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Architects, engineers, and subcontractors all model differently, using their own parameters, categories, and naming conventions.",[],"normal",{"_key":27,"_type":17,"children":28,"markDefs":33,"style":25},"ef79fcdb1e87",[29],{"_key":30,"_type":21,"marks":31,"text":32},"4e4c3358d256",[],"In theory, BIM models contain everything needed for estimation and planning. In reality, getting that data into a usable form takes a lot of manual effort.",[],{"_key":35,"_type":17,"children":36,"markDefs":41,"style":25},"3ea32c2ddf0d",[37],{"_key":38,"_type":21,"marks":39,"text":40},"279d1734cb88",[],"That gap has real consequences. Estimators spend time cleaning and reshaping data instead of analyzing it. Inconsistent inputs increase the risk of pricing errors and change orders. Decisions slow down because no one fully trusts that they’re looking at a complete, consistent picture. 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The data just isn’t set up to support that.",[],{"_key":51,"_type":17,"children":52,"markDefs":75,"style":25},"85d50dc2b458",[53,57,62,66,71],{"_key":54,"_type":21,"marks":55,"text":56},"730d610839a2",[],"In Suffolk’s case, their team wasn’t typically building the models, so they had limited access and context needed to understand how the models ",{"_key":58,"_type":21,"marks":59,"text":61},"90abc09ef4bc",[60],"em","worked",{"_key":63,"_type":21,"marks":64,"text":65},"5c58e2dc853e",[]," in ",{"_key":67,"_type":21,"marks":68,"text":70},"0565bf207679",[69],"78d8bd999b6b","Revit",{"_key":72,"_type":21,"marks":73,"text":74},"db4bc9e95003",[],". 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Instead of relying on manual handoffs or file exchanges, the data lake reflects the live state of the project. That removes a lot of friction and avoids the typical “walled garden” problem where data gets stuck in one system.",[178],{"_key":171,"_type":77,"href":151},{"_key":180,"_type":17,"children":181,"markDefs":186,"style":160},"80b2a55acc2e",[182],{"_key":183,"_type":21,"marks":184,"text":185},"36a6db4746d9",[],"The Building Data Lake",[],{"_key":188,"_type":17,"children":189,"markDefs":194,"style":25},"7193760a225d",[190],{"_key":191,"_type":21,"marks":192,"text":193},"23bb9b3cfd16",[],"Speckle converts complex BIM files into a queryable relational database accessible via standard APIs. In practice, this means data that used to live inside siloed tools becomes something teams can actually query, connect to, and use. It also opens things up significantly because the data is no longer tied to authoring tools; the data team can work with it without needing Revit licenses or specialized software.",[],{"_key":196,"_type":197,"enableLightbox":198,"images":199},"993f705324bf","gallery",true,[200],{"_key":201,"_type":108,"alt":202,"asset":203},"edab62d9e883","The Building Data Lake Suffolk Construction and Speckle",{"_ref":204,"_type":112},"image-eb8d774cfce60802d343dd8e615c4aed29ea9015-2800x1575-heif",{"_key":206,"_type":17,"children":207,"markDefs":212,"style":160},"e25b0e503c04",[208],{"_key":209,"_type":21,"marks":210,"text":211},"4fd891207353",[],"Event Driven Automations",[],{"_key":214,"_type":17,"children":215,"markDefs":220,"style":25},"b64bf89f1c24",[216],{"_key":217,"_type":21,"marks":218,"text":219},"9d4f7aed8128",[],"Every time a new model version is committed, automated functions run health checks and classify the data. This shifts quality control earlier in the process. Rather than catching issues downstream, the system ensures only clean, structured data moves forward, reducing rework later.",[],{"_key":222,"_type":17,"children":223,"markDefs":228,"style":160},"cb1fa9ed571c",[224],{"_key":225,"_type":21,"marks":226,"text":227},"15d887d944c8",[],"The Building Data Pipeline",[],{"_key":230,"_type":17,"children":231,"markDefs":236,"style":25},"73286e453eeb",[232],{"_key":233,"_type":21,"marks":234,"text":235},"1d0a4de713e6",[],"All of this feeds into the building data pipeline. Instead of building custom scripts or processes for each project, Suffolk has created a repeatable system that automatically handles tasks such as structural pattern matching and material quantity extraction. The pipeline maps design data directly to Suffolk’s internal schemas, so outputs remain consistent across projects and workflows as estimation scales.",[],{"_key":238,"_type":239,"companyLogo":240,"companyName":91,"jobTitle":242,"personImage":243,"personName":246,"text":247},"9e8ac6ee768e","quote",{"_type":108,"asset":241},{"_ref":111,"_type":112},"Senior Director, Design AI",{"_type":108,"asset":244},{"_ref":245,"_type":112},"image-47d83a37a5178222cae5b3f432d9fa51dc1ce4d3-1098x1098-jpg","Murat