Commercial ownership
Took partnerships from first conversation through diligence, launch and scale, alongside my co-founders.
05 / About
Behind the work.
The decisions, people and projects that shaped how I work.
Founder decisionsThree calls that shaped KarmaLife: first customer, real-time decisions, where GenAI belongs
Founder decisions
The calls founders
have to get right.
Every startup hits a handful of decisions that are hard to undo. These are three I made as a founder, the same kinds of calls I now help other founders think through.
Who is the first customer?
- Constraint
- Lenders wouldn't fund without proof; marketplaces wouldn't integrate without a lender.
- The call
- Treat the lender's risk team as the first customer. Open up model performance and governance before asking for capital.
- Trade-off
- A slower first deal and deeper scrutiny, in exchange for trust that compounds.
- Result
- A lender-funded platform disbursing ₹300-350 Cr a year.
What actually has to be real-time?
- Constraint
- Slow decisions cost conversion inside partner apps, and the platform had to keep scaling.
- The call
- Move signal ingestion into event-driven pipelines and keep only the decision itself synchronous.
- Trade-off
- More moving parts in the data layer, in exchange for a fast, predictable experience for partners.
- Result
- ~12s → ~1s decisions, 96% of underwriting automated.
Where does generative AI belong?
- Constraint
- Regulated decisions must be explainable and reproducible; LLM output is neither by default.
- The call
- Keep the decision deterministic and explainable. Use LLMs for interpretation, audit summaries and operations around it.
- Trade-off
- Less "AI magic" in the pitch, more confidence from the people who sign off.
- Result
- AI that risk and compliance teams can audit, not just demo.
What FS founders need from platformsThe four gaps I felt as a founder and hear from founders I mentor
From the founder's side of the table
What financial services founders need from the platforms they build on.
As a founder, I was the customer of cloud, AI and data platforms. These are the gaps I felt most, and the ones I hear repeatedly from founders I mentor.
- An introduction to the right enterprise buyerA warm path to the person who signs at a bank or lender is worth more than any amount of credits.
- Security and compliance evidence buyers acceptReference patterns for data residency, model governance and auditability that shorten a bank's review.
- Predictable AI economics past the free creditsFounders need to see what inference will cost at 10× usage before they commit an architecture to it.
- One person who knows their businessA single point of contact who understands the startup, the customer and the industry, and routes problems quickly.
Technical depthIndependent credit-risk research and prototypes on GitHub
Technical depth
Credible with founders'
engineering teams.
I stay hands-on enough to assess a startup's architecture, challenge its AI claims and judge whether it will hold up in a bank's technical review.
US credit-risk model evaluation
Logistic regression vs. XGBoost · SHAP
Benchmarks on SBA 7(a), HMDA, Freddie Mac and Fannie Mae data, including holdouts and calibration, not just ranking accuracy.
Explore the repository ↗GPU underwriting + LLM audit summaries
XGBoost · 0.869 AUC
GPU data processing, SHAP adverse-action explanations and an LLM audit-summary layer kept separate from the decision model.
Explore the repository ↗Multi-market alternative-data credit
0.826-0.893 AUC · 4 markets
One underwriting engine for India, Indonesia, the Philippines and the US, generating IFRS 9, Ind AS 109 and US CECL loss models from configuration.
Explore the repository ↗Production outcomes on this page come from KarmaLife. These projects are independent research and prototypes.
ExperienceKarmaLife, Smartron, Samsung R&D and early engineering roles
Experience
Depth before
the founder title.
My career spans mobile platforms, computer vision, biometrics, applied AI and financial services. That breadth helps me understand what a startup is really building and who will buy it.
Education
Senior Management Programme
Indian Institute of Management Ahmedabad
B.E., Electronics and Communication
Karnatak University
Senior Management Programme
Indian Institute of Management Ahmedabad
B.E., Electronics and Communication
Karnatak University
2019-present
KarmaLife
Co-Founder · Chief Product and Technology Officer
Venture-backed fintech. Built lender-funded, marketplace-embedded credit for gig and blue-collar workers, from inception to 2M+ users and ₹300-350 Cr in annual disbursements.
2018-2019
Smartron
Director · AI Labs
Established and led applied AI and deep-tech initiatives within the tronX platform.
2006-2018
Samsung R&D
Senior Technical Manager
Built engineering teams across mobile, computer vision and biometrics. Delivered iris recognition for Galaxy Tab Iris and Aadhaar authentication. Contributed international patents.
2001-2006
Early engineering roles
Celstream · Sasken · Pinexe · Suvistas
Software Engineer to Team Lead in embedded systems and multimedia codecs.