Why do the construction, architecture and real estate industries need AI enablement now?
Innovators at these companies trust us
Complexity, liability, pace: the hard reality on construction sites and in FM
In construction, architecture and real estate, long planning cycles meet tight deadlines, strict compliance requirements and a flood of documents: bills of quantities (BoQs), fire protection regulations, handover protocols. These tensions lead to delays, change orders and higher costs — precisely where efficient processes and clean data make the difference.
At the same time, standardized skills for meaningful AI use are often missing: site managers, architects and facility managers need practical, domain-specific training that has direct impact on projects — not abstract theory. Without targeted enablement, AI potential remains stuck at the proof-of-concept stage.
Why we have the industry expertise
Our work combines technical depth with operational experience in product-near environments. We understand construction processes, BIM workflows, tender logic and the interaction between trades – and we translate this technical language into concrete learning paths and tools. Our goal is to train teams so they not only operate AI, but can independently develop and use it responsibly.
The team at Reruption brings experience from venture building, product development and training: we run executive workshops, department bootcamps and bespoke on-the-job coachings that are directly integrated into ongoing projects. We work with real datasets, bills of quantities and BIM models to build sustainable competencies.
Our trainings are practice-oriented and roll out beyond PowerPoint: we accompany the first iterations of an AI Copilot, develop prompting frameworks for tenders and establish internal communities of practice so knowledge remains anchored within the company.
Our references in this industry
For construction and infrastructure-related applications we draw on transferable project experience: with STIHL, for example, we developed product and training solutions for landscaping (GaLaBau) and implemented digital saw trainings — projects that gave us deep understanding of manual processes, safety trainings and workplace learning.
In the area of document analysis and research, we realized a project with FMG that enables AI-supported document search and analysis — experiences that are directly transferable to bills of quantities, contracts and compliance checks in construction projects.
For the design of practical learning platforms and digital training content, we worked with Festo Didactic to support a digital learning platform for industrial training. We use this expertise to set up modular bootcamps and on-the-job formats for construction and FM teams.
About Reruption
Reruption was founded with the idea of not just advising companies, but acting as a co-preneur: we work embedded in the organization, take responsibility for outcomes and deliver functioning solutions, not just concepts. Our focus is on AI Strategy, AI Engineering, Security & Compliance and Enablement — the four pillars that make real AI readiness possible.
For construction, architecture and real estate we combine these pillars with rapid prototypes, clear roadmaps and sustainable training paths. Regional competence centers, for example in Stuttgart with its strong engineering and construction economy, allow us to work closely with local engineering firms, major contractors and facility managers.
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What our Clients say
Hans Dohrmann
CEO at internetstores GmbH 2018-2021
This is the most systematic and transparent go-to-market strategy I have ever seen regarding corporate startups.
Kai Blisch
Director Venture Development at STIHL, 2018-2022
Extremely valuable is Reruption's strong focus on users, their needs, and the critical questioning of requirements. ... and last but not least, the collaboration is a great pleasure.
Marco Pfeiffer
Head of Business Center Digital & Smart Products at Festool, 2022-
Reruption systematically evaluated a new business model with us: we were particularly impressed by the ability to present even complex issues in a comprehensible way.
AI transformation in construction, architecture & real estate
The construction and real estate industry stands at an intersection: digitalized plans, BIM models and networked building technology meet traditional processes, fragmented data and high legal requirements. AI can be the link that speeds up processes, reduces risks and lowers operating costs. To succeed, however, you need not only technology but targeted enablement — teams must understand, apply and critically evaluate the tools.
Industry Context
Construction projects generate huge amounts of structured and unstructured data: plans, site photos, defect reports, bills of quantities (BoQs), contracts and permits. At the same time, requirements for documentation, occupational safety and compliance are increasing. Facility management extends the lifecycle of assets and needs reliable handover and maintenance data.
In regions like Stuttgart, with strong engineering and production density, we see particularly high demand for interfaces between planning and execution: BIM models must be linked with tenders, schedules and quality controls. Here AI creates the opportunity to automate data flows and accelerate decision-making processes.
At the same time, the industry is heavily regulated. Every AI solution must therefore consider data protection requirements, proof obligations and liability-relevant aspects. Without governance and training, companies can easily end up using tools efficiently but in ways that are legally or organizationally risky.
