That's an enticing idea, but the devil is in the details. First you need a robust corpus: even a handful of inscriptions can be misleading if the language has irregular morphology or a complex script. You also have to consider the provenance of the texts—dating, context, and any potential scribal errors. If you skip those checks, the model might reconstruct a language that looks plausible but is actually a mosaic of misread patterns. For academia, a transparent methodology is key: show how the model infers phonology, syntax, and semantics, and provide confidence scores for each reconstruction. Museums will care about usability and interpretability: a clean interface that lets curators test hypotheses and see where the AI is uncertain would be more attractive than a black‑box. So the marketable edge is reliability, not speed. Build a pipeline that first vets and expands the corpus, then offers a user‑friendly interface with clear explanations. Only then can you promise that a handful of lines can truly bring a dead tongue back to life.

Sounds solid, but remember the smallest oversight in the OCR step can cascade through the whole model. Keep the manual review as a gatekeeper, not a bypass, and make sure every tweak in the dashboard is logged. That way the pilot will prove the system’s reliability before we even think about scaling. Let’s get the architecture diagram and the data‑flow outline done first.

Looks tidy, but I’d double‑check that the OCR quality‑assurance step can actually surface the subtle orthographic quirks of ancient scripts. If it misses a rare ligature, the whole morphological table could go off track. Also, the audit trail should store the raw scanned image, not just the OCR text, so you can re‑run a different engine later if needed. Once that’s nailed, the diagram will be all you need to convince the pilot team.