Grade Coach's answer:

Grade Coach is in early-customer phase (post-launch, pre-scale). Tested by teachers at institutions where the founder has taught or partnered:

• RMIT University
• Scotch AGS (Australian Grammar School)
• ISB - International School of Business
• Hanyang University
• HCMC University of Science (KHTN)
• VUS - Vietnam USA Society English schools
• DYB Choisun
• Shandong University of Science and Technology
• Teach For America

TranscriptFetch's answer

Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.

Grade Coach's answer:

Frontend - React 18 + TypeScript + Vite - Tailwind CSS v4 - Zustand (state)

Backend - Netlify Functions - Google Gemini 3.1 Pro - OpenAI gpt-image-2

Database & auth - Supabase Postgres

Payments & ops - Stripe

TranscriptFetch's answer

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.

Grade Coach's answer:

Enzo, the founder, spent more than a decade teaching in international classrooms — RMIT, Scotch AGS, ISB, HCMC University of Science, plus partnerships with Hanyang University, Shandong UST, and Teach For America. The grading pile was killing his weekends.

He tried ChatGPT for nearly a full term before building anything. It worked — for a few papers. Then it drifted. Same essay scored differently on Tuesday vs Friday. Handwriting recognition was unreliable. The model would invent rubric criteria the teacher hadn't asked for.

The realization: general chatbots aren't built for grading consistency across a class set. A tool that locks the rubric, applies it the same way every time, and lets the teacher stay the final authority — that's what was missing.

Grade Coach started as a personal tool to grade his own classes. Then friends asked for access. Then schools. Now it's a complete teacher toolkit built on a shared substrate: every paper graded compounds into per-student concept-mastery maps that power worksheet generation, parent reports, and class analytics.

The founder still teaches the product the way he wished someone had taught him.

TranscriptFetch's answer

Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.

Grade Coach's answer:

K-12 teachers grading rubric-based assignments, with a wedge focus on:

• International-school and ESL teachers — the founder's background. The product is designed around rubrics teachers set themselves, not around language fluency, so it doesn't penalize ESL writing patterns unless your rubric tells it to.
• Teachers who grade from photos — handwritten papers, phone uploads, mobile-first workflow. Most paid traffic lands on mobile, so the product is built around that.
• AP / IB / MYP teachers with complex multi-criterion rubrics where consistency across the stack matters most.
• Schools and departments piloting AI-grading at a per-teacher subscription rate — not enterprise-only, not a custom-quote sales motion.

Secondary audience: university instructors grading short-answer or essay work where rubric-lock matters more than question-grouping.

TranscriptFetch's answer

Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.

Grade Coach's answer:

Three reasons, in order:

1. The teacher is always the final check. Every AI score and comment is shown to you for review and edit before anything is final. Other tools auto-submit; Grade Coach treats AI as a first pass, not the last word.

2. Private by default. Student work never trains any AI, never gets shared between teachers or schools. We don't sell student data. The privacy line is locked: student work stays between you and your students.

3. Consistency across the class. Where other graders treat each paper as a fresh AI prompt (and drift between them), Grade Coach locks the rubric on the first paper and uses that locked interpretation across the whole stack. The same essay would get the same grade if you re-ran it tomorrow.

The product is also honest about what it isn't. No AI detection (skeptical that it works reliably). No auto-submission. No chatbot pretending. Just rubric-locked grading that gives you your evening back.

TranscriptFetch's answer

Most short-form video has no caption track to download. TikTok’s auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.

Grade Coach's answer:

The locked-rubric guarantee is the core mechanic. When you upload your first paper, Grade Coach locks the AI's understanding of your rubric, then uses that exact same locked interpretation for every paper in the class set. Paper 1 and paper 30 are scored against identical criteria — no model drift, no inconsistency.

A few other things that set it apart:

• Built by a teacher, not a tech team that consulted teachers later. The founder spent 10+ years in international classrooms.
• Mobile-first. Snap a photo of handwritten student work from your phone. Designed for a teacher with a stack of papers and a phone, not a desk and a scanner.
• You bring your own rubric — paste it, photograph it, or write it. No template, no wizard. Department rubrics, AP/IB, state standards, custom criteria all work the same way.
• One credit pool across grading, worksheet creation, parent reports, practice generation, and class analytics. Not separate seats for separate tools.