Khyren is the home for AI-augmented products and solutions across fintech and edtech — built by a small, focused team that combines decades of industry operating experience with the discipline of shipping fast, with users at the center.
Operating experience our team brings from
The decisions that shape people's lives — what to study, when to retire, how to invest, how to learn — are the decisions software has historically failed at the most. Generic dashboards. Cookie-cutter advice. Industry averages instead of personal context.
Khyren exists because we think AI has finally made it possible to build software that genuinely serves the individual user — not the average user. Software that listens, analyzes, and gives clear answers tailored to who you are. That's what every Khyren product is built to do.
The fintech and edtech industries we build in are the industries we've spent careers operating in — at TIAA, E*TRADE, Interactive Data, Thomson Reuters, and a portfolio of education businesses.
We don't bolt AI onto products. We build with AI throughout the development lifecycle — and we build products where AI does the analytical heavy lifting users actually need.
Our teams are deliberately small. Our cycles are deliberately short. The result: products that respond directly to user feedback within weeks, and a culture that doesn't accept slow as a permanent condition.
Shenba is a 20-year fintech product leader and CFA charterholder. She has spent her career building financial software at scale and her recent years building education technology from the ground up.
Earlier in her career, Shenba led product across wealth management, brokerage, trading, and institutional financial data — at TIAA/MyVest (enterprise wealth management), E*TRADE (retail brokerage and active trading), Interactive Data (eSignal real-time charting for active traders), and Thomson Reuters (institutional financial data and analytics).
In 2019, she founded a portfolio of education technology businesses, scaling them across digital learning, K-12 supplemental curriculum, and learning management platforms used by thousands of students and educators. That seven-year operating run is where the Khyren AI-augmented playbook was developed and refined.
She founded Khyren as the home for everything that comes next — bringing the operating discipline of those careers and the leverage of modern AI to a new portfolio of products and solutions, alongside a team that shares the same conviction.
Khyren is built by a deliberately small team of senior operators — engineers, designers, and product builders who have shipped at scale and want to do it faster, with AI as a force multiplier instead of a replacement for judgment.
Designers who came up shipping consumer products and B2B fintech tools. They translate user research into interfaces that respect the user's time and context.
Full-stack engineers who pair tightly with AI tooling, write rigorous tests, and ship to production every week. Background ranges from financial systems to learning platforms.
The people who decide where AI genuinely improves a product and where it's a distraction. They build the analytics, the recommendations, and the behavioral models behind every Khyren product.
PMs who spend more time talking to users than running roadmap meetings. Each product line has a dedicated PM who owns user research, prioritization, and outcome accountability.
The team that closes the loop between what users need and what we ship. They own onboarding, support, and the feedback channel that keeps the product roadmap honest.
A distributed team across the US and India keeping the company running — finance, partnerships, legal, and the day-to-day discipline of a multi-product operation.
We're growing the team across product, engineering, and AI roles. Get in touch if you want to build with us.
Every feature traces back to a documented user need. Engagement is a means; user outcomes are the goal.
Speed without rigor is sloppy. Rigor without speed loses. We build for both, always.
AI does the heavy lifting users can't do for themselves. It doesn't get bolted on for show.
We build in industries we've operated in. The regulations, the user behaviors, the failure modes — we've already seen them.
Complex problems deserve simple interfaces. The complexity belongs in the AI doing the analysis, not in the screens the user has to navigate.