🏆 PropTech Germany Award 2024
🎖️ PropTech 2026 Awards Nominee

Real Results, Fixed Cost

Every engagement below was delivered within the estimated budget and timeline under our fixed-cost model. No surprises — just outcomes.

VSBD Transparency Toolkit

Every case study below includes the full budget, timeline, team composition, and KPI breakdown from the actual engagement. This is our standard transparency framework — the same one we use internally across every client project.

💰
Fixed budget
Actual cost, not estimate
📅
Timeline
Delivery weeks, not ranges
👥
Team composition
Every role, every sprint
🎖️ PropTech 2026 Awards Nominee
Case Study 01

Agentic Orchestration Layer for PropTech

Agentic AI · LLM Orchestration

A governed control plane that coordinates specialized AI agents — document, valuation, tenant-communication, and maintenance — across a real estate platform. Deterministic orchestration, typed tool contracts, human-in-the-loop approvals, and full tracing turn isolated LLM features into a reliable, auditable automation layer.

BudgetUnder €250k
TimelineUnder 5 months
Tech Stack
LLM OrchestrationPythonAgentsGuardrailsObservabilityKubernetes
Team
Solution Architect2 AI/ML Engineers1 Backend Engineer1 MLOps Engineer1 Automation QA
70%
of routine asset-ops tasks handled end-to-end by agents
faster turnaround on document-heavy workflows
100%
of high-stakes actions logged with human-in-the-loop approval
40%
reduction in LLM token spend via routing & caching

What We Delivered

  • Orchestrator service routing tasks across specialized agents with typed tool contracts
  • Guardrail layer: input/output validation, policy checks, and grounded retrieval
  • Human-in-the-loop approval gates for irreversible or high-value actions
  • End-to-end tracing and evals — every agent step replayable and scored
  • Model routing and prompt caching for cost and latency control
  • Fallback and circuit-breaker logic so a single agent failure never cascades
Client Goal

Move from isolated LLM features to a coordinated agent layer that can plan and execute multi-step real estate workflows safely — with guardrails, human approval on high-stakes actions, and observability that satisfies audit and compliance.

🚑 Crisis Recovery & Turnaround
Case Study 02

Crisis Recovery for a German PropTech Platform

Turnaround · Engineering Governance · ML

A German B2B PropTech platform for property-price evaluation was on the brink of failure — delivery deadlines missed three times, architectural decay, and a proprietary ML model with poor accuracy. VSBD crisis-managed the SDLC, rebuilt the ML core, decomposed the monolith, and scaled the engineering org to 50+ engineers across six cross-functional teams — returning the product to seamless delivery.

BudgetOn request
TimelineOn request
Tech Stack
Microsoft AzureC#AngularMicroservicesRegression MLDevOps
Team
Delivery Manager3 Project Managers6 Team Leads6 Business Analysts6 Product OwnersSolution Architect~26 Backend Engineers (C#)~12 Frontend Engineers (Angular)Data ScientistsDevOps Engineers6 QA Engineers
90%
lower bug-fixing expenses — from €1.6M to €180K per year
99.9%
platform availability, up from 90%
1 mo
time to market, down from 3 months
99.2%
accuracy from the bespoke regression ML model
lower cost of ownership on the forecast component

What We Delivered

  • Replaced the proprietary, sparse-data ML model with a bespoke regression model and custom feature engineering — retrainable within hours
  • Decomposed a 500k+ line-of-code monolith into functional microservices
  • Established solution architecture, DevOps, and enterprise engineering practices from scratch
  • Structured the org into six cross-functional teams (~50 engineers) — each with a Business Analyst and Product Owner — with three Project Managers reporting to a Delivery Manager
  • Governance and monitoring to review progress, surface roadblocks early, and streamline product increments
  • Strategic roadmap completed across five major engineering areas
  • Graceful transition of the product into the support and maintenance phase
Client Goal

Recover the platform from a technical crisis, introduce meaningful change to stakeholders fast, put governance, engineering and monitoring processes in place, and release the platform to production after three missed deadlines.

🏆 PropTech Germany Award #1
Case Study 03

AI Real Estate Data Intelligence SaaS

SaaS Platform Development

High-capacity multi-tenant SaaS platform connecting multitude of client data sources with synergy of AI components and discrete business workflows. Deployed on k8s with Terraform IAC, built to scale seamlessly.

