AI-workflows.io's answer

Three reasons. First, AI agents replace the logic layer entirely. Where Zapier needs 15 steps with filters and branches, AI Workflows needs 3 nodes: trigger, agent, output. Second, the knowledge base is native. Upload your docs and the agent references them on every run, no Pinecone or vector database setup needed. Third, you can watch the AI think. Reasoning traces show exactly why an agent made each decision, so you can debug and improve instead of guessing. All of this with no code, starting at €59 per month.

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.

AI-workflows.io's answer

Most automation tools treat AI as just another step in a rigid if-then chain. AI Workflows is different because the AI agent IS the orchestrator. Instead of building 15-step workflows with branches and filters, you connect a trigger to an agent and let it read context, make decisions, and execute actions autonomously. The visual canvas makes complex AI orchestration feel like dragging blocks on a whiteboard. Built-in knowledge base (RAG), agent memory, and reasoning traces mean your workflows actually understand your business, not just move data between apps.

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.

AI-workflows.io's answer

Founders, small business operators, marketing teams, and revenue operations leaders at companies with 1 to 50 people. They are tech-savvy enough to have tried Zapier or Make but frustrated by the limits of rules-based automation. They want AI that understands their business context and makes judgment calls, not just moves data between apps. Typically running sales, support, content, or back-office operations and looking to do more with a lean team.

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.

AI-workflows.io's answer

We kept seeing the same pattern: teams bolting ChatGPT onto their Zapier workflows and hoping for the best. It worked for simple tasks but fell apart the moment they needed AI to actually understand context, reference company docs, or make decisions across multiple steps. So we built the platform we wished existed. A visual canvas where AI agents are first-class citizens, not afterthoughts. Where you can upload your knowledge base, watch agents reason in real-time, and deploy workflows that genuinely think. We are currently in early access, building in public with our founding members shaping the roadmap.

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.

AI-workflows.io's answer

1) Next.js (React) for the web application 2) React Flow for the visual workflow canvas 3) Supabase (PostgreSQL) for the database and authentication 4) OpenAI, Google Gemini, and Anthropic Claude for AI model providers 5) Vercel for hosting and edge delivery 6) Stripe for payments and billing 7) Resend for transactional email 8) Upstash Redis for rate limiting and caching 9) TypeScript end to end

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.

AI-workflows.io's answer

We are currently in early access and building our founding member base. We don't publicly disclose individual customer names at this stage, but our early users include:

Solo founders automating lead qualification and outreach Marketing agencies repurposing content across channels E-commerce operators managing reviews and inventory Small law firms extracting clauses and generating NDAs Recruiting teams screening resumes and scheduling interviews Operations managers turning meeting notes into action items