agent use Empla's spatial tools and location evidence within your existing workflow. ## Planned location signals ### Demographics Learn about the people near a potential location and how they compare with your target customers. ### Mobility Look at how people move through an area and what that could mean for your business format. ### Competitors See which competitors operate nearby and consider their presence when comparing sites. ### Catchment areas Estimate the area a storefront could serve and compare it with the reach of your existing stores. ## Questions you can ask ### Choosing an area Which areas in Berlin fit our next coffee shop? Start with your business format, target customers and market. We're building Empla to use local data to rank areas that could fit. ### Changing your requirements What changes if I focus on takeaway rather than sit-down? Update your brief as you learn. You can bring in your format, customer criteria and property requirements whenever they matter, without following a fixed order. ### Your existing stores How should we think about the stores we already operate? Include your existing locations when comparing areas. A map can show overlap, but it cannot prove that a new store won't take sales from another. ### Finding a property Why does this area fit, and which properties should we investigate? The planned workflow will suggest relevant properties alongside explained location rankings. Check the evidence, source coverage and the site's practical suitability before committing. ## Product ideas ### Find promising areas. Empla will combine demographic, mobility, competitor and catchment-area data to rank areas against what your business needs. ### See why they fit. The planned rankings will explain why a location fits your business. You'll be able to compare alternatives and adjust the ranking when your format or requirements change. ### Find properties to investigate. We're building Empla to help you find relevant properties in those areas, with the evidence behind each suggestion available as you decide what to investigate. ## Interpretation and limits - The preview follows a script. It does not use live AI, connect to an MCP service or demonstrate a working end-to-end MVP. - Mapped OpenStreetMap object counts are not footfall, customer demand, vacancy, a commercial-competition measure or a site-suitability score. - You need to verify property availability, commercial terms and practical suitability separately. The preview has no live property inventory or purchasing service. - Check each source's coverage and freshness for the decision you're making. Any gaps in the evidence need to be visible. ## How to investigate a location - Start with your question, the area you're considering and what your team already knows. Refine the brief as you learn. - Keep each observation's source, geographic scope and limits visible. An illustration alone is not enough to recommend a location. - Return to comparisons or change your requirements when needed. You choose the next question; there is no fixed sequence. - Check important assumptions with the right data, people and on-site visits. People remain responsible for consequential actions. ## Where the example data comes from - The preview adapts the question, context layers and workspace-to-agent story from the owner-supplied Lovable YC prototype. - The map uses the supplied OpenStreetMap/Nominatim prototype data, credited to OpenStreetMap contributors under ODbL. It does not run a live map query. - The layers export records retrieval on 2026-10-25. The cells file has no snapshot date. Its latest tagged-node timestamp does not establish how fresh the whole dataset is. - The website review date records when we reviewed the copy. It does not record a data refresh or establish market coverage. ## Preview and access - You can watch the scripted preview on the homepage. It runs in your browser without calling a live model, map provider or MCP service. - We're still building the functional MVP. Contact Empla to discuss your use case. This website does not offer immediate application access or a free trial. - The MCP interface is in development. The Markdown notes and documentation index describe our plans; they are not working endpoints and do not guarantee agent discovery. ## FAQ ### What is Empla? We're building a location intelligence platform for expansion teams and AI agents deciding where to open their next storefront. It will rank promising locations and explain why they fit, with relevant properties to investigate. ### What data will Empla use? We plan to combine demographic, mobility, competitor and catchment-area data with your expansion requirements. The scripted preview uses a limited OpenStreetMap example. It doesn't show all of those sources or run a live ranking. ### Why would my AI agent use Empla? An AI model doesn't automatically have the spatial tools or current geospatial data a location decision needs. We're building Empla to provide that data and the reasoning behind location suggestions. A planned MCP interface will make those tools available to agents. ### Do I have to follow a fixed process? No. You can start with your current question and return to a comparison or change your requirements later. Bring in existing stores, customer criteria or property requirements when you need them. ### Can I use my own AI agent? That's the plan. We're developing an MCP interface so your chosen agent can use Empla's tools and evidence. The preview shows how this could work, but the connection isn't live. ### Is the product preview a live demo? No. It follows a script adapted from our prototype. The functional MVP is still in development. The page doesn't run a fresh location analysis or send messages. ### What does the map example tell me? It shows OpenStreetMap geometry and mapped objects from the prototype's local example. The counts don't estimate demand or recommend opening a site. ## Related pages [Home](/) · [Product](/product/) · [About](/about/) · [Contact](/contact/) · [Product notes](/product/facts/)