Melek","We use Speckle Automate, and the AI algorithm is pretty much deciding, ‘Hey, based on what I see here, the closest matching code is xyz,’ so that we can programmatically move the data to estimation.",{"_key":249,"_type":17,"children":250,"markDefs":264,"style":25},"7526316ae65d",[251,255,260],{"_key":252,"_type":21,"marks":253,"text":254},"52b9001f2c9c",[],"The day-to-day workflow looks very different as a result. Instead of exporting model data, cleaning it manually, and rebuilding it in spreadsheets, the data flows automatically from ",{"_key":256,"_type":21,"marks":257,"text":259},"8cba22a371c6",[258],"307bb79cd658","ACC into Speckle",{"_key":261,"_type":21,"marks":262,"text":263},"3233c1cdcea5",[],", gets structured in the data lake, is enriched through automation, and then moves directly into estimation systems with consistent cost coding.",[265],{"_key":258,"_type":77,"href":151},{"_type":108,"alt":267,"asset":268},"How Suffolk Construction operationalizes design data with Speckle",{"_ref":269,"_type":112},"image-36b6a7e0126a7a4d2300c7ad6ab69f6de9560dd6-2131x1177-png",{"_type":108,"asset":271},{"_ref":269,"_type":112},[273],{"_id":274,"slug":275,"title":278},"4c1923eb-b574-4249-a997-9e52c24c6658",{"_type":276,"current":277},"slug","commercial","Commercial","2026-05-12",[281,284,287],{"result":282,"title":283},"Since starting with clean, structured data","Estimators move faster",{"result":285,"title":286},"By standardized data feeding into estimation","Fewer downstream corrections",{"result":288,"title":289},"Pattern recognition across every project","Portfolio intelligence",[291,299,307,315,323,331,339,347],{"_key":292,"_type":17,"children":293,"markDefs":298,"style":25},"8cc4ef7b02b6",[294],{"_key":295,"_type":21,"marks":296,"text":297},"02561af8c107",[],"This shift from manual workflows to a structured data pipeline changes how projects are actually delivered.",[],{"_key":300,"_type":17,"children":301,"markDefs":306,"style":25},"8fc0fe85827c",[302],{"_key":303,"_type":21,"marks":304,"text":305},"11fd83212564",[],"Estimators can move faster because they’re starting with clean, structured data instead of raw inputs that need to be fixed. That means less time spent preparing data and more time focused on analysis and decision-making.",[],{"_key":308,"_type":17,"children":309,"markDefs":314,"style":25},"5f7d1081dcb1",[310],{"_key":311,"_type":21,"marks":312,"text":313},"3037b1dc1ed9",[],"There’s also a noticeable improvement in consistency. With standardized data feeding into estimation, the risk of errors caused by mismatched inputs goes down, which leads to more reliable pricing and fewer downstream corrections.",[],{"_key":316,"_type":17,"children":317,"markDefs":322,"style":25},"d1cb2349d30d",[318],{"_key":319,"_type":21,"marks":320,"text":321},"0ecb05dd01a1",[],"Manual handling is reduced across the board. Instead of repeated copy-paste workflows and one-off scripts, teams rely on a system that automatically prepares and routes data.",[],{"_key":324,"_type":17,"children":325,"markDefs":330,"style":25},"cc8566ecb9b9",[326],{"_key":327,"_type":21,"marks":328,"text":329},"147e86dc4e7e",[],"At the same time, access to BIM data broadens. Estimators, planners, and project managers no longer have to rely on static PDFs or indirect summaries; they can work directly with structured model data. That leads to faster decisions and better coordination between teams.",[],{"_key":332,"_type":17,"children":333,"markDefs":338,"style":25},"a527ab415b47",[334],{"_key":335,"_type":21,"marks":336,"text":337},"22c5b69b54bd",[],"Over time, the impact compounds. Because the data is consistently structured and stored, each project adds to a growing pool of usable information. Model elements are automatically classified and mapped to cost codes, which allows estimation workflows to run with minimal manual input.",[],{"_key":340,"_type":17,"children":341,"markDefs":346,"style":25},"a8b14b5c6e64",[342],{"_key":343,"_type":21,"marks":344,"text":345},"020dda2619ae",[],"This also opens the door to learning across projects. Patterns that were previously hard to see, like recurring design issues, can now be identified earlier. Instead of reacting to problems on site, teams can start catching them during design and planning.",[],{"_key":348,"_type":17,"children":349,"markDefs":354,"style":25},"71acb21975f3",[350],{"_key":351,"_type":21,"marks":352,"text":353},"74eeb3be9884",[],"What used to be an isolated project data becomes something much more valuable: a shared, evolving dataset that improves performance across the entire