request a virtual engagement<\/a> on this topic with experts from our Microsoft Digital team.<\/p>\n<\/aside>\n\n\n\nNot anymore.<\/p>\n\n\n\n
Thanks to a solution we created that is internal to Microsoft, our indoor maps are now always current. (And while this solution isn’t presently available to customers, we’re sharing our story around it in hopes that you can learn from our approach.)<\/p>\n\n\n\n
With these maps available, the service ticket mentioned above now includes a visualization of the floor plan, which immediately indicates the correct room and highlights the faulty equipment\u2019s exact location (if already on the floor plan).<\/p>\n\n\n\n
The technician can now diagnose the problem in minutes. This can be done on their equipment, without the need to understand how to use specialized software or having knowledge of the building\u2019s layout.<\/p>\n\n\n\n
This shows the value of indoor maps when they work correctly. But our maps here at Microsoft didn\u2019t always work this way.<\/p>\n\n\n\n
A long-standing map gap<\/h2>\n\n\n\n For years, facility service technicians at Microsoft could access floor plans that were stored in a central repository but needed specialized software to view them. Our floor plan files came from a wide variety of vendors, with different naming conventions and drawing standards. <\/p>\n\n\n\n
Initially, we created indoor maps using this data for some buildings requiring a lot of manual work. As a result, updates to the maps were slow and expensive. As soon as a map slipped out of sync with reality, teams stopped relying on it.<\/p>\n\n\n\n <\/figure>\n\n\n\n\n\u201cEnterprises have struggled for years to maintain accurate indoor maps. The heart of this struggle is ultimately standards that are applied inconsistently to the source material.\u201d<\/p>\nVishu Admal, program and product lead, AI indoor maps initiative, Microsoft Digital<\/cite><\/blockquote>\n\n\n\nThese issues had a real impact on our day\u2011to\u2011day operations:<\/p>\n\n\n\n
\nOur facilities teams didn\u2019t have the spatial context in their work order to understand exactly where problems were happening, because they didn\u2019t have convenient access to detailed map layers that indicated the precise location of building elements (such as plumbing, or heating and cooling systems).<\/li>\n\n\n\n Our security teams couldn\u2019t easily overlay incident data on floor plans.<\/li>\n\n\n\n Our IT teams couldn\u2019t map device locations to the real world with confidence and relied on PDF version of maps.<\/li>\n<\/ul>\n\n\n\n\u201cEnterprises have struggled for years to maintain accurate indoor maps,\u201d says Vishu Admal, our program and product lead for our AI indoor maps initiative here in Microsoft Digital, the company\u2019s IT organization. \u201cThe heart of the struggle is ultimately standards that are applied inconsistently to the source material.\u201d<\/p>\n\n\n\n
Recently, we’ve developed an intriguing solution: An AI\u2011driven mapping data pipeline\u2014with out-of-the-box large language models (LLMs)\u2014that recognizes patterns, identifies inconsistencies, and produces updated indoor maps every day, as the floor plans evolve and change.<\/p>\n\n\n\n
Today, that data pipeline is keeping our indoor maps up to date for more than 500 buildings around the world. It supports the systems and teams that keep our campuses operating every day\u2014facilities, space management, security, and IT.<\/p>\n\n\n\n
And more importantly, this solution makes sure that when someone has a high-priority need for an indoor map, it\u2019s completely accurate and current.<\/p>\n\n\n\n <\/figure>\n\n\n\n\n\u201cThe problem has always been tripping up on the varying quality and consistency of the AutoCAD files. This is especially true at enterprise scale, where drawings come from different firms and have different standards.\u201d<\/p>\nI.M. Ndimubanzi, engineering manager, Microsoft Digital<\/strong><\/cite><\/blockquote>\n\n\n\nFrom CAD to operational maps: Finding a solution<\/h2>\n\n\n\n Some indoor mapping projects start with a simple assumption: The floor plan is already structured data.<\/p>\n\n\n\n
Our project didn\u2019t have that. What we had was computer-aided design (CAD) geometry, plus text, plus years of vendor variation.<\/p>\n\n\n\n
