Unabyss's answer

Unabyss isn't just MCP connectors bolted onto keyword search. It's a full context layer that sits between your tools and your AI: it ingests data from 30+ sources, then cleans, chunks, tags, versions, and connects it into a multi-store context graph (graphs + RAG + semantic search). Your AI tools — Claude, Cursor, any agent — pull the right slice of context on demand over MCP, so you never re-explain yourself and never maintain manual .md files again. The structuring and retrieval layer is the moat; raw MCP connectors don't do it.

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.

Unabyss's answer

Most memory tools (Mem0, Letta, Supermemory, Cognee, Personal.ai) or platform-native memory (ChatGPT/Claude/Gemini) lock your context inside one place or treat it as a flat store. Unabyss is MCP-first and portable: your context lives in one user-owned layer and works across every AI tool at once. You get diff-based ingestion so only what changed re-syncs, full version history with newest-version-wins conflict resolution, and iOS-style granular permissions that keep personal and company context cleanly separated — with an audit trail and one-click revoke. It's the difference between a memory feature and a context infrastructure you control.

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.

Unabyss's answer

Two core personas. First, Builders — developers, AI consultants, and technical PMs who are MCP-native and already wiring up agents and automations; they activate through MCP naturally. Second, AI Enthusiasts — founders, operators, marketers, and growth people who use AI every day and are tired of re-explaining their context across tools. We're expanding from this prosumer wedge toward small teams (5–15 people), where the value shifts to a shared "company brain" and cross-project memory.

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.

Unabyss's answer

Unabyss began with a simple thesis: people should own a portable context layer that any AI tool can use. We started with content creation as the wedge — an AI ghostwriter with a deep-interview mode that captured how someone actually thinks and works — and hit $12.5K MRR at $500+ ARPU in seven months. But users kept telling us the magic wasn't the writing; it was that "it knows me." They started asking why their other tools couldn't start from that same context. That pull pushed us to build the full context vault and go all-in on MCP: the real "wow" isn't a vault UI, it's Claude or Cursor instantly having your context with zero copy-paste. We launched on Product Hunt in May 2026 and hit #1 Product of the Day.

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.

Unabyss's answer

Backend: Django 6 + Django REST Framework Web (product + marketing): SvelteKit — app.unabyss.com and unabyss.com Database: PostgreSQL (including Neon) Distribution: MCP server (primary), plus REST API and OpenAI function-calling adapters Integrations: 30+ native connectors Infrastructure: Docker Compose, VPS deployment behind nginx with SSL

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.

Unabyss's answer

• Over 1,000 users relying on Unabyss as their AI context layer
• Founders, operators, and AI power users across 30+ connected tools