Assembling core
Skip to content
Technology & product engineering

AI, blockchain and data at zetta-scale.

ZettaCore Labs is a technology and product engineering company. We build AI systems, blockchain infrastructure, fintech platforms and data pipelines for businesses that need them to hold under real production load.

Talk to an expert See what we build
1021The order of scale we design toward
AI + chainEngineered as one system, not two
Production-firstBuilt to run, not to demo
AIBLOCKCHAINFINTECHDATAWEB3CLOUD
Who we are

Technology is evolving.
Your infrastructure should evolve faster.

ZettaCore Labs is a technology and product engineering company, founded in 2024 in Ahmedabad, India. We design and build the systems underneath digital products - the models, contracts, services and infrastructure that have to keep working when volume, scrutiny and complexity increase.

Most teams don't need another proof of concept. They need an architecture that survives contact with production: data that stays consistent, contracts that hold under audit, models that behave predictably, and infrastructure that scales without a rewrite. That is the work we take on.

We work across AI, blockchain, data, software and cloud as one connected practice rather than separate teams - because in real systems, the hard problems live at the boundaries between them.

How we think about engineering
Capabilities

Five practices.
One engineering core.

Select a practice to see what sits inside it. Each one is delivered by the same architecture-first process, so systems built across practices actually fit together.

Why ZettaCore

Principles we don't trade away.

These aren't positioning statements. They're the constraints we apply to every architecture decision - and what clients, partners and investors can hold us to.

Built for scale

Architecture chosen for where the system is going, not only where it starts. Growth should be a configuration change, not a rewrite.

P01

Engineered for security

Threat modelling, least privilege and audit trails belong at design time. Retrofitting security costs more than building it in.

P02

Intelligence at the core

AI placed where it changes an outcome - inside real workflows, with evaluation and fallbacks.

P03

Built for performance

Latency, cost and reliability are tracked from the first sprint, not discovered at launch.

P04

Technology that solves

We start from the business outcome and work backwards. If a simpler system gets you there, we recommend the simpler system.

P05
System view

Inside the core.

Six layers, one system. Scroll to move through the stack - every layer below changes what the layer above it can safely promise.

LAYER 01 - AI

Intelligence

Models, agents and retrieval sitting directly on your domain data. This layer is only as trustworthy as the data layer beneath it, which is why we never build it first.

LAYER 02 - DATA

Data

Pipelines, embeddings, feature stores and lineage. Getting data contracts right is what makes AI output reproducible instead of anecdotal.

LAYER 03 - APPLICATIONS

Applications

The products people actually touch: web, mobile and internal tools. Interfaces designed so the intelligence underneath is legible and controllable.

LAYER 04 - BLOCKCHAIN

Blockchain

Where settlement, provenance or shared state needs to be verifiable by parties who don't trust each other. Used deliberately - not applied to problems a database already solves.

LAYER 05 - CLOUD

Cloud

Compute, networking and delivery shaped around the traffic and cost profile you actually have, with recovery paths tested before they're needed.

LAYER 06 - INFRASTRUCTURE

Infrastructure

Environments, pipelines, secrets and observability defined as code. The layer that decides how fast everything above it can change without breaking.

CORE AI DATA APPS CHAIN CLOUD INFRA
Industries

Where the systems run.

Domain constraints change the architecture more than the technology does. These are the environments our engineering approach is built for.

Selected work

Case studies.

Payment infrastructure, trading analytics, tokenized real-world assets and SaaS delivery - the architecture decision behind each, and what it produced.

CASE 01 - FinTechPayments console showing multi-currency balances, settlement volume and a queue of transfers awaiting approval
FinTech

Enterprise payments platform

Challenge
Regulated payment operations have to do several hard things at once - verify identity to a compliance standard, move money across currencies, let corporate customers delegate access to their own staff, and process payouts in bulk. Each has a different scaling and failure profile, and a single-tenant, single-currency service cannot be stretched to cover them all.
Solution
A microservices platform where KYC, ledger, transfers, sub-user management and bulk payment processing are separately deployable services behind one API. Kubernetes on Azure gives each service its own scaling envelope, so a bulk payout run cannot starve interactive onboarding, and compliance logic can change without redeploying the transfer path.
Technology
Node.js, microservices, Kubernetes, Azure
Outcome
Deployed to production as an enterprise fintech platform.
FinTechView case study: Enterprise payments platform
CASE 02 - Web3 & digital assetsCrypto trading terminal showing a live candlestick chart, indicator strip and streaming order book
Web3 & digital assets

