Meet us at VIVE 2026 — Feb 22-25 in Los Angeles. Learn more →

Meet us at VIVE 2026 — Feb 22-25 in Los Angeles. Learn more →

Ivy Teng: From McKinsey to TextQL

Ivy Teng: From McKinsey to TextQL

Ivy Teng

Member of Operational Staff

–

September 19, 2025

–

[Team]
← Back to Blog

Why TextQL

I'd like to start with a simple truth I hate: 90% of success is because of luck, 90% of failure is because of bad luck.

7pm on a Friday, my inbox blinked and I blew up the email. "We're sorry to inform you that your office transfer to Greater China was denied.." I read the first line, shut the page, and felt my heartbeat spike. I opened it again, to search for a contingency plan at the end, but there wasn't one. My mind went blank. I didn't know what to feel or what to do, so I went back to writing the end of week email for the CEO of a $20B company, pulling numbers from various Tableau dashboards. Only after I wrapped up, realization came to me: wow, I have nothing to do with this company anymore.

4 straight visa lottery, each had a 25% chance of success, meaning the odds of losing them all were < 1/250, about ~0.4%. Yet that was my reality. An international transfer would have been the natural next step. But that year, Toronto, Singapore, Tokyo, China… every door closed in front of me. Timing couldn't have been worse: I was immersed in a high-stakes project, working long hours and juggling the weight of senior client expectations. The project consumed everything in me, leaving no time or mental capacity for my own visa situation until it was all too late. I had 8 weeks left to pack up my bags and get the hell out of USA. I went to the New York Office one last time to return my work laptop and phone. One more day and it would've become illegal to possess it.

McKinsey was my first love, the only job I applied to in my senior year. During my three years there, I genuinely had fun learning on the fly, travelling, asking stupid questions, fighting fire drills, and making good friends. I was told I have a spike in data analytics, so I leaned into it to land high-stake projects in industries I was curious about. One project I was analyzing drug fulfillment across health plans; in another, I led the ramp-up of a $200 million margin expansion initiative.

Each time I was the one stitching together messy, siloed data. Initially it felt heroic, like solving mind-bending puzzles no one dare to touch. Many projects in, I started to wonder: is there a more long-term solution than a temporary accelerated fix until the next crisis? When AI started to take off, I thought this would change the way we do client work. But a year and a half passed, I was still doing things the same old way.

When I found TextQL, I knew this had to be my next bet, for a few reasons:

  • TextQL handles gigantic data scale. In one go, TextQL processes data 10 million times larger than what ChatGPT can. A sandcastle for the AI Agent to spin up compute.
  • TextQL constructs a complete data context. It connects data from every corner of a company so analysis is done with full business knowledge, through an Ontology:
    • Philosophically, this means: stripping away everything else, from a data existential perspective, what does the company look like?
    • Practically, this means: How do five different sales pipeline datasets connect to four revenue-ops systems? Which supplier agreements flow into which inventories, and which campaigns actually move which SKUs? The data scientist who's been around for a decade knows those answers. With TextQL, now every analyst in the company can.
  • TextQL ships crazy fast - customer wants to chat with their tableau, someone built a bespoke tableau connector; customer wants to output ppt slides straight from data warehouse, we shipped the function in 3 days. No one here loses sleep over what to build to go viral or ride the next AI wave. Every day the engineering team scramble to ship things that should've existed long time ago but no one has successfully done it yet.
  • The AI works. TextQL's agent replicated an analysis I did with even more thoughtful insights. It made me sad and happy how fast it did it (7 minutes vs. my 2 days)

Lastly, people here all come with fascinating stories. One engineer got hired straight from a Discord channel after shipping an MVP in hours that had taken others weeks. Another dropped out of college mid-semester because waiting one more term to ship new features felt unbearable. Self-taught operators, ex-founders, top Mormon missionary…70% of the company are on a work visa, 100% ADHD geniuses.

I also joined TextQL with a fun back story. No formal interview process, I was told to "booking 25 executive level meetings with fortune 500 companies". I had zero GTM experience and 3 weeks to do it. I sucked at everything at first, but somehow people trusted me to experiment and turn things around. Those 3 weeks felt like 3 months, with a ton of lessons learned and habits unlearned.

Now I run our Deployment and do a mix of GTM. I'm excited to build out TextQL's Deployment team, continuing the McKinsey-style engagement, building use cases around our product that will solve customer problems. I run our content, shipping videos and newsletters to introduce product roadmap. I also run the event side of GTM, planing trade shows and partnership across the US. If you're working on something in product growth or building a lasting AI brand, reach out and let's be friends.