ProBuild AI: AI-Assisted Workflows for Construction
QPOI helped build a production construction-tech platform that turns complex documents, blueprints, and project inputs into structured, reviewable workflows for real users.
Duration
10 Months
Platforms
Web (Angular + .NET)
Tech Stack
Angular, .NET Core, SQL Server
Engagement
End-to-End Architecture
The challenge
Construction teams often rely on dense PDFs, technical drawings, and fragmented manual processes. Interpreting that material by hand is slow, inconsistent, and hard to scale.
Off-the-shelf AI was not enough. The workflow needed to be structured, reviewable, and usable inside a production platform rather than presented as a black-box answer.
What QPOI built
- Document ingestion and staged analysis workflows
- AI-assisted extraction and task generation
- Human review interfaces and production platform delivery across Angular and .NET
A multi-stage, reviewable workflow
We designed and delivered a multi-stage document and workflow system that helps construction teams move from dense PDFs and project data to structured, actionable outputs inside a live platform.
Document ingestion and analysis
Built workflows to process PDFs and technical project documents in stages rather than relying on a single prompt.
Phase 1: OCR extraction and structural analysis.
Phase 2: Identifying core construction elements and dependencies.
Phase 3: Generating structured task outputs that could feed later workflow steps.
Human review and correction
AI outputs needed to be reviewable, not blindly trusted. We added revision flows so users could inspect and adjust results when source material was ambiguous.
User corrections could then carry through the workflow, improving reliability while keeping people in control of the final result.
Long-context handling
Construction projects generate large amounts of context, so the workflow needed to stay usable across extended task histories and complex documents.
We introduced background summarisation and state management to help the system preserve important decisions without degrading output quality over time.
Production implementation
Beyond the core document workflow, QPOI also contributed to the wider platform infrastructure needed to make the product usable in day-to-day operations.
Real-Time Infrastructure
We implemented real-time progress and response streaming so users could see workflow status, follow long-running operations, and interact with results as they were generated. Two SignalR hubs supported both high-level status broadcasting and token-by-token response streaming.
Frontend performance
Heavy client-side work was pushed off the main thread where needed, keeping the platform responsive during document compilation and complex UI operations.
Additional platform work
Beyond the core AI workflow, QPOI also contributed to marketplace functionality, reporting, and location-based platform features that supported the wider product.
- Marketplace tools: Map-based search and filtering for relevant opportunities.
- Reporting support: Interfaces and data flows that helped users work with generated outputs.
- Platform breadth: Engineering support across multiple operational areas of the product.
Enterprise Team Architecture
We helped build the admin and account-management capabilities needed for team-based usage, including structured onboarding and role-based access.
- Invitation flows: Tokenized onboarding links for workspace setup.
- Team controls: Role and seat management aligned to how construction companies actually operate.
Complex Financial Transactions
We also contributed to the monetisation layer, including billing logic and transaction handling that supported the commercial side of the platform.
Business impact
Turned document-heavy steps into guided digital processes inside a live platform.
Gave users structured outputs and revision tooling instead of opaque AI responses.
Extended the wider product through work on real-time systems, admin capabilities, and monetisation infrastructure.
Technology Stack
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