Our Engineering Process
A structured six-stage approach from discovery to deployment and beyond — so you always know where the build stands and what comes next.
The ORVINUS engineering process is a six-stage method — Discovery, Architecture, Engineering, Testing & Validation, Deployment, and Optimization — designed to take AI systems from idea to production without the usual casualties: scope drift, demo-grade code, