LinguaBOOST
A bright worktable showing layered speech and language analysis
AI English Analysis

Not one chatbot.
A controlled analysis system.

LinguaBOOST combines public AI model providers with a private application, task-specific pedagogy, structured validation and recorded quality checks. The distinction matters.

See the product evidenceTake the level test
The architecture

Several controlled layers between input and useful result.

01

Learner or teacher input

Text, voice, file or selected source

02

Private LinguaBOOST layer

Identity, roles, organisations and learning record

03

Pedagogy layer

CEFR context, source grounding, criteria and support language

04

Model routing

Fast, everyday, frontier and specialist services

05

Quality control

Validation, fixed fixtures, comparisons and teacher review

The boundary

Public models. Private product controls.

Only the content needed for a task is sent to an AI processor. LinguaBOOST keeps control of identity, roles, organisation boundaries, private files, the learning record, orchestration and the final experience.

  • Server-enforced access rules protect account and organisation data.
  • Uploaded teaching files use private storage and short-lived access.
  • LinguaBOOST does not sell personal data or use it for third-party advertising.
  • Our use of processors and GDPR rights is documented in the Privacy Policy.
A scored speaking attempt showing an editable transcript, five category scores, an overall score and Czech coaching
Speaking: five scores and coaching in your language.
Models selected by task

Speed where speed is enough. Judgement where judgement matters.

Fast models handle extraction and clean-up; stronger models handle learner-facing work; frontier judgement is reserved for demanding feedback and lesson building. Speech, voice and search use specialist services.

  • The public page does not hard-wire model names to features.
  • Routing can change when a better evaluated option appears.
  • The learner receives one consistent product rather than a collection of model interfaces.
A song lesson with the official music video playing beside the clean, readable lyrics
The official video on one side, the lyrics on the other.
A Kafka passage lesson with a plain-English introduction, a listen button and an author biography card
The passage explained, read aloud, and the author introduced.
Quality controls

Structured, grounded and compared before change.

A model answer is not accepted merely because it sounds fluent. Responses follow defined shapes, source-based analyses point back to the source, and candidate model changes can be compared on fixed speech and song fixtures before release.

  • Schema validation catches incomplete or malformed output.
  • Task-specific rubrics score the dimensions the learner practised.
  • Teacher review remains available; automated scoring makes no legal or similarly significant decision.
  • Learning events connect attempts to progress over time.
A lesson discussion answer scored 96 out of 100 with Czech feedback, corrections and a natural English rewrite
In a lesson: your answer corrected and rewritten naturally.
A practice activity where a written sentence is scored on organisation, vocabulary, grammar and content with bilingual coaching
In practice: your writing scored, in four dimensions.
Claim boundaries

What we do not claim.

LinguaBOOST does not claim to own a private foundation model, use EU-only hosting, hold ISO or SOC certification, deliver perfect AI accuracy, or replace a human assessor. We describe only the controls we actually operate.

Read the Privacy Policy

Judge the architecture by the work it produces.

Explore the real samples, read the data policy, or discuss the control model for your organisation.

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