Co-founder & CEO · Krypton

Hi, I'm Neelesh. I'm building an agentic financial stack — one balance that pays, settles and earns at the same time.

Bengaluru. Before this: six years of payments and liquidity infrastructure inside Goldman Sachs, a robotic kitchen that served real customers, and a medical drone team I started at university.

Neelesh Nayan
3
Ventures founded
$20Bn
Daily balance movements, tracked in real time
35+
Engineers, clinicians and operators led
2
Patents in review

01 — July 2025 — July 2026

Krypton

Co-founder & CEOBengaluru, India

One balance. Always liquid, always earning.

TODAYpaymentstreasuryyieldmove · convert · sit idleCLARKnatural language · agents over MCPONE BALANCEliquid and earning at the same timepaymentstreasuryyieldone place, nothing idle, no hop between them
Same three functions. Underneath, one balance — and Clark is how anyone reaches it.
The thesis

Money lives in separate systems that don't talk to each other — one to pay from, one to hold in, one to earn on — and every hop between them leaks a fee and a few days. Cross-border makes it obvious: an INR to AED payment still routes through dollars, so you pay a spread on the way in and another on the way out.

Krypton collapses those systems into one balance, so money keeps moving and keeps earning at the same time. Clark sits over it as the interface, for a person asking in words or an agent asking over MCP.

What I did
  • Co-founded and led Krypton, across thesis, product architecture, regulatory strategy and fundraise.
  • Designed the three-layer composable stack (Pay, Yield Engine, Clark) and the netting model that settles direct pairs in under 60 seconds at 0.3–0.5% all-in.
  • Shipped the MVP to the Ethereum testnet, and drove the dual-entity path: PayCo under CBUAE + FIU-IND, FundCo as an ADGM Exempt Fund.
  • Assembled a team from Goldman Sachs, Morgan Stanley, Apple and MILA, including a Goldman MD and CFA charterholder as co-founder and COO.
Stablecoin infrastructureCross-border paymentsTokenised RWAAgentic AI
How it fits together
01

Krypton Pay

Cross-border rails

Netting pools hold reserve-backed liquidity in native currency pairs, so INR→AED settles directly. One hop instead of four, at any hour. Idle liquidity routes straight into the Yield Engine rather than sitting dead.

02

Yield Engine

Intelligent capital allocation

Risk profile in, yield out. Strategies run off-chain on QuantConnect's LEAN and become ERC-4626 vault tokens, backed by tokenised RWAs and tradable on Uniswap. Every strategy carries its own on-chain track record from day one, and the token works as collateral anywhere else in DeFi.

03

Clark

Agentic runtime

Clark takes natural language and orchestrates payments, portfolio, yield and tax behind it, then opens the same stack to outside AI agents over streamable MCP. Developers ship their own financial agents and earn per call, per AUM, or per yield generated.

<60s
Cross-border settlement, direct pair
0.3–0.5%
All-in cost vs. 3–5% legacy
1 hop
No USD intermediary, no redundant conversions
3
Markets served by one integrated stack
The team

Vishesh Jain

Co-Founder & COO

Managing Director at Goldman Sachs, 10+ years of quantitative modelling across mortgages, rates and credit. IIT Delhi, CFA Charterholder.

Dhuruva Priyan GM

AI & Quant Research

PhD candidate at MILA, Quebec AI Institute — deep learning, RL, and quant modelling for strategy execution.

Abhishek Mallik

Agentic Runtime Infra

Software Engineer at Apple. 18+ publications, 900+ citations, 2 patents across AI and big data.

Rest of the team3

Rushi Shah

Markets & Strategies

Exotic derivatives trader at Morgan Stanley; previously Risk Management at Nomura and Global Markets at Goldman Sachs.

Akshansh Bhanjana

Payments & Web3

Software Engineer at Goldman Sachs, 4+ years across Security Automation and Deposits Engineering in Transaction Banking.

Nirbhay Kumar

Product & Design

Designer at eCW, 4+ years crafting customer-centric products and leading design and growth initiatives.

The composable stack — one balance, always working.

Krypton Pay

Krypton Pay
Direct pair settlement through the netting pools — one hop, under 60 seconds.

Yield Engine

Yield Engine
Risk profile in, tokenised vault allocation out.
More demos1

Clark

Clark
The agent runtime driving the whole stack from natural language.

02 — 2020 — 2023

FoodLabs

FounderBengaluru / Manipal, India

Your kitchen on the cloud.

The thesis

Cooking for myself through lockdown, I kept noticing that a dish is a short sequence of operations — boil, blend, sauté — in the right order at the right settings. Which means you don't want a robot chef. You want a compiler.

So the hardware stayed dumb and cheap: single-purpose modules, one job each. The intelligence went into the software, which turned a dish into a graph of steps and ran whatever could run at the same time. Fifteen people, two patents and paying subscribers, all of it alongside the Goldman job. We never raised a rupee.