Different architecture and construction partners drew buildings in different ways. Labels, layers, symbols, and even basic conventions (like how rooms were \u201cclosed\u201d in a drawing) weren\u2019t consistently followed. That inconsistency is what broke any attempts at automation.<\/p>\n\n\n\n
\u201cThe problem has always been tripping up on the varying quality and consistency in the AutoCAD files,\u201d says I.M. Ndimubanzi, an engineering manager in Microsoft Digital who is the technical lead for our indoor maps initiative. \u201cThis is especially true at enterprise scale, where drawings come from different firms and have different standards.\u201d<\/p>\n\n\n\n
So, we built a data pipeline that assumes the input will be messy, but it can still produce a reliable output, time and again.<\/p>\n\n\n\n
Converting CAD geometry into render-ready maps<\/h3>\n\n\n\n We split this work into three stages. In brief, these can be labeled as: parse, interpret, and serialize.<\/p>\n\n\n\n
1. Parse CAD input into machine-usable signals<\/strong><\/p>\n\n\n\nWe start by extracting raw geometry and text from CAD using open-source parsing libraries. That gives us the basic data we can feed into downstream steps without forcing every file to look identical first.<\/p>\n\n\n\n
2. Use AI for interpretation and hygiene<\/strong><\/p>\n\n\n\nThe hardest part of the work isn\u2019t reading the CAD file. It\u2019s interpreting what the drawing means when dealing with variations in room names, abbreviations, and other conventions (which may differ by vendor, region, or even building).<\/p>\n\n\n\n
This is where we use AI-driven large language models to transform the extracted CAD signals into structured data.<\/p>\n\n\n\n
Instead of manually cleaning and translating each file, we use AI models to ingest CAD drawings directly and interpret what the data represents. Walls become walls, rooms become rooms. Doors, elevators, and fixtures are identified as distinct, usable elements rather than raw line work.<\/p>\n\n\n\n
That same approach helps solve a long\u2011standing data hygiene issue: inconsistent naming. For example: across the portfolio, the same type of space can appear as \u201cConference Room,\u201d \u201cConf. Rm.,\u201d \u201cMPR,\u201d or \u201cMulti\u2011Purpose Room.\u201d<\/p>\n\n\n\n
The AI helps normalize those variations into standardized space categories, turning messy labels into consistent, structured data that can be reused across systems.<\/p>\n\n\n\n
3. Serialize to GeoJSON with proven tooling<\/strong><\/p>\n\n\n\nOnce AI produces a structured representation, we convert the data into GeoJSON\u2014a popular spatial data exchange and rendering format\u2014using open-source tooling.<\/p>\n\n\n\n
GeoJSON gives us a clean, reliable data source for our mapping tools. This keeps the final output consistent and predictable, which is critical for rendering at scale and integrating into other applications.<\/p>\n\n\n\n
Note that this design is intentional: AI does the interpretation, while deterministic tooling does the formatting. This separation is what makes the pipeline stable.<\/p>\n\n\n\n <\/figure>\n\n\n\n\n\u201cAs long as they can add this SDK in their application or interface, they can connect to our databases. It gives them access to our map library.\u201d<\/p>\nAmr Dawood, senior software engineer, Microsoft Digital<\/cite><\/blockquote>\n\n\n\nCreating an SDK that makes maps usable everywhere<\/h3>\n\n\n\n A mapping pipeline is only valuable if other teams can use the results without becoming mapping experts. That\u2019s why we paired the mapping pipeline with a software development kit (SDK) that makes indoor maps embeddable inside operational tools.<\/p>\n\n\n\n
\u201cAs long as they can add this SDK in their application or interface, they can connect to our databases,\u201d says Amr Dawood, a senior software engineer in Microsoft Digital. \u201cIt gives them access to our map library. They can use predefined functions to choose the buildings, the layers they want to render, and how they want to display and order those layers.\u201d<\/p>\n\n\n\n
We built this SDK so product teams can treat the new indoor maps like any other UI component:<\/p>\n\n\n\n