The Challenge
Hemubo, a major player in renovation and maintenance, faced a classic data problem: critical knowledge was locked in unstructured documents (SharePoint, file shares) and disconnected software systems.
- Vendor Selection: Comparing complex multi-page vendor proposals against RFPs was a manual, weeks-long process.
- Internal Support: HR, Finance, and ICT teams were overwhelmed with repetitive questions buried in policy documents.
- Long-term Planning: Assessing building states for 30-year maintenance plans required analyzing massive volumes of scattered inspection reports.
Our Approach
We didn't solve these as isolated problems. Instead, we architected a single, AI-ready data platform that serves the entire organization securely:
- Unified Data Backbone: We developed a pipeline to ingest and normalize data from both structured systems and unstructured office documents. This created a single "source of truth" accessible via a central AI interface with strict, role-based access controls.
- Deep-Tech Document Analysis (Quote Comparison): We moved beyond simple keyword matching. We built a specialized agent capable of reading complex vendor proposals, extracting line-item details, and cross-referencing them against the original RFP/RFQ.
- Predictive Intelligence: We deployed models to digest thousands of historical inspection reports, identifying patterns to predict maintenance requirements and costs decades into the future.
- Sovereign Knowledge Bases: For the internal "Ask Hemubo" tool, we gave departments (HR, ICT, Finance) full ownership of their knowledge bases, ensuring the AI only answers from approved, up-to-date documentation.
The Results
The platform has fundamentally changed daily operations at Hemubo:
- Weeks to Hours: The Quote Comparison tool reduced the proposal review cycle from weeks to mere hours. It creates internal "price books" by storing and analyzing vendor data, allowing Hemubo to benchmark market prices and, in some cases, skip the quote process entirely.
- Strategic Automation: Beyond analysis, the system now handles workflows—automating emails to vendors and processing updated proposals instantly.
- 30-Year Precision: Maintenance planning is now data-driven, with AI generating optimized long-term maintenance scenarios and cost projections based on actual building conditions.
- Self-Service Culture: Employees resolve routine queries instantly via the internal AI, significantly reducing the support load on administrative teams.
