Most AI assistants ask you to start from scratch every time. You switch tabs, paste your question, explain the context, wait for an answer, then do it all over again tomorrow. FastrFlow (https://fastrflow.com/), a Dallas–Fort Worth startup co-founded by Madhavam Shahi and Kavish Soningra, thinks that’s backward.
Instead of building another chatbot, the company has created what it calls a “screen-aware AI copilot”—an invisible overlay that watches what you’re already doing. If you’re reading lecture slides, editing a Google Doc, or working through a problem set, the AI sees your screen in real time and can answer questions without the usual copy-paste ritual. No tab switching. No re-explaining what you meant three days ago.
What makes FastrFlow different is what happens next. The tool doesn’t forget. It transcribes lectures and meetings, then turns them into structured notes, summaries, and action items. Over weeks and months, it builds what the founders call a “personal knowledge layer”—a searchable record of everything you’ve learned or discussed. A student can ask, “How does this week’s material connect to what we covered in October?” and get an answer grounded in their own history, not generic internet results.
From Dorm Rooms to Deal Rooms
The company has already signed up more than 2,000 users across 20-plus universities, mostly students juggling lectures, problem sets, and part-time work. But Soningra and Shahi saw the same problem playing out in professional settings. Sales reps spend hours prepping for calls, digging through product docs, and trying to remember what was promised in the last demo.
So FastrFlow built a live sales mode. During a call, the tool pulls accurate details from documentation and past transcripts, helping reps answer technical questions on the spot. The goal is to turn institutional knowledge into a live resource—not a scattered collection of Slack threads and Notion pages.
Aiming for 500 Universities and 1 Million Users
The company recently closed $375,000 in early funding—$335,000 from Mucker Capital and an additional $40,000 from the Telora Fellowship, a program that accepts fewer than 1% of applicants. That capital is being put toward an aggressive expansion: from 20 universities today to more than 500, with a target of one million student users.
The roadmap includes deeper automation—personalized review plans, auto-generated quizzes and flashcards, and instant answers that pull from a student’s own lecture history. On the enterprise side, the team is targeting 100-plus SMB deployments, particularly in sales organizations that need fast, accurate answers during live customer conversations.
The Telora Fellowship’s backing, alongside Mucker Capital, signals investor confidence in a tool that doesn’t just answer questions—it remembers them. For students cramming before exams and sales teams prepping for demos, that continuous memory layer might be the difference between repeating yourself and actually moving forward.