Key Use Cases
Tendering Copilots: AI-assisted assistants can automatically derive bills of quantities from plans and specifications, suggest standard texts and calculate variants. In workshops and bootcamps we teach how to prompt such Copilots, which quality checks are necessary and how to validate cost assumptions.
Project documentation & defect management: With NLP and image analysis, site photos, protocols and defect reports can be automatically classified, prioritized and assigned to a responsible person. Our trainings show how teams control such models, which thresholds make sense and how to reduce false positives.
Compliance checks & safety protocols: AI can automatically check contract clauses and regulations for deviations, detect fire protection or occupational safety requirements and recommend remedial measures. We teach how to integrate these checks into daily review processes without delegating legal responsibility.
BIM integration: AI can identify information gaps in BIM models, prioritize clash reports and semantically evaluate change requests. In our BIM integration trainings we connect prompting principles with technical pipelines so architects, planners and site managers share the same expectations.
Facility management & predictive maintenance: Based on sensor data and logbooks, AI models enable maintenance predictions and optimize service windows. We train facility managers in interpreting predictions, defining KPIs and designing escalation mechanisms.
Implementation Approach
Our enablement offerings start with executive workshops in which we clarify strategic objectives: which processes should be accelerated? Which KPIs matter? This is followed by department bootcamps for HR, Finance, Ops and FM, where we provide concrete templates, playbooks and prompting frameworks.
The AI Builder Track turns non-technical domain experts into productive creators: they learn model selection, quality metrics and simple data pipelines. In parallel we build enterprise prompting frameworks that include standard prompts, role descriptions and evaluation criteria — keeping usage reproducible and auditable.
On-the-job coaching is the core of our approach: we accompany the first real applications live on projects, integrate AI tools into existing workflows and hold retrospective sessions to continuously improve. This creates organizational learning instead of isolated prototypes.
Technically, we consider integration points to common BIM tools, DMS systems and FM platforms. Security & Compliance are built in from the start: access concepts, role management and audit logs are part of every training and implementation roadmap.
Success Factors
Successful enablement is not measured by the number of workshops, but by sustainable usage: how often is the tendering Copilot applied, how many defects are pre-filtered automatically and how much time do site managers save per week? We establish metrics and reporting so progress becomes visible.
Change management is central: we support leaders in clarifying responsibilities and build communities of practice that ensure knowledge exchange between project teams, planners and FM. Only then do individual skills become an organizational capability.
ROI arises from reduced throughput times, fewer change orders, lower error rates and optimized maintenance costs. Typical timelines: first noticeable effects after 6–12 weeks with a PoC and bootcamp; scaled rollout and measurability after 3–9 months, depending on the size of the organization.
In the end there is an internal capability set: prompt-capable domain experts, standardized playbooks for each department and a support backlog that internal AI communities continue to develop. This is sustainable and reduces dependency on external consultants.
Ready to make your teams fit for AI?
Start with an executive workshop or a department bootcamp and establish sustainable capabilities within your company.
Frequently Asked Questions
Tendering Copilots automate recurring, manual tasks like extracting bill of quantities items from plans, generating standard line items and comparing offers. This significantly reduces the time required to prepare bills of quantities and lowers the error potential that often leads to change orders. In practice, we see that a well-tuned Copilot can produce a first draft of a BoQ in a fraction of the previous time and returns the site manager to the role of reviewer.
Crucial is the combination of model output and human validation. Our trainings show how teams structure checks and review processes, which prompts deliver the best results and how to safely incorporate standard texts and contract clauses. This yields reliable outputs without relinquishing legal responsibility.
Technically, you must ensure clean input data and clear interfaces to DMS and BIM systems. In our bootcamps we cover data preparation, quality metrics and how to iteratively improve a Copilot system: feedback loops from real projects are the key to higher precision.
In the long run this leads not only to time savings but also to stronger negotiation positions: those who deliver validated BoQ versions faster can review offers sooner and make decisions earlier. This reduces the risk of plan changes and change orders, which brings direct cost benefits.
The most important lever is learning by doing — trainings run parallel to real sites but focus on clearly defined use cases. We start with small, well-defined pilots: for example automatic import of daily reports or AI-assisted prioritization of defect notifications. This keeps normal operations intact while teams immediately see benefits.
Our bootcamps and on-the-job coachings are designed to work on real tasks right away. That means participants bring their own documents and cases, which we adapt together into the workflows. This produces immediately applicable playbooks and ensures the learning sticks.
