What challenges did levelbuild face before AI implementation?

Inefficient management of disparate applications and data silos across construction workflows hindered operational efficiency. Significant latency in previous on-prem and cloud infrastructure affected system performance and user adoption. High onboarding times and expensive infrastructure limited scalability amid growing customer base.

What was the impact of the AI solution?

Reduced latency by 75%, enhancing user experience and accelerating product adoption. Cut onboarding times by 75%, from a month to a week, improving customer onboarding efficiency. Reduced database and infrastructure costs by 50% through efficient resource sharing and managed services. Enabled AI-driven search to reduce research time from weeks to minutes for large construction projects. Improved operational efficiency and business insights for over 20,000 users daily.

Source

GCP

levelbuild: AI-Driven No-Code Platform for Construction Industry on Google Cloud

Use case typeWorkflow automationUpdated Jun 13, 2026

levelbuild is a German technology company offering a no-code workflow and application management platform for the construction industry, centralizing disparate applications and data into a unified interface and enterprise data warehouse. In 2024, levelbuild migrated its platform infrastructure to Google Cloud for improved scalability, reduced latency, onboarding time, and costs, crucial for over 20,000 users generating 10 million transactions daily. They integrated Google Cloud's AI capabilities including Vertex AI, Gemini models, and Cloud Run to introduce AI-driven features such as predictive analytics, sentiment analysis, and document search, reducing document search time from weeks to minutes. Google Cloud's managed services allow optimization of compute resource consumption, cutting database costs by 50% and enabling resource sharing across customers, improving performance and uptime. A digital workforce of AI agents is planned to automate tasks like RFP analysis, sentiment detection, and schedule adjustments, enhancing operational efficiency in construction project management.

Organization: levelbuild
Industry: Tech & Comms
Location: Germany

Reported outcomes

−75%

quantified impactTime & speed

−75%time−50%cost

Strategic outcomes

New product / capabilityAdded AI-driven document processing and searchSpeed & agilityAccelerated customer onboardingCost efficiencyReduced infrastructure and database costsCustomer experience & trustImproved user experience and adoption

Primary read

Use case focus

Showing 3 of 4

1Workflow Automation
2AI Agents
3Document Intelligence

Inefficient management of disparate applications and data silos across construction workflows hindered operational efficiency.
Significant latency in previous on-prem and cloud infrastructure affected system performance and user adoption.
High onboarding times and expensive infrastructure limited scalability amid growing customer base.

Migrated the entire platform infrastructure to Google Cloud, choosing it over other providers for better performance and AI integration capabilities.
Implemented AI-driven microservices and used Vertex AI with Gemini-based models for document processing, email summarization, and video transcription.
Partnered with Seibert Group for migration guidance and AI technology enhancement.
Planned deployment of AI agents to perform predictive analytics, sentiment analysis, and automated schedule adjustments within the platform.

Technologies

Vertex AI Gemini BigQuery Google Cloud AlloyDB Cloud Run Cloud Storage Google Meet

Reduced latency by 75%, enhancing user experience and accelerating product adoption.
Cut onboarding times by 75%, from a month to a week, improving customer onboarding efficiency.
Reduced database and infrastructure costs by 50% through efficient resource sharing and managed services.
Enabled AI-driven search to reduce research time from weeks to minutes for large construction projects.
Improved operational efficiency and business insights for over 20,000 users daily.