Fiber Engineering AI Pro

The problem

Fiber engineering projects often suffer from data drift between design reports, BOMs, and cost estimates. Manually reconciling optical power budgets with field-collected OTDR traces or as-built data is error-prone and time-consuming.

What it does

• Calculates end-to-end optical power budgets for GPON, XG-PON, and XGS-PON architectures using deterministic formulas.
• Generates procurement-ready Bill of Materials (BOM) with automated quantity derivations for splitters, closures, and cable lengths.
• Performs OSP design QA/QC audits to identify capacity bottlenecks, oversubscription risks, and link-loss failures.
• Reconciles as-built field data against original designs to flag material deviations in route or equipment.
• Estimates project schedules and cost structures based on production rates, civil labor, and material drivers.

Frameworks & tools

Standard fiber technologies including GPON, XG-PON, and XGS-PON. Processes GIS data via KML, GeoJSON, or coordinate text. Follows standard ITU-T G.984/G.987/G.9807 protocols.

Why this beats prompting it yourself

General-purpose LLMs hallucinate optical loss values and fail to maintain mathematical consistency across different project documents. This skill enforces strict data labeling (CALCULATED vs. ASSUMPTION) and ensures that a change in route length automatically updates the BOM, optical budget, and cost estimate without silent drift.

Use cases

• Perform a preliminary FTTH design audit to check if a link will close before deployment.
• Convert GIS route coordinates into a count of required splices, closures, and slack fiber.
• Analyze OTDR event tables to identify high-loss fusion splices or macrobends.
• Draft technical project proposals that link commercial costs directly to engineering quantities.

Known limitations

Cannot parse binary files like .sor (OTDR) or .shp (Shapefiles); requires text-based data import. Does not perform licensed structural pole-loading analysis.