BigBen—conversationalEnglishinTelegramwithAIcharacters
People quit a language not over grammar but over the fear of speaking: a tutor is expensive and on a schedule, apps give templates with no real dialogue, and speaking practice never becomes a habit.
In brief
A conversational English trainer in Telegram (bot + mini app) with 10–12 AI characters — voice and text, live corrections, 48 scenes. The case explains the original process, implementation stages, available public outcomes, solution limits and the questions another company should verify with its own data before a pilot.
Key takeaways
- 1 min — to the first conversation
- 10–12+ — AI characters
- Success is evaluated through an accepted process and verified evidence, not a model promise.
The case explains the original problem, a verifiable approach, available public evidence and limitations. Details that cannot be supported by disclosed evidence are not presented as outcome promises.
Architecture and process
The speaking product separates lesson plan, dialogue model, recognition, feedback and progress profile. Users know when they are speaking with AI, and probabilistic feedback is not presented as a formal exam. Personal recordings are minimized and governed separately.
Public evidence boundary
The public page contains only the first-party facts approved for disclosure. Private architecture, personal data, contracts and internal logs are intentionally excluded.
What to validate before reuse
Validate user age and consent, accent recognition, pedagogical rubric, audio retention and human support. Practice frequency alone does not prove language progress; comparable learning tasks and transparent feedback are required.
How we built it
Diagnostic
We mapped why learners quit — the fear of speaking and the lack of live practice. We locked the outcome metric: how fast a person enters the first conversation.
Prototype on real data
We built a Telegram bot and mini app with AI characters and live corrections — voice and text on real dialogues.
Baseline and scope
The team documents the current process, decision owner, permitted data and acceptance criteria. Public reporting separates observable facts from hypotheses; unsupported details are not used to justify an outcome.
Verification and operating handoff
The team compares accepted outcomes with evidence and documents limitations, access, monitoring and fallback. The process owner accepts the system only after a real-scenario check. Version changes trigger reassessment; an unverified effect never becomes a public promise.
What came out
A similar process? Let us assess the impact first
We will review the task, data and metric. If a pilot is unnecessary or AI is the wrong fit, we will say so before any work starts.
Limitations and risks
- The outcomes belong to this specific project and do not guarantee the same effect in another company.
- The public version does not disclose confidential data, personal information or private infrastructure details.
Questions and answers
What problem did the Big Ben project address?
The case explains the original problem, a verifiable approach, available public evidence and limitations. Details that cannot be supported by disclosed evidence are not presented as outcome promises. Public metrics from this project are not a guarantee for another organization.
How did Aiconic structure the work?
Diagnostic: We mapped why learners quit — the fear of speaking and the lack of live practice. We locked the outcome metric: how fast a person enters the first conversation. Prototype on real data: We built a Telegram bot and mini app with AI characters and live corrections — voice and text on real dialogues. Public metrics from this project are not a guarantee for another organization.
Can another company expect the same result?
The outcomes belong to this specific project and do not guarantee the same effect in another company. The public version does not disclose confidential data, personal information or private infrastructure details. Public metrics from this project are not a guarantee for another organization.
Sources and evidence
- NIST Privacy Framework
Privacy and data-lifecycle management.
- AI Risk Management Framework
Lifecycle AI risk management.
- Agentic AI Threats and Mitigations
Tool, permission and autonomous-action risks.
Aiconic Editorial Team · This material was prepared with AI tools in an Aiconic editorial session. Facts and wording are limited to published project data.
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