Engineering Intelligence. Delivering Impact.
Engineering intelligent systems that operate at scale.
From intelligent applications to distributed infrastructure, Starone designs, builds and operates AI systems, software platforms and cloud infrastructure for organisations working in complex, fast-changing environments.
AI engineering
LLM applications, retrieval, agents and evaluation that hold up outside the demo.
Software engineering
Architectures chosen for the problem: modular monoliths, microservices, event-driven systems.
Cloud infrastructure
Infrastructure as code, Kubernetes and multi-region designs sized to real requirements.
Digital products
SaaS, fintech and mobile products taken from first release to scale without a rewrite.
Enterprise systems
Integration, modernisation and internal platforms with auditability built in.
Emerging technology
Web3, digital assets and tokenisation, treated as engineering rather than speculation.
What we engineer
Systems, not features
Most technology fails at the boundaries: between the model and the data, the service and the queue, the application and the infrastructure. We engineer the whole system, so those boundaries are designed rather than discovered in production.
AI & intelligent systems
A model call is the smallest part of an AI system. Moving from experiment to production means engineering every stage around it, and measuring each one.
Learn more- 01
Data
Ingestion, cleaning, permissions and freshness
- 02
Models
Selection, routing and inference pipelines
- 03
Orchestration
Retrieval, tools, agents and guardrails
- 04
Applications
Product surfaces and enterprise integration
- 05
Evaluation
Test sets, regression suites and review
- 06
Observability
Tracing, quality, latency and cost
Software engineering
Three lenses on the same system. Each one constrains the others, so we design them together.
The rendering strategy is an architectural decision. Server rendering, static generation and client-side applications each trade time-to-first-byte against interactivity, cacheability and operational cost. We choose per route, then enforce it with a component architecture and a design system the whole team can extend.
Learn more- React, Vue, Next.js, TypeScript
- SSR, SSG and SPA per route
- Design systems and component architecture
- Core Web Vitals and accessibility
Cloud & infrastructure
Infrastructure engineered around application requirements, security, resilience, performance and economics.
- AWS, Azure, Google Cloud
- Kubernetes and Terraform
- High availability and disaster recovery
Cybersecurity & DevSecOps
Security designed into trust boundaries, identity and the delivery pipeline, not added before launch.
- Threat modelling
- IAM and secrets management
- Pipeline and supply-chain security
Blockchain & Web3
The infrastructure around the chain: keys, nodes, indexing and reconciliation with off-chain systems.
- Smart contracts
- Wallet and custody architecture
- Tokenisation and RWA
Data engineering
Pipelines with contracts, lineage and quality checks, serving analytics and AI from the same foundations.
- Batch, CDC and streaming
- Warehouses and lakehouses
- Embedding and feature pipelines
Mobile engineering
Native and cross-platform applications designed for unreliable networks and long-lived app versions.
- iOS, Android, Flutter, React Native
- Offline-first sync
- Mobile CI/CD and store releases
Digital product engineering
A first release that proves the product and still leaves the data model, tenancy and deployment ready to scale.
- SaaS and marketplaces
- API products
- MVPs without the rewrite
From zero to production
One engineering lifecycle, from the first idea to continuous evolution
Every system we build passes through the same twelve stages. Skipping one does not remove the work; it moves it to production, where it costs more.
- 01
Idea
The outcome the system has to produce, and for whom.
- 02
Discovery
Requirements, constraints and the assumptions most likely to be wrong.
- 03
System architecture
Boundaries, data flows, integration points and failure modes.
- 04
UX / product design
The flows people use, validated before they are built.
- 05
Application engineering
Frontend, backend and mobile, built in tested increments.
- 06
AI / data layer
Pipelines, retrieval and models, with evaluation from day one.
- 07
Cloud infrastructure
Environments defined in code and sized to the workload.
- 08
Security
Identity, trust boundaries and controls designed in, then tested.
- 09
QA & performance
Functional, load and failure testing against explicit targets.
- 10
Deployment
Automated, staged releases with a rehearsed rollback.
- 11
Observability
Metrics, logs and traces tied to service-level objectives.
- 12
Continuous evolution
Measured improvement of quality, performance and cost.
How we engineer
Seven disciplines, applied in order
A method that keeps architecture, security and operations in the room from the first conversation.
- 01
Discover
Understand the business requirements, the constraints and the technical risk before proposing a solution.
- 02
Architect
Design the system around scalability, security and maintainability, and write the decisions down.
- 03
Engineer
Build modular, testable, production-grade software in short iterations with working software at each one.
