Lingua Ponte — TODOs

Define target market: enterprise consulting firms? multinational HR? language schools? all three?

Competitive analysis: what exists today (Grammarly Business, Lilt, Unbabel, etc.) and where the gap is

Decide: SaaS platform vs. consulting framework vs. open-source toolkit vs. hybrid

Define the MVP scope — what’s the smallest thing that demonstrates the 5-layer model with real value?

Formalize the 5-layer barrier taxonomy with academic references (Austin/Searle, Grice, Hofstede, Bajtín)

Document the 4 patterns (asimetría, traducción fantasma, sobresimplificación, exclusión) with case evidence

Interview at least 3 people who work in multilingual teams about their friction points

Study existing NLP tools for conversation analysis (whisper, spaCy, pragmatic markers detection)

Tech stack decision: Python backend, what frontend, what database

Data model: conversations, barriers, patterns, interventions, KPIs

API design: how does conversation data flow through the 6-phase pipeline

Privacy/compliance: conversation data is sensitive — encryption, consent, GDPR

Conversation ingestion: accept transcripts, audio, chat logs

Barrier classifier: map segments to the 5 layers

Pattern detector: identify recurring friction across conversations

Intervention engine: recommend strategies based on detected patterns

Dashboard: visualize KPIs over time

Refine the 5 diagram variants for investor/partner pitch

Build a demo with real (anonymized) conversation data

Prepare RAE/ASALE presentation materials

Write the dispositio for the pitch: exordium→narratio→confirmatio→peroratio