Founder · CEO · QLU.ai
Fahad Jalal
He taught a machine to read a job description the way a recruiter reads a room - and to do it before lunch.
He taught a machine to read a job description the way a recruiter reads a room - and to do it before lunch.
Type a sentence - "VP of Sales who has run a private-equity exit" - and wait. Most recruiting software would hand you a Boolean puzzle and a coffee break. Fahad Jalal's QLU.ai hands you a shortlist. That single design choice, plain language in, qualified humans out, is the wager he has spent years making good on.
QLU.ai is an AI platform for executive search and talent sourcing, headquartered in San Francisco. It does the unglamorous heavy lifting of the recruiting trade: reading a hiring requirement, mapping the market, finding the people who fit, verifying how to reach them, and then reaching them. The pitch is that it replaces a stack of tools - LinkedIn Recruiter, Apollo, a sprawl of spreadsheets - with one system that claims to cover 97% of executives and to cut sourcing time by roughly 90%.
Jalal founded the company in 2020 and has run it as CEO ever since. He is, by training, an unusually broad builder: an electrical engineer who became a computer scientist who became a financier. That combination is the through-line of everything that follows.
Describe the ideal hire conversationally. No Boolean operators, no nested filters - the natural-language engine interprets the brief and returns candidates that match the intent, not just the keywords.
Career history, company context, verified contact details, and signals predicting whether someone might actually be open to a move. The dossier arrives assembled.
Automated outreach across email, phone, and LinkedIn that qualifies in real time and books the meeting. The follow-up that recruiters dread, handled.
The biggest challenge is not building powerful tools. It is getting consultants to use them.- Fahad Jalal, on the real obstacle to AI in executive search
Long before QLU.ai, Jalal proved he could move a number. At Mentor Graphics he grew a business unit from zero to $100M in annual recurring revenue in two years. That is the kind of line that opens doors, and it explains the confidence with which he now talks about market share and search timelines.
He has done it more than once. Across his career he has exited multiple startups to Fortune 100 companies and, by his own account, created over $1 billion in shareholder value along the way. He started young - ScoopSpy, his first company, ran from 2008. He has been a founding investor and chairman at Chowmill, a founding investor at Smartron, and the founder of SitterFriends, a marketplace that paired parents with childcare providers.
That last one is the tell. SitterFriends was, at heart, a matching problem: connect the right person to the right need, fast and with trust. QLU.ai is the same problem at the top of the org chart, with far higher stakes and far better tools.
Queen's University
BSc, Electrical Engineering (Circuit Design)
Stanford University
MSc, Computer Science
The Wharton School
MBA, Finance (Financial Derivatives)
Founder and CEO of his first venture (through 2010).
Head of Business Development - the seat where he drove a unit to $100M ARR.
Moved into a strategic advisory role at Mentor Graphics.
Founded a childcare marketplace; became a founding investor at Smartron.
Founding Investor and Chairman (through 2019).
Founded the company and became CEO. The current chapter begins.
QLU.ai raised its seed round to scale the platform.
Shipped a major release and took the stage at Hunt Scanlon's AI Talent Conference in New York.
Ask Jalal what makes a great AI leader and he will not point you to the best coder in the room. The ideal executive, he argues, brings "technical fluency, ethical judgment, and organizational adaptability" - someone who understands AI well enough to "question it intelligently" rather than worship it.
It is a revealing standard, because it is also a description of how he positions QLU.ai. The firm uses data analytics to read leadership narratives - what candidates say, what they have actually done, where they could go - and, in his words, to "look beyond buzzwords to understand their philosophy of AI." The machine surfaces; the human decides.
His read on where the whole profession is headed is bigger than faster sourcing. He sees executive search evolving from finding candidates toward "architecting leadership ecosystems powered by real-time intelligence" - recruiters who anticipate talent needs instead of scrambling to react to them. AI-first firms, he says, win by shrinking timelines and seeing around corners. Through all of it, his line holds: technology should enhance the consultant, not replace the consultant.
Exceptional leaders understand AI fundamentals well enough to question it intelligently.
We look beyond buzzwords to understand their philosophy of AI.
The future of search is architecting leadership ecosystems powered by real-time intelligence.
AI-first executive search firms gain advantage by anticipating client needs and reducing timelines.
The biggest challenge is not building powerful tools. It is getting consultants to use them.