portfolio.",[],{"_type":276,"current":356},"how-suffolk-construction-operationalizes-design-data-with-speckle",[358,366,374,384,392],{"_key":359,"_type":17,"children":360,"markDefs":365,"style":25},"29f6fa1c325e",[361],{"_key":362,"_type":21,"marks":363,"text":364},"c8ef1802b597",[],"To solve this, Suffolk built a centralized data lake focused on construction data. Before Speckle, they lacked visibility into and access to this data, which made it difficult to integrate into their system. With Speckle, they were able to smoothly bring this data into their data lake. In effect, Speckle acts as a pre-construction data layer that connects to their Databricks lake.",[],{"_key":367,"_type":17,"children":368,"markDefs":373,"style":25},"ae016bba4edf",[369],{"_key":370,"_type":21,"marks":371,"text":372},"3cd93bf46b85",[],"Instead of working directly with fragmented BIM files, teams push data from their authoring tools and Autodesk Construction Cloud into Speckle. There, it’s normalized, structured, and prepared for downstream use.",[],{"_key":375,"_type":239,"companyLogo":376,"companyName":91,"jobTitle":378,"personImage":379,"personName":382,"text":383},"b7eea62a31ce",{"_type":108,"asset":377},{"_ref":111,"_type":112},"BIM Data Engineer",{"_type":108,"asset":380},{"_ref":381,"_type":112},"image-8ba4c92363c2cba608c8613a1ec5792bed4fa0d3-400x400-jpg","David Morgan","We have our modeling tools and ACC, and we're doing all this authoring, but how do we extract and clean the data? We push it into Speckle, making it accessible upstream for construction. That's our data lake system that helps process data and maintain clean business logic.",{"_key":385,"_type":17,"children":386,"markDefs":391,"style":25},"a52524a627e1",[387],{"_key":388,"_type":21,"marks":389,"text":390},"c58ecbb0bae9",[],"This changes the role of BIM data entirely. Instead of being something you extract from when needed, it becomes part of an ongoing system, something that can be accessed consistently, reused across workflows, and trusted when decisions need to be made.",[],{"_key":393,"_type":394,"aspectRatio":395,"platform":396,"videoId":397},"a614f72b37b7","videoEmbed","16/9","vimeo","1191444818",[399,404],{"_id":400,"slug":401,"title":403},"f113ce04-5c0e-4487-bfc8-15eaa511aedb",{"_type":276,"current":402},"build-custom-tool","Build with Speckle",{"_id":405,"slug":406,"title":408},"21843b35-f445-4f13-af48-68c3785c95b4",{"_type":276,"current":407},"dashboards-and-insights","Dashboards & insights",[410,415],{"_id":411,"slug":412,"title":414},"181b6a0b-a687-4945-83d1-97feaaa5832e",{"_type":276,"current":413},"innovation","Innovation",{"_id":416,"slug":417,"title":419},"8da27892-9818-4e5a-a1c2-19f8fc789318",{"_type":276,"current":418},"data-lake","Data Lake",[421,423],{"_id":422,"name":70},"7a5a5d48-3e74-43d5-8672-a376b9b15abb",{"_id":424,"name":425},"73281e88-8074-4fd8-92f9-8824c0caefee","ACC",{"_createdAt":427,"_id":428,"_rev":429,"_system":430,"_type":12,"_updatedAt":433,"challenge":434,"company":459,"companyLogo":475,"companyName":467,"draft":113,"excerpt":478,"future":479,"howTheyDidIt":529,"mainImage":855,"ogImage":859,"projects":861,"publishedAt":872,"resultMetrics":873,"results":883,"slug":1027,"solution":1029,"solutions":1084,"team":1087,"title":1098,"toolsConnected":1099},"2026-03-05T13:40:04Z","b8911ffb-0176-4177-a0eb-f4d4e19ad097","H6rFpJfiHOfEF1cTwrJAum",{"base":431},{"id":428,"rev":432},"6RK3jWpOBOdRzgd0gmAATL","2026-05-19T10:27:24Z",[435,443,451],{"_key":436,"_type":17,"children":437,"markDefs":442,"style":25},"a62b10afbd53",[438],{"_key":439,"_type":21,"marks":440,"text":441},"0a42dd7dcda7",[],"Major projects are increasingly confronted with structural challenges stemming from fragmented data ecosystems, strong reliance on proprietary platforms, and inefficient manual workflows. Project information is typically dispersed across multiple tools, environments, and license models, resulting in higher costs, reduced interoperability, and limited capacity to access, transform, and reuse data at scale.",[],{"_key":444,"_type":17,"children":445,"markDefs":450,"style":25},"dba2b4afe76d",[446],{"_key":447,"_type":21,"marks":448,"text":449},"c0da00bc99aa",[],"Beyond the technological fragmentation, teams dedicate a disproportionate amount of time to low-value, repetitive activities—such as data cleansing and structuring, georeferencing alignment, model federation, and version comparison. These tasks, while necessary, do not directly contribute to design or delivery quality, yet they significantly slow down production cycles and increase the risk of inconsistency.",[],{"_key":452,"_type":17,"children":453,"markDefs":458,"style":25},"68b263c580a5",[454],{"_key":455,"_type":21,"marks":456,"text":457},"d5aa20a3fc07",[],"At the same time, maintaining data integrity, traceability, and