The construction industry has never lacked data; it lacks usable intelligence at the moments that matter most. In the high-stakes phases of tendering and pre-construction, the industry still relies on manual \u201cControl-F\u201d searches through thousands of pages of unstructured documents.<\/p>\n
I recently spoke with Herman Smith<\/a>, a civil engineer and former Chief Digital Officer at Multiconsult, who left the corporate world to solve this specific bottleneck. His startup, Volve<\/a>, isn\u2019t just another AI wrapper; it is a specialized \u201cdrill\u201d designed to penetrate the complexity of construction documentation.<\/p>\n For years, the AEC industry has faced a frustrating paradox: we have more digital tools than ever, yet productivity has not improved. Herman observed this from the inside, managing hundreds of unique software licenses while seeing companies struggle to adapt to new workflows.<\/p>\n \u201cI have seen from the inside how the industry is struggling to adopt new practices and technologies,\u201d Herman notes. \u201cWith AI emerging, the pace goes up. There\u2019s a need to create tailored solutions that can actually apply across the industry to change something, but the established players are not able to do it themselves.\u201d<\/p>\n This realization led to the founding of Volve, an intelligence platform for tendering and pre-construction.\u00a0<\/p>\n The problem isn\u2019t a lack of information; it\u2019s that the core of every project sits within a massive set of documents where responsibilities are defined and risk goes \u201csky high\u201d. If a team misses a single requirement in a 1,000-page Request for Proposal (RFP), the cost can be devastating for both the contractor and the client.<\/p>\n One of the most compelling insights from our discussion was Herman\u2019s perspective on the current AI landscape. As Large Language Models (LLMs) like ChatGPT become ubiquitous, many firms are tempted to build their own internal \u201cCustom GPTs\u201d. Herman warns that this approach has significant limitations in a professional engineering context.<\/p>\n \u201cI compare these general solutions to a Swiss Army knife,\u201d Herman explains. \u201cThey can do a little bit of many things, but if you push them too hard, they will break. In construction, you don\u2019t use a Swiss Army knife on-site; you use very specialized tools for each process. I see Volve as a drill\u201d.<\/p>\n Generic AI solutions often fail in AEC for two primary reasons:<\/p>\n \u201cGeneric AI can give you a flavor of really good outputs, but it will never give you the same flavor twice, and it can also give you false flavors,\u201d says Herman. To counter this, Volve builds advanced agentic pipelines and leverages Retrieval-Augmented Generation (RAG) to ensure that every answer is anchored in the project documents.<\/p>\n The core of Volve\u2019s value proposition is its ability to \u201cnormalize\u201d the chaos of construction documentation. Whether a client uses a Finnish standard, a FIDIC contract, or a bespoke template, the platform crunches the text and treats it as data rather than just prose.<\/p>\n Once a project team \u201cdumps\u201d their 40 or 1,000 documents into the platform, Volve takes about 20 minutes to crunch through and organize the information. From there, users can trigger specific \u201cfeatures\u201d or workflows:<\/p>\n \u201cWe don\u2019t have a better format than documents today to describe these projects,\u201d Herman admits. \u201cWe go on top of that and fill the gap between the documents and the intelligence needed for decision-making\u201d.<\/p>\n The industry\u2019s greatest weakness is arguably its inability to learn from past mistakes. Most knowledge resides in the heads of senior experts and leaves the building when they retire. Volve attempts to institutionalize this knowledge by allowing firms to upload historic tender feedback.<\/p>\n \u201cConstruction is not known to be learning more than you and me being experts and learning from ourselves,\u201d Herman observes. \u201cWe can apply learning so that when a client tells you that you lost a bid because of X, the system automatically gives you feedback next time you call it to enhance your next bid\u201d.<\/p>\n This \u201clearning loop\u201d extends into the execution phase through change assessment. By uploading meeting minutes, change requests, and claims, contractors can use the platform to build data-driven arguments for their positions during negotiations.<\/p>\n Eventually, the strategic value of AI emerges not by focusing on a single project at a time, but by considering the entire project portfolio, past and present, as a data asset.<\/p>\n As AI moves closer to the point of decision-making, the question of liability becomes paramount. Herman is clear that Volve is a \u201csuperpower support\u201d tool, not an automation of the human professional.<\/p>\n \u201cThe final responsibility is with the person making the decision,\u201d Herman insists. \u201cThat\u2019s why we always provide sources back to the original document. We don\u2019t know the risk appetite of our clients. We can provide a proposal, but we cannot replicate the Redline process without the user\u2019s input\u201d.<\/p>\n The emergence of tools like Volve suggests a shift in how AEC firms will compete in the near future. It is no longer enough to have the best engineers; firms must have the best \u201coperating layer\u201d for their project data.<\/p>\n When general contractors like Skanska or NCC use these platforms to enhance the quality of their bids, the \u201csubjective\u201d parts of a proposal, such as methodology and risk mitigation, become more robust and data-driven. As Herman notes, some clients are already reporting that they are winning more projects because they can demonstrate to public clients that they respond precisely to every requirement without error.<\/p>\n The goal is not just time efficiency; it is business impact. In an industry where margins are razor-thin, the ability to control risk and win the \u2018right\u2019 projects is the ultimate competitive advantage.<\/p>\n The question for AEC leaders is no longer whether to use AI, but how to use it. They must decide whether to rely on a \u201cSwiss Army knife\u201d or invest in the \u201cdrills\u201d built for the rigors of the industry.<\/p>\n The title image is courtesy of Volve<\/em>.<\/p>\n<\/div>\nThe paradox of digitalization without a productivity boost<\/strong><\/h2>\n
The \u201cSwiss army knife\u201d vs. the specialized drill<\/strong><\/h2>\n
![\"Construction](\"https:\/\/aec-business.com\/wp-content\/uploads\/2026\/03\/tools-in-hand-small.png\")
<\/figure>\n\n
Turning text into normalized data<\/strong><\/h2>\n
\n
From tendering to execution: The learning loop<\/strong><\/h2>\n
The Human in the Loop: Responsibility in the AI Age<\/strong><\/h2>\n
Implications for the industry<\/strong><\/h2>\n