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About

Rajiv Ramakrishnan
CTO, and the person who reads your code.

I have spent twelve years building systems where a bug costs someone money. Investor transactions, KYC pipelines, patient records, billing. The common thread is that nobody involved found "it works on my machine" reassuring.

What changed recently is who is writing the first version. A founder with no engineering background can now ship a working product in a weekend, and that is genuinely good — the bottleneck moved from "can you build it" to "can it survive contact". But the second problem did not get easier just because the first one got solved, and it is much harder to see. Broken code announces itself. Structurally soft code works perfectly right up until the day it does not.

I got this wrong myself once, early, in a way I still think about. We shipped a multi-tenant system where tenant isolation was enforced in the query layer and nowhere else. It was correct. It was also one refactor away from not being correct, and nobody would have noticed for weeks. Nothing happened, we caught it, but the gap between "currently correct" and "structurally safe" is the thing I now look for first in everyone else’s code.

What I will not do: tell you a system is fine when it is not, or dress up a list of style preferences as a risk assessment. If your codebase is in reasonable shape you will hear that, and you will have spent money to find out — which is a fair trade for knowing.

Track record

  1. R1

    Ashta

    CTO · AI-native real estate platform

    Verifiable
    • Production AI inference for property valuations
    • SOC 2 evidence pipeline across the engineering org
    • Re-architected the property-data ingestion path
  2. R2

    BuyProperly

    CTO · Fractional real estate investing platform

    Verifiable
    • Multi-tenant architecture backing $5M+ in investor transactions
    • KYC and KYB onboarding shipped end to end
    • AWS and Terraform infrastructure from a blank account
  3. R3

    Sei AI

    Senior engineering · YC W22

    • Data model underneath the first product iteration
    • Production deploy and incident response rotation
  4. R4

    Athenahealth

    Engineering · Healthcare platform

    • Production code on a HIPAA-bound system at scale
    • Tenancy patterns that transferred directly to fintech
  5. R5

    Verizon

    Engineering · Telecom, first role

    • Network-bound systems engineering
    • Where the instinct for production-grade defaults got installed