How can AI engineering solutions make construction, architecture and real estate projects faster, safer and compliant?

Data security starts with the architecture: in construction projects, plans, bills of quantities and contract data are often sensitive. We generally recommend not sending critical data to external APIs, but building a self‑hosted infrastructure. Technologies like Hetzner for hosting, MinIO for object‑storage‑like solutions and Traefik for secure ingress configurations make it possible to run AI models locally or within a controlled environment.

For data storage we use enterprise knowledge systems based on Postgres + pgvector so that vector embeddings and metadata can be managed together. Access control, audit logs and encryption at rest and in transit are part of the standard setup for such environments.

Compliance also means traceability: every AI decision should be documented — which model, which data basis and which prompting parameters were used. This traceability is essential for site inspections, regulatory audits and potential liability questions.

Finally, we recommend hybrid approaches: models can run locally for sensitive queries while non‑critical, scalable functions remain in trusted cloud environments. A clear data governance framework determines which data can be processed where.

BIM integration is not an add‑on but a core element of successful AI projects in architecture and construction. We start with an analysis of the existing BIM toolchain: which software versions, which IFC specifications and which exchange formats (IFC, BCF) are used. From this we derive a data model that links semantic information (room types, building elements, material specifications) with geometric data.

Technically, IFC models are parsed and converted into a semantic schema that AI models can read. This enables checks to be applied not only textually but also spatially: collision analyses, quantity takeoffs and plausibility checks then rely on actual geometries.

For end users we build UI components or integrations into existing BIM clients so that results are visible in familiar tools. For example, a copilot can automatically produce a compliance checklist upon plan approval or generate a list of RFI tickets that are directly fed into the project platform’s issue tracking.

Iterative validation is important: architects and structural engineers must be able to assess AI results so the system can learn to correctly interpret industry‑specific abbreviations, standards and local construction practices.

ROI strongly depends on the use case. For tender copilots we often see time savings of 30–60% in bill of quantities (LV) preparation and a reduction in formal errors, which leads to faster awarding. For documentation automation, administrative effort in handovers and acceptance is significantly reduced, producing direct savings on change orders and warranty cases.

A typical timeline: a proof‑of‑concept (PoC) takes us a few days to a few weeks and is standardised at €9,900. After a successful PoC follows an MVP phase of 6–12 weeks, followed by production rollout and scaling within 3–6 months, depending on data quality and integration scope.

The biggest levers for quick ROI are clear prioritisation (e.g. tenders for standard parts), clean data provisioning and early involvement of eventual users. Small, measurable releases increase adoption and demonstrate short‑term savings.

In the long run, investments in robust infrastructure (e.g. private models, vector DBs) pay off through lower operating costs, better data sovereignty and reusable components that benefit multiple projects and sites.

Switching to self‑hosted models makes particular sense when data protection, regulatory requirements or strategic control over AI outputs dominate. Construction projects often contain sensitive information — for example proprietary structural details, contract terms or personal data of workers — that should not be transmitted to third parties.

Self‑hosting allows full control over models, fine‑tuning data and auditing. Technologies like self‑hosted LLMs in combination with pgvector for knowledge bases enable the operation of highly specialised copilots without exposing data.

The downside is additional operational effort: infrastructure, security updates and scaling must be managed internally or by a managed service. We evaluate this in the PoC and recommend hybrid approaches where less sensitive functions initially run via trusted APIs while critical processes operate locally.

Crucial is a risk analysis: if compliance requirements or company policy forbid sending data to third parties, self‑hosting is often the only acceptable option.

AI systems provide suggestions, not legally binding decisions. To minimise misinterpretations, we employ multiple layers of protection: first, thorough data validation; second, explainable models or supplementary rules (“guardrails”) for critical areas; and third, human‑in‑the‑loop processes for any decision with legal relevance.

Technically, we combine LLM outputs with deterministic checks: if an AI system identifies a technical deviation, an automatic plausibility check against geometric or quantity data is triggered. In cases of ambiguity, the system generates a task for a specialist instead of issuing a direct approval.

Responsibility ultimately remains with the client and the named technical leads on site. Our role is to provide transparent processes and audit trails so it can be demonstrated how a recommendation was produced and who authorised it.

We also recommend legal reviews for critical use cases, especially when AI suggests contract provisions or safety‑relevant measures.

Scaling is less a question of raw compute and more about architecture: modular components, standardised data models and reusable ML pipelines are crucial. We build solutions so that core components (embeddings, vector DBs, copilot logic) are centralised but configurable, allowing local variants and rules to be accommodated.

For multiple sites, role‑based access controls, multi‑tenancy in the database and configurable governance policies ensure local legal requirements are met. Monitoring and observability provide insight into usage, quality and costs per site.

Operationalisation also requires organisational measures: a central AI enablement team, local champions and an operating model for updates and rollouts. This enables sharing of best practices and faster onboarding of new projects.

Finally, a platform strategy helps: once interfaces to CAFM, ERP and BIM are established, the effort for new projects is significantly reduced because reusable connectors and mappings are available.

For text‑ and rule‑based tasks like tenders, specialised LLMs combined with retrieval‑augmented generation (RAG) for document‑based responses are recommended — with one important caveat: when data sovereignty is the priority, RAG components should operate with a private vector database (e.g. pgvector) and a controlled context.

For multi‑step workflows where decisions build on one another, agents/workflows are useful. They coordinate backend calls, table queries and model inference in definable steps so that complex tasks are automated yet traceable.

Technology choice (OpenAI, Anthropic, Groq or self‑hosted models) depends on requirements for latency, cost and data protection. Hybrid architectures allow generative tasks to be offloaded to external APIs while sensitive queries remain local.

Ultimately, what matters is prompt engineering combined with structured training (e.g. retrieval sets, few‑shot examples) and a robust metric baseline that measures quality, cost per run and operational stability.