What challenges did levelbuild face before AI implementation?

Legacy on-premises infrastructure and previous cloud provider limited scalability, increased latency, and hampered platform performance for a growing customer base. The platform needed better integration of AI to add new features that enhance productivity and efficiency in the construction industry workflows.

Source

GCPEvidence: Medium50/100

levelbuild Migrates No-Code Construction Workflow Platform to Google Cloud with AI Enhancements

Use case typeDocument automationUpdated Jun 13, 2026

levelbuild, a Germany-based technology company, migrated its no-code workflow and application management platform for the construction industry to Google Cloud to improve scalability, reduce latency, and enable AI-driven features. The legacy infrastructure limited performance and scalability while the company needed to integrate AI to enhance construction workflow efficiency for over 20,000 users. The solution involved migrating the platform to Google Cloud using serverless infrastructure including Cloud Run, BigQuery, AlloyDB, and AI-driven microservices with Vertex AI and Gemini-based models. AI capabilities include converting scanned docs to PDFs, deduplicating images, translating tabular data, summarizing emails, extracting to-do lists, transcribing and summarizing Google Meet video conferences, and sentiment analysis. Migration improved per-click latency by 75%, cut onboarding time by 75%, halved database costs, enhanced uptime and scalability, and accelerated platform development. Partnership with Seibert Group guided the AI integration and cloud migration process, ensuring GDPR compliance and performance improvements.

Organization: levelbuild
Industry: Manufacturing
Location: Germany

Reported outcomes

Time: −75%

Time & speed

Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 75% decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Reduced per-click latency by 75% and onboarding times by 75%, significantly improving user experience and accelerating adoption.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: levelbuild
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1AI-Driven Workflow Automation
2Cloud Migration
3AI Document Processing

Migrated the entire platform to Google Cloud employing serverless infrastructure with Cloud Run, BigQuery and AlloyDB, enabling resource sharing for better cost efficiency.
Leveraged Vertex AI and Gemini-based models to create AI-driven microservices for tasks like email summarization, to-do extraction, video transcription, sentiment analysis, and document processing.
Collaborated with Google Cloud partner Seibert Group to ensure smooth migration, performance benchmarking, and effective AI integration following GDPR compliance.
Created plans for future AI agents to automate research, sentiment analytics, and RFP analysis to further optimize workflows.

Technologies

Google Cloud Cloud Run BigQuery AlloyDB Gemini Vertex AI Google Meet

AI features are enhancing productivity, enabling faster research and decision-making leading to stronger ROI for customers.

Sources & evidence1

Evidence: Medium50/100Evidence strength