Learning and careers decision
sage
A small team or single capable developer can build a useful MVP (text and recorded-audio practice + feedback), but reproducing the full polished product (real-time speech coaching, native apps, community, clinical validation) is multi-week and operationally heavier than an MVP.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off120 h to build
$150/mo6 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- User picks a scenario → system runs an LLM-driven simulated conversation (text or recorded audio→ASR) → app evaluates responses and/or speech (pace, tone, filler words) → store progress and adapt next challenge.
What it still won’t have
- Polished mobile UX and native app store distribution
- Community/Discord integration and community-moderation features
- Clinically validated interventions and evidence-backed personalization
- High-quality real-time speech analysis latency/accuracy tuning
- Brand, paid marketing, and existing userbase
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
sage does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal LLM‑powered social skills coaching web app using Next.js (React) for frontend, a Node/Express API, Postgres for storage, and deploy on Vercel or Render; integrate OpenAI (or similar) for conversation LLM calls and a speech-to-text API (e.g., Whisper API) for audio transcription. Core features in scope: user signup/login, selectable practice scenarios, LLM-driven conversational turn handling (text and audio→ASR→LLM), generation of feedback summaries (pace, filler words, clarity) stored per user, and a simple progress dashboard. Out of scope: native mobile apps, community/Discord, paid billing, clinical validation. Include error handling for API failures, retries for ASR/LLM calls, unit/integration tests for the API and feedback engine, and CI deployment scripts.
How we checked
How the score was reached
- Partly verdict base52
- Evidence score52
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 1
Every page the run actually retrieved.
Integrity checks
What held up, and what did not.