Multi-tenant crypto trading analytics

Challenge
Trading interfaces are judged on latency. Charts must update as the market moves, technical indicators have to be computed continuously rather than on request, and the same infrastructure has to serve multiple tenants without one tenant’s activity degrading another’s view of the market.
Solution
A Fastify service streaming live market data from the Binance API to browser clients over WebSocket, with Redis holding hot candlestick series so indicator computation reads from memory rather than re-requesting upstream. MACD, RSI and Ichimoku are computed server-side and pushed, keeping the client thin and every tenant’s chart consistent.
Technology
Fastify, Redis, WebSocket, Binance API
Outcome
Deployed as a multi-tenant analytics and trading dashboard.
Web3 & digital assetsView case study: Multi-tenant crypto trading analytics
CASE 03 - Web3 & digital assetsTokenization console showing an ERC-3643 token, an eligibility-checked holder registry and a cross-chain settlement queue
Web3 & digital assets

RWA tokenization and cross-chain settlement

Challenge
Tokenizing a real-world asset for a regulated market is not the same problem as launching an open token. Transfers must respect identity and eligibility rules at the contract level, the issuer needs recovery and enforcement powers a standard token does not provide, and settlement has to work across more than one chain without weakening either constraint.
Solution
Permissioned token contracts built on the ERC-3643 standard, where eligibility is checked on transfer rather than assumed at issuance, paired with a cross-chain settlement design so one instrument can settle on more than one network. Hardhat underpins the test and deployment workflow, because contracts carrying regulated value are reviewed before deployment, not after.
Technology
Solidity, ERC-3643, Hardhat, cross-chain settlement
Outcome
In active development for Luxembourg-focused financial products.
Web3 & digital assetsView case study: RWA tokenization and cross-chain settlement
CASE 04 - SaaSDelivery console showing a release pipeline, container environments on AWS and two-way Salesforce record sync
SaaS

SaaS delivery and Salesforce integration

Challenge
A SaaS product that has to stay in step with Salesforce faces two problems at once. The deployment pipeline has to make releases routine rather than eventful, and the integration has to keep two systems consistent in both directions - where each side can originate a change, and neither can be treated as the sole source of truth.
Solution
Containerised deployment to AWS EC2 using Docker Compose with images published to ECR, driven by GitHub Actions so a merge produces a deployed release without manual steps. Salesforce synchronisation runs bidirectionally: Apex triggers push changes outward and custom webhooks carry changes back, so an update on either side reaches the other.
Technology
AWS EC2, Docker, Docker Compose, ECR, GitHub Actions, Salesforce Apex
Outcome
Deployed to production on AWS with an automated release pipeline.
SaaSView case study: SaaS delivery and Salesforce integration
How we work

From problem to production.

A sequence, not a menu. Each stage produces something the next stage depends on, so decisions stay traceable back to the goal that caused them.

01

Discover

Business goals, constraints, existing systems and the failure modes that actually cost money.

02

Architect

Technical and product architecture, decision records, and the trade-offs written down before code starts.

03

Build

Iterative development, integration and testing against the scenarios that matter, not just the happy path.

04

Launch

Production readiness: monitoring, rollback, load behaviour, security review and handover documentation.

05

Scale

Optimise cost and latency, evolve the architecture, and keep the system maintainable as the team grows.

Insights

Notes from the build.

Engineering writing on digital assets, AI, data and infrastructure.

All insights
Build with ZettaCore

Have a complex idea?
Let's build the core of it.

From intelligent systems to decentralized infrastructure, ZettaCore turns ambitious ideas into scalable digital products.

Talk to an expert View case studies
Contact

Let's build what comes next.

Tell us what you're trying to build and what's currently in the way. You'll get a technical response, not a sales sequence.

  • Emailsales@zettacorelabs.com
  • Phone+91 63595 36599
  • OfficeAhmedabad, Gujarat, India

Both reach the engineering team directly.

We'll only use these details to reply to your enquiry.