Built an AI platform for construction document intelligence and semantic search

A UK-based construction technology startup was building an AI platform that connected enterprise documents and operational data with large language models for contextual information retrieval and analysis.

The company needed a scalable way to process construction documents, retrieve contextual information, compare LLM outputs, and support semantic search across multiple enterprise data sources.

• Construction documents contained unstructured text, images, and technical formats
• Retrieving contextual information across enterprise sources was time-consuming
• Multiple LLM responses required evaluation based on user preferences
• Invoice mismatches were difficult to identify manually
• SharePoint and enterprise document integration increased processing complexity

1. Unify structured and unstructured data

Classic Informatics designed the platform to process text, images, and mixed document formats together for more accurate contextual understanding.

2. Build semantic retrieval capabilities

Vector and graph databases enabled semantic search and contextual retrieval across enterprise documents, SharePoint data, and embedded organisational content.

3. Support multiple AI models

The architecture allowed different LLMs to be selected based on business use cases, response quality, and user preferences.

4. Focus on scalable AI workflows

Reusable AI pipelines, document ingestion processes, and tenant-aware architecture helped support long-term platform scalability.

AI Document Intelligence

• Multi-format document processing
• Semantic document search
• Construction document Q&A
• Invoice data extraction
• LLM response comparison engine

Enterprise Data Integration

• SharePoint document integration
• Enterprise document ingestion
• Multi-source data embedding
• Tenant-aware data management
• Organisational knowledge indexing

AI & Retrieval Infrastructure

• Vector database architecture
• Graph database relationships
• OpenAI embedding pipelines
• LangChain agent workflows
• Semantic Kernel implementation

Platform Engineering

• Python Django backend platform
• AI workflow orchestration
• Cross-model inference support
• Retrieval-augmented generation pipelines
• Scalable document processing framework

The client can now process and retrieve information from enterprise construction documents through semantic AI workflows instead of manual document review. The platform improved contextual search, simplified multi-source data retrieval, and enabled AI-driven document intelligence across organisational systems. 

Business Impact

• Faster retrieval of construction information
• Improved contextual document search accuracy
• Reduced manual invoice verification effort
• Centralised enterprise document intelligence
• Flexible support for multiple LLM models