AI to Support QA and Documentation

Using AI to Support QA and Documentation in FTTH Projects

Across Europe, the scale and pace of FTTH deployment are pushing project teams to rethink how documentation—especially construction photos and technical records—is handled. As the volume of data increases, traditional manual review processes are proving time-consuming and error-prone.

One area gaining traction is the use of AI-driven tools to support automated quality checks. Image recognition technologies, for example, are being piloted to validate construction photos by detecting duplicates, checking geolocation data, and highlighting inconsistencies with network design plans. This approach has shown early promise in reducing the administrative burden during duct and trench inspections.

Similarly, natural language processing (NLP) is being tested to assist in reviewing compliance documents, as-built records, and project reports—flagging missing information or deviations from required formats. These AI-based workflows can supplement human review by handling repetitive tasks, reducing manual oversight, and speeding up handover readiness.

However, implementation is not without challenges. In practice, it’s essential to:

• Train models with sector-specific datasets for meaningful accuracy
• Align AI tools with on-site workflows and human QA processes
• Maintain transparency and human oversight to avoid over-reliance on automation

Early adopters of these tools—like the engineering team at Yungo—note that the greatest value comes when AI is integrated not as a replacement, but as a supporting layer within existing project controls. The broader lesson for the industry: AI can help scale QA efforts, but successful adoption depends on context, training, and collaboration with field teams.

As FTTH buildouts become more data-driven, exploring the role of AI in streamlining documentation may offer a worthwhile path to greater efficiency and consistency.

‍