For leaders, our executive workshop provides a governance layer where interfaces, responsibilities and escalation rules are defined. This governance ensures training results can be adopted into projects safely and responsibly without overburdening operational decision-making.
Another point is tool integration: we build small, secure integrations to existing systems (BIM, DMS, FM software) so users do not have to switch between many new interfaces. This reduces friction and increases acceptance of the training measures.
Risks in construction projects are usually legal, safety-related and data-protection related. AI models can make incorrect assessments, overlook relevant contract clauses or insufficiently protect personal data. Companies must therefore define governance structures, audit logs and responsibilities clearly before using AI in regular operation.
Our AI governance trainings address exactly these points: we establish roles for data stewards, define review processes for model outputs and specify which decisions should never be fully automated (e.g., final contract acceptance or safety-critical approvals).
Regarding data protection: especially for site photos or personal working-time data, GDPR-relevant aspects must be considered. We advise on data minimization, pseudonymization and secure storage as well as on data deletion strategies for training data.
Finally, traceability is important: in legal disputes it must be documented how a model arrived at a recommendation. Our trainings and playbooks therefore also include documentation templates and reporting standards that can serve as evidence in case of dispute.
The time to noticeable ROI varies; typically first effects are visible after 6–12 weeks when a pilot Copilot or a document classifier goes live. In this phase manual cycles decrease and teams gain time for quality control instead of pure data collection.
For scalable effects, such as standardized tendering processes or organization-wide BIM integrations, we expect 3–9 months until full measurability. This includes rollout, iterative model improvements and the establishment of communities of practice that spread know-how internally.
It is important to define clear KPIs from the start: time saved per BoQ, reduction in change orders, speed of defect resolution or savings in maintenance costs. We support KPI definition and the setup of reporting so ROI becomes transparent.
Long-term ROI arises not only from automation but from changed ways of working: faster decisions, fewer friction points between planning and execution and better documentation quality that reduces liability risks.
The BIM integration training combines technical understanding of IFC and Revit data with pragmatic prompting and validation rules. Participants learn how to identify information gaps, automate prioritization of clash reports and semantically evaluate change requests using NLP. Practical exercises work with anonymized BIM excerpts from real projects.
In the Safety AI Basics module we teach how AI can monitor safety protocols, support risk assessments and classify incidents. We emphasize that these systems are assistants, not replacements for safety-responsible personnel — including escalation rules and audit logs.
The documentation training focuses on structured handover protocols, automated photo analysis, defect detection and generating consistent acceptance reports. Practical sessions show how to calibrate models, which vocabulary to prefer in protocols and how to design integration points with DMS systems.
All modules conclude with playbooks, prompts and on-the-job templates that can be used immediately in projects. We also offer follow-up sessions to incorporate lessons from live projects and continuously adapt the content.
Communities of practice connect practitioners from different projects, trades and locations. They are the engine for continuous learning: experiences, prompts, data gotchas and model tuning are shared, standard solutions spread and new use cases are identified. Without such communities, insights often remain with individuals or project islands.
We support the build-up of these communities with a starter kit: moderation guides, meeting templates, knowledge repositories and a roadmap for skill paths. Central is the combination of regular show-and-tell sessions and thematic working groups (e.g., tenders, BIM, FM) so knowledge grows in a structured way.
Operationalizing also means distributing responsibilities: who is the prompt owner, who is responsible for model performance, who takes care of data quality? Our trainings help establish these roles and anchor them in existing organizational structures.
In the long run, communities increase scalability: organizations become less dependent on external service providers, keep IP internally and can react faster to market changes. This makes them more resilient to disruption.
Technically, a company does not need an immediate complete modernization, but certain basics help enormously: a central DMS, structured BIM models with clear metadata, and defined data access rules. These systems make it easier to train, test and roll out models.
Clear interfaces (APIs) to BIM and FM tools are also important so AI tools do not operate in isolation but feed data into existing workflows. Our bootcamps cover how to design such integrations in a minimally invasive and secure way.
On the infrastructure side, a cloud-based, securely configured environment is often sufficient for prototyping; for production models we jointly assess requirements for latency, cost per run and data sovereignty. Security and compliance are part of the design process throughout.
Finally, the most important resource is the time of domain experts: they must invest in workshops and on-the-job coachings. Our approach is designed to use this time efficiently and deliver immediate value on concrete problems.
Contact Us!
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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