Talktivity
Real-time voice-AI English coach: talk to an AI tutor, get a CEFR report, replay every mistake.
Cut a 32% incomplete-score rate on AI reports to near zero.
An AI English-speaking coach. Learners hold real-time voice conversations with an AI tutor over WebRTC, then receive an LLM-generated CEFR-scored report, wrapped in an 8-week course with spaced-repetition review, gamification, and community. Answers are now recorded and analysed on a durable pipeline, so a learner can replay the exact moment of each mistake rather than read about it. Ships as an installable Android build with over-the-air updates. Freemium, billed on two rails: AamarPay (BDT) and Lemon Squeezy (USD).
Problem
AI-generated score reports failed silently, a measured 32% of sessions produced incomplete or missing CEFR scores, and per-user voice minutes had no hard ceiling, so cost could run away.
Constraint
Voice sessions are live and stateful; a retry can’t replay a conversation, and the fix had to work inside an existing LiveKit + LLM pipeline the team already shipped.
Decision
Replaced probabilistic parsing of the model’s report with deterministic scoring, and enforced per-plan voice-time budgets server-side with BullMQ-armed token expiry that ends a session at the LiveKit layer.
Tradeoff
Deterministic scoring is stricter and occasionally rejects a valid-but-oddly-shaped model response, so it needed a fallback path and more up-front schema work than trusting the model’s prose.
Result
Incomplete-score rate dropped from 32% to near zero, and voice-time cost became bounded and predictable per plan.