Budget€367k
Timeline9 months
Tech Stack
KubernetesTerraformPythonReactMLOpsDevOps
Team
Product OwnerSolution ArchitectMLOps / DevOps EngineerUI/UX Designer1 Lead Engineer2 Back-End Engineers1 Front-End EngineerManual QA
70%
improvement in model response time (MVP vs POC)
$50K+/mo
savings in cloud operations fees
1TB+
storage capabilities designed & implemented
#1
Real estate startup in Germany — PropTech Germany Award

What We Delivered

  • Multi-tenant SaaS platform MVP within budget and timeline
  • Highly scalable microservices architecture on Kubernetes
  • Terraform IAC with seamless deploy/migrate capabilities
  • Engineering excellence with data-driven decision making
  • Full-spectrum testing: unit, integration, functional, smoke, regression, usability
  • Continuous IT support and development for platform and services
  • Security by design with penetration testing
Client Goal

High-capacity SaaS platform able to seamlessly connect multitude of client data sources. Synergy of AI components with discrete business workflows logic implementation.

💳 High-Impact R&D AI
Case Study 04

ML Payment Gateway Cascade

Payment Optimization

Synergy of ML model and data pipelines to streamline payment processing and maximize profits across a diverse portfolio of payment gateway providers. Robust automated ML evaluation and CI/CD pipeline.

Budget€370k
Timeline6 months
Tech Stack
PythonML ModelsData PipelinesCI/CDAutomation QA
Team
Project Manager2 Data Engineers1 Data Scientist2 Data Analysts1 Manual QA1 Automation QA
40%
reduction in payment gateway fluctuations
95%
reduction in human error rate
50%
decrease in time-to-market
25%
reduction in support costs

What We Delivered

  • Robust ML models created and maintained
  • Supportive data stream pipelines for multiple integrations
  • Automated model evaluation process
  • Improved corporate transparency by eliminating manual financial data manipulation
  • Full QA suite: exploratory, compatibility, UI/UX, regression, E2E
  • CI/CD process for product increments
Client Goal

Synergy of ML model and data pipelines to streamline payment processing and maximize profits across diversity of payment gateway providers.

🔮 Predictive Maintenance AI
Case Study 05

Outage Prediction & Anomaly Detection

Performance & Risk Identification

AI model with supportive services for a natural resource extraction company to prognose and manage maintenance activities and MIH resolution. Deployed to production with full automated release candidate testing.

BudgetUnder €400k
TimelineUnder 6 months
Tech Stack
MLOpsPythonAzureMicroservicesDevOps
Team
Project ManagerDev Team LeadBackend DeveloperFrontend Developer2 Data Engineers1 Data Scientist1 MLOps Engineer1 Automation QA
70%
budget planning accuracy vs proprietary past period estimations
90%
reduction in time spent on testing while maintaining quality
30%
increase in overall software quality by multiple metrics
99%
success release ratio measuring prod P0 incidents
60%
more effective maintenance team dispatch planning

What We Delivered

  • Robust microservices architecture with automated backup/restore
  • Seamless scaling capability for rapid business growth
  • Configurable data pipelines for straightforward integrations
  • Detailed change log and discovered/rediscovered bug tracking
  • Handed over to support phase with minimal resources
  • Fully automated release candidate E2E testing
Client Goal

Natural resource extraction company looking for a way to prognose and manage maintenance activities and MIH resolution. AI model with supportive services was developed and released to production.

⚙️ AI Component Development
Case Study 06

AI Component for MS Dynamics 365

CRM Intelligence & Automation

Reduce human effort on routine tasks, automate data gathering and develop XML data schema extraction/upload workflows for a leading MS Dynamics 365 software vendor.

BudgetUnder €100k
TimelineUnder 3 months
Tech Stack
Azure CloudPythonReactReact NativeMS Dynamics 365
Team
Project ManagerBackend DeveloperFrontend DeveloperMobile DeveloperML EngineerManual QA
30%
increase in deal processing speed
20%
increase in deal closure rate
84%
employee satisfaction rating (post-demo feedback)
25%
decrease in contractor FTE expenses

What We Delivered

  • Platform component designed from POC through MVP to PROD
  • Wireframes and designs aligned with client vision and UI/UX principles
  • Solution architecture adopted to client environment and infrastructure
  • Passed company security audit and penetration testing
  • Monitoring, control metrics, and notifications for support
  • Significant decrease in human error across deal workflows
Client Goal

Reduce human effort on routine, automate data gathering and come up with XML data schema extraction/upload workflows based on requirements.

Ready for results like these?

Every engagement is fixed-cost. Tell us about your PropTech platform and we'll come back with a team composition and delivery estimate within 24 hours.