AI & Intelligent Systems
Applied AI that does a specific job inside a business process — retrieval that cites its sources, agents with real guardrails, models evaluated against your data rather than a public benchmark.
We engineer intelligent AI, blockchain and cloud systems built for speed, security, scalability and real-world performance.
ZettaCore is a service-driven technology company. 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, software and cloud as one connected practice rather than separate teams — because in real systems, the hard problems live at the boundaries between them.
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.
Applied AI that does a specific job inside a business process — retrieval that cites its sources, agents with real guardrails, models evaluated against your data rather than a public benchmark.
Decentralized systems designed for the constraints that actually matter: gas cost, finality, upgrade paths and audit readiness. Contracts are written to be reviewed, not just to compile.
Products and platforms built on boring, durable foundations — clear domain models, typed boundaries, tested paths — so the interesting parts stay changeable for years.
The layer everything else stands on. Architected for predictable cost and recovery, with deployment, observability and security treated as product features rather than afterthoughts.
Data infrastructure that makes a number mean the same thing everywhere. Pipelines with contracts, quality checks that run as gates rather than reports, and governed sets that AI can be trusted against.
These aren't positioning statements. They're the constraints we apply to every architecture decision, and the reason systems we build tend to stay in service.
Architecture chosen for where the system is going, not only where it starts. Growth should be a configuration change, not a rewrite.
P01Threat modelling, least privilege and audit trails belong at design time. Retrofitting security costs more than building it in.
P02AI placed where it changes an outcome — inside real workflows, with evaluation and fallbacks.
P03Latency, cost and reliability are tracked from the first sprint, not discovered at launch.
P04We start from the business outcome and work backwards. If a simpler system gets you there, we recommend the simpler system.
P05Six layers, one system. Scroll to move through the stack — every layer below changes what the layer above it can safely promise.
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.
Pipelines, embeddings, feature stores and lineage. Getting data contracts right is what makes AI output reproducible instead of anecdotal.
The products people actually touch: web, mobile and internal tools. Interfaces designed so the intelligence underneath is legible and controllable.
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.
Compute, networking and delivery shaped around the traffic and cost profile you actually have, with recovery paths tested before they're needed.
Environments, pipelines, secrets and observability defined as code. The layer that decides how fast everything above it can change without breaking.
Domain constraints change the architecture more than the technology does. These are the environments our engineering approach is built for.
Payment infrastructure, trading analytics, tokenized real-world assets and SaaS delivery — the architecture decision behind each, and what it produced.
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.
Business goals, constraints, existing systems and the failure modes that actually cost money.
Technical and product architecture, decision records, and the trade-offs written down before code starts.
Iterative development, integration and testing against the scenarios that matter, not just the happy path.
Production readiness: monitoring, rollback, load behaviour, security review and handover documentation.
Optimise cost and latency, evolve the architecture, and keep the system maintainable as the team grows.
Engineering writing on AI, blockchain and infrastructure.
On-chain order book, off-chain matching with on-chain settlement, or a pooled counterparty. Three designs behind perpetual DEXs, and what each costs in latency, trust and liquidation risk.
9 min readSampleUsers state an outcome and solvers compete to deliver it. How intent-based DEX design moves execution risk off the user, and what it demands of settlement, MEV protection and solver incentives.
8 min readSampleERC-4337 introduced smart accounts; EIP-7702 lets an existing account behave like one. What gas sponsorship, batching and recovery mean for teams shipping crypto products to non-crypto users.
7 min readSampleFrom intelligent systems to decentralized infrastructure, ZettaCore turns ambitious ideas into scalable digital products.
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.
Both reach the engineering team directly.