- 04
Integrate
Connect APIs, data systems, third-party platforms and infrastructure, and make each integration fail safely.
- 05
Validate
Stress-test performance, security, reliability and user experience against the targets set in discovery.
- 06
Deploy
Automate deployment through infrastructure as code and delivery pipelines, with a tested way back.
- 07
Operate
Monitor, optimise and continuously improve the platform, then hand it over or keep running it.
Solutions
What these capabilities become
The same engineering disciplines, assembled into the kinds of systems organisations ask us to build.
AI Systems
Knowledge assistants, document intelligence, copilots and decision support grounded in your own data, evaluated before release and governed in operation.
SaaS Platforms
Multi-tenant platforms with enforced tenant isolation, single sign-on, metered billing, self-service onboarding and releases that do not interrupt customers.
Enterprise Applications
Internal systems and workflow platforms that integrate with ERP, CRM and legacy estates, keep data consistent across them and leave an audit trail.
FinTech
Payment, ledger, wallet and lending systems built on double-entry accounting, idempotent processing, continuous reconciliation and complete audit trails.
Digital Assets
Wallet and custody infrastructure, exchange components, deposit and withdrawal pipelines, on-chain monitoring and treasury tooling, engineered for key security.
Business Automation
Workflow engines, document processing and AI agents with human approval steps, orchestrating work across the systems an organisation already runs.
RWA Infrastructure
Engineering for real-world asset tokenisation: asset data, permissioned tokens, custody, investor onboarding, reconciliation, corporate actions and reporting.
Industries
Engineering problems differ by industry
A ledger, a clinical record and a shipment event stream fail in different ways. We design for the failure modes of the domain.
Reference architectures
How we would design it
Illustrative designs for common system types: the problem, the components, the decisions and the trade-offs. They are reference material, not accounts of client projects.
Reference Architecture
AI Knowledge Platform
A retrieval-augmented platform that answers questions over enterprise documents and systems, with citations, permission-aware retrieval and full observability.
Reference Architecture
High-Performance Transaction Platform
A microservices platform for financial transactions built on Java, Spring Boot, Kafka, Redis and a relational ledger, designed for correctness first and throughput second.
Reference Architecture
Digital Asset Infrastructure
The off-chain infrastructure behind a digital asset product: blockchain nodes, wallet services, secure key management, indexing, transaction monitoring and an API gateway.
Ingestion
- 01
Sources
Documents, databases, internal APIs
- 02
Parse and chunk
Extract text, keep structure
- 03
Embed
Batch embeddings, versioned model
- 04
Index
Vector and keyword indexes with ACLs
Query
- 01
Question
Authenticated user and their scope
- 02
Retrieve
Hybrid vector and keyword search
- 03
Rerank
Keep only the strongest passages
- 04
Generate
Grounded answer with citations
- 05
Evaluate and log
Trace, score and capture feedback
Technology radar
What we adopt, trial and assess
Our current engineering view of the technologies shaping AI, cloud, software, Web3 and security.
- Adopt9
- LLMs · RAG · Containers · Infrastructure as Code · Event-driven architecture · Distributed systems · DevSecOps · IAM · Application Security
- Trial7
- AI Agents · Kubernetes · Serverless · Microservices · Smart Contracts · On-chain analytics · Zero Trust
- Assess5
- Multimodal AI · Computer Vision · Edge computing · RWA · Digital Identity
Engineering insights
Written by the people who build the systems
Positions, trade-offs and design guidance from our engineers.
AI Engineering · 29 Sept 2026 · 4 min read
Why most AI prototypes fail in production
A prototype proves that a model can produce a good answer once. Production requires a system that produces acceptable answers every time, at a known cost, on data nobody curated. The gap between the two is engineering, not model quality.
AI Engineering · 22 Sept 2026 · 4 min read
RAG vs fine-tuning: choosing how a model learns your domain
Retrieval gives a model facts at request time; fine-tuning changes how it behaves. Treating them as competitors leads to the wrong architecture, because they solve different problems.
AI Engineering · 15 Sept 2026 · 5 min read
Designing reliable AI agents
An agent is a model in a loop with tools, and every property that makes it useful also makes it hard to trust. Reliability comes from the harness around the model: bounded loops, idempotent tools, managed context and evaluation of whole trajectories.
Where software engineering meets emerging technology
- Build what comes next.
- From complexity to production.
- Technology engineered for reality.
Bring us the hard part
An architecture that has to scale, an AI system that has to be right, infrastructure that has to stay up. Tell us about it and an engineer will reply within two working days.