Qadar AI Shield's answer

Qadar governs how teams use AI — right at the point of use in the browser — without inspecting the content of prompts. Most tools either sit at the network layer (heavy to roll out, blind to in-browser AI) or only evaluate models. Qadar combines three things in one lightweight layer: shadow-AI discovery, runtime enforcement (block, warn, observe, require justification, or mask sensitive data before it reaches an LLM), and an audit trail. A company with no dedicated security team can deploy it in minutes — no network re-architecture, no endpoint rollout.

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.

Qadar AI Shield's answer

• Lighter than enterprise suites. No network re-architecture or endpoint agent fleet — Shield runs as a browser extension plus an admin console and is live in minutes.
• Built for teams without a security team. Sensible defaults, plain-language policies, and warn/justify moments that coach users instead of only blocking.
• Content-free by design. Shield sees that AI is used and what type of data is at risk — not the content of prompts.
• Governance and evidence in one. Discovery, runtime enforcement, and an audit trail that helps you meet GDPR and EU AI Act deployer duties — not just a dashboard.

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.

Qadar AI Shield's answer

Small and mid-sized companies — roughly 50–250 employees — whose teams already use AI every day but have no dedicated AI-security or compliance function. Strongest fit: consulting, professional services, and tech-enabled B2B firms that are browser-first. Buyer: COO, Head of Operations, or founder; technical validator: Head of IT; risk co-signer: DPO/compliance. DACH-first, but relevant to any company that has to show it governs AI use.

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.

Qadar AI Shield's answer

Teams adopted AI faster than their companies could govern it. In most 50–250-person businesses, people were already pasting work into ChatGPT and other tools — with no visibility, no policy, and no audit trail — and the existing answer was either "block everything" or heavyweight enterprise security no lean team could run. Qadar was built to close that gap: real control over everyday AI use that a company without a security team can actually deploy.

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.

Qadar AI Shield's answer

Qadar AI Shield is delivered as a Chrome/Chromium browser extension (Manifest V3) for in-browser discovery and enforcement, paired with a cloud-based admin console for policies, audit, and reporting. It's content-free by design — Shield analyzes metadata about AI usage (which tool, what type of data), not the content of prompts — and works across 20+ AI platforms without an endpoint agent.

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.