What I did
  • Built an AI-powered robotic kitchen with a vertically stacked layout that cut the floor space each one needed, and took it to ₹1.5L MRR on subscriptions, bootstrapped.
  • Developed Remy, an AI chef fusing olfactory, thermal and vision data to 94% prediction accuracy, and foodlOS, which parallelises cooking across modules and cut cooking time ~40%.
  • Shipped Foodl Go to Butterfly Café and ran 250+ orders through it, then opened a marketplace where users create their own recipes and earn per order. 50+ submissions in month one.
  • Built and led a team of 15 across robotics, AI, app dev, ops and sales, and demoed to Ranjan Pai (CEO, Manipal Group), Accel, Pi Ventures and Bessemer. All of it alongside a full-time job at Goldman.
Modular roboticsROS2Computer visionBootstrapped
How it fits together
01

Universal Cooking Language

The abstraction

Any dish, however complex, is a few fundamental cooking functions applied in the right order. Write the sequence instead of the recipe and every dish comes out of the same small instruction set.

02

Food Robotics as a Service

The hardware bet

Not one expensive general-purpose robot chef, but cheap single-purpose units that each do one function well. They stack vertically for three-dimensional use of floor space, and each function scales independently with what a location actually cooks.

03

foodlOS

The orchestration layer

Software is the product. A ROS2 runtime (dispatcher, resource manager, node manager, watchdog) parallelises function nodes into dishes and cut cooking time by ~40%, while a computer-vision loop tunes parameters and redeploys what it learns across every kitchen.

₹1.5L
MRR, bootstrapped — no outside capital
94%
Remy's prediction accuracy
~40%
Cooking time cut by foodlOS
250+
Orders served by Foodl Go
Patents2
  1. Patent #1 — GNN recommendation engine & recipe optimiserIn review

    Optimises for macros, taste preferences and kitchen inventory to drive new dish discovery and streamline kitchen operations.

  2. Patent #2 — Digital Cooking architectureIn review

    A three-step architecture that orchestrates distributed robots to replicate a recipe from intent, which is what makes remote cooking in a dark kitchen, and a marketplace for dishes, possible.

foodlOS — one orchestration layer, n kitchens.

The AI Kitchen

The AI Kitchen
The kitchen running, uncut.

Foodl Go — live at Butterfly Café

Foodl Go — live at Butterfly Café
The MVP in real service: 250+ orders, 21 ingredients, composed by the customer.
More demos4

The AI Chef

The AI Chef
Remy — olfactory, thermal and vision data fused to 94% prediction accuracy.

The Art of Digital Cooking

The Art of Digital Cooking
The core invention, and the interface for it.

Robotic arm

Robotic arm
The arm that does the work.

Digital twin

Digital twin
A digital twin of the AI kitchen.

03 — June 2019 — June 2025

Goldman Sachs

Tech Associate — Data & Analytics Platform, Transaction BankingBengaluru, India

Six years as an engineer on the platform Transaction Banking ran its numbers on.

The thesis

I was an engineer on Transaction Banking's data and analytics platform. The systems underneath it were built to move payments, not to answer questions about them.

So most of the work was getting numbers out of them fast enough to be worth having, and clean enough that Liquidity, Risk, Finance and Sales would act on them without checking twice. Balances, FX, four payment rails, revenue and risk — six years of it, close enough to see how each piece behaves under load.

What I did
  • Built a real-time Balance Movement Viewer covering ~$20Bn in intraday movements across 11,000 accounts, used daily by Liquidity, Risk, Finance and Sales, and the instrument TxB navigated the SVB deposit influx through. Demoed at TxB's Leadership Forum 2024 to CXOs and firm leaders.
  • Delivered a single source of truth for revenue and projections with TxB's CFO and Strats, bridging a ~30% gap in the FY23 numbers.
  • Measured debit risk and aggregated return exposure across ACH, Check, BACS and SEPA, plus payments latency, SLA tracking and account dormancy.
  • Onboarded AWS QuickSight firm-wide; worked the Stripe go-live, TxB's first marketplace and API client; and as an intern built an Account Decision Model driving $5.5M+ in fee savings.
Transaction BankingLiquidity analyticsPayments riskACH · BACS · SEPA
$20Bn
Intraday movements surfaced in real time
11,000
Accounts covered
$5.5M+
Fee savings from the Account Decision Model
~30%
FY23 revenue reporting gap closed
What the desks steered by.

04 — September 2016 — November 2018

Project DronAid

Founder & Team LeadManipal Institute of Technology, Manipal

Ambulances get stuck in traffic. Drones do not.

The thesis

The medicine exists and the doctor exists. What fails is the road between them, and fixing roads does nothing in the ten minutes that decide the outcome. So we went over it — a drone a responder summons from an app, route planned before takeoff, no pilot in the loop.