consistency across disciplines, stakeholders, and project phases remains a complex governance challenge. Without structured and reliable information flows, it becomes difficult to feed operational dashboards with trustworthy data, automate quality controls, or support decision-making with real-time insights. The result is a gap between the potential of digital tools and the actual value extracted from project information.",[],[460],{"_key":461,"_type":17,"children":462,"markDefs":472,"style":25},"ad1abe5bdfa2",[463,468],{"_key":464,"_type":21,"marks":465,"text":467},"84f6264bf519",[466],"c1334e8f43e0","Pomerleau",{"_key":469,"_type":21,"marks":470,"text":471},"33cb94cdbe46",[]," is a leading Canadian construction company recognized for delivering complex building and infrastructure projects through innovation and collaboration. Founded in 1966, the company integrates digital technologies, sustainability, and new ways of working to rethink how the built environment is designed and delivered. This innovation-driven approach positions Pomerleau as an active contributor to global conversations around open data and platforms such as Speckle.",[473],{"_key":466,"_type":77,"href":474},"https://pomerleau.ca/en",{"_type":108,"alt":109,"asset":476},{"_ref":477,"_type":112},"image-c84c7ded09fec30e4ae71c4f4d13954c5575ae30-3840x735-png","How Pomerleau used Speckle to automate BIM workflows, cutting equipment validation and QA/QC checks by 90% while accelerating data extraction and project insights.",[480,488,496,504,512,521],{"_key":481,"_type":17,"children":482,"markDefs":487,"style":25},"2f5ce5794480",[483],{"_key":484,"_type":21,"marks":485,"text":486},"1822b0ca8ead",[],"After successfully validating the value of a self-hosted deployment, Pomerleau is now scaling Speckle at the organizational level by transitioning to Speckle Cloud in the new Canada region. This strategic move reduces infrastructure and maintenance overhead while aligning the platform with enterprise IT standards. It also unlocks access to advanced capabilities such as Speckle Intelligence, Automate workflows, and tighter integration with Autodesk Construction Cloud—further strengthening interoperability across the digital ecosystem.",[],{"_key":489,"_type":17,"children":490,"markDefs":495,"style":25},"998b10a5c710",[491],{"_key":492,"_type":21,"marks":493,"text":494},"12ed1dd8b950",[],"In parallel, the team continues to expand its internal toolset. An AI-powered chatbot is being developed to allow field and project staff to query model data through natural language, reducing reliance on technical interfaces and accelerating access to actionable information.",[],{"_key":497,"_type":17,"children":498,"markDefs":503,"style":25},"a6598d5e2b9a",[499],{"_key":500,"_type":21,"marks":501,"text":502},"8500a9ef69b3",[],"The IFC exporter is also being enhanced to support a broader range of geometry representations and metadata structures, ensuring higher fidelity and improved data continuity across systems.",[],{"_key":505,"_type":17,"children":506,"markDefs":511,"style":25},"e943611f8032",[507],{"_key":508,"_type":21,"marks":509,"text":510},"739e4d7675b9",[],"Pomerleau positions Speckle not simply as a platform, but as an open and extensible collaboration framework—one that eliminates silos, reduces repetitive manual effort, and establishes a resilient data foundation capable of supporting automation, analytics, and AI-driven workflows in the long term.",[],{"_key":513,"_type":17,"children":514,"markDefs":519,"style":520},"dd430fd99fbd",[515],{"_key":516,"_type":21,"marks":517,"text":518},"66529240568c",[60],"Acknowledgment",[],"h4",{"_key":522,"_type":17,"children":523,"markDefs":528,"style":25},"566d820fedac",[524],{"_key":525,"_type":21,"marks":526,"text":527},"e09d50da28b5",[60],"This work was made possible through close collaboration between innovation, VDC, and operations teams. Oscar Mannarella, Araham Jesus Martinez Lagunas, and Yazid Ben Said played a key hands‑on role in developing, testing, and applying the Speckle‑based solutions on active projects. From an operations perspective, Cédric Simard was instrumental in identifying and clearly articulating on‑site challenges in model and information management, helping surface concrete problems that could be addressed through a data‑driven approach. At the time, Dragica Adamovic led the initiative as Innovation Manager, structuring discussions and guiding the effort from early ideas through implementation. Fernando Valdivieso supported the initiative at a strategic level, leading discussions with upper management to secure the necessary investment and server infrastructure. 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