The airframe turned out to be the easy part. The hard part was the clinical protocol: what to carry, in what, for how long. Which is why five doctors sat with the twenty engineers. Nobody was paid and everybody had classes.

What I did
  • Founded and led Project DronAid, the official drone team of Manipal Institute of Technology: 20+ engineers across app dev, AI and robotics, alongside 5 clinicians.
  • Demonstrated a fully autonomous drone that emergency responders could summon remotely via an app.
  • Raised INR 50k in seed and 10L+ in university funding from MAHE, and brought in NVIDIA, SolidWorks, T-Motors and Ansys as sponsors.
  • Featured by Rajeev Chandrasekhar, Ministry of Electronics and Information Technology, Government of India.
Autonomous UAVMedical logisticsComputer visionStudent-founded
25+
Engineers and clinicians led
₹10L+
Seed and university funding raised
1st
In Design at the UAS Challenge
3rd
In Asia at the UAS Challenge
Record3
  1. Recognised by Rajeev Chandrasekhar ↗TechTatva, MIT Manipal · Oct 2023

    Then Union Minister of State for Electronics & IT, on the team's drones for medical delivery and their use of India's NavIC for positioning. Five years after I handed the team on.

  2. Universal Aerial Systems (UAS) ChallengeLondon, UK

    3rd in Asia · 22nd globally · 1st in Design · 5th in Business Use Case

  3. TechnoXian World Robotics ChampionshipDelhi, India

    First Runner Up

Both results are from my time leading the team. DronAid still runs at Manipal, with a fleet that now spans Atlas, Icarus, Phoenix, Vayu, Keish, Akira and the Murphy series, and has since competed at Quark Search & Rescue, the Vayurvya RotorCraft Competition, UDGAM, Mangaluru Blue and Elicit Expo.

Summon to delivery, with no pilot in the loop.
The minister's post, October 2023 — posted to Instagram by @rajeev_chandrasekhar.
Murphy Mark VI hexacopter at night, navigation lights lit, payload bay slung underneath
Murphy Mark VI — night trial, payload bay slung underneath.
Akira, a large carbon-fibre hexacopter with a DronAid canopy and medical payload box
Akira — the delivery platform, carrying its payload box.
Vayu, a fixed-wing UAV on the competition field
Vayu — fixed wing, on the line at the UAS Challenge.

05 — 2016 — 2018

Teach Code for Good

Volunteer, then Vice PresidentManipal, Karnataka

A student-run NGO that taught kids to code in schools which had never offered it.

The thesis

India has the youngest workforce in the world, and most of the schools serving it never teach a child to make a computer do anything. That gap doesn't close on its own; it compounds for forty years.

An engineering campus has exactly one thing in surplus — undergraduates who can already code and have free afternoons. Writing the curriculum took a weekend. Getting eighty of them to the same school every week for three years took everything else.

What I did
  • Started as a volunteer teacher, then served as Vice President, running the programme across partner schools around Manipal.
  • Led 80+ volunteers delivering a Python and web development curriculum to students in Classes 6–10, reaching 1,000+ children.
  • Ran the programme at government schools including Saralebettu Government School and Manipal Junior College, none of which had offered computer programming before.
  • Backed by the Resolution Project through its Social Venture Challenge, which supported the chapter as a student-led social venture.
NGOComputer science educationGovernment schoolsPython
80+
Volunteer teachers led
1,000+
Students reached
6–10
School classes taught

Skills

What I build with

01

AI systems

The architecture around a model, not the model.

  • Agent runtimes & orchestration
  • Structured output · MCP
  • Context modelling & retrieval
  • Eval loops & cost observability
02

Financial infrastructure

Money that crosses borders and survives a regulator reading it.

  • Stablecoin rails & multi-currency netting
  • Tokenised RWA · ERC-4626 · Solidity
  • Payments risk · ACH · BACS · SEPA
  • ADGM · CBUAE · FIU-IND structuring
03

System design

Pipelines a desk makes decisions off, in real time.

  • Python · TypeScript · SQL
  • Postgres · Snowflake · Airflow
  • Event-driven architecture · AWS
  • Schema & data-model design

Earlier: robotics and autonomy — ROS, computer vision, sensor fusion and flight systems, across DronAid and FoodLabs.

The thread

What's next

The pattern is the same each time: something everyone agrees is broken, that nobody has looked at closely enough to say which part actually fails. Roads fail, so we flew over them. A dish turns out to be closer to code than to a recipe, so we wrote a compiler for it. Payment rails fail in ways I watched from the inside for six years, so there is Krypton.

That instinct doesn't switch off, so two experiments run alongside the main work, both in the open and both unfinished. Neither is a company. One is about who gets seen for which job, the other about whether a model holds a rule when holding it costs something, and neither is a problem I should be poking at alone.

If you work on regulated money or agent infrastructure, or you need an operator for something hard, say hello.

Manipal Institute of Technology · BTech, Computer & Communication Engineering (minor in Digital Marketing) · 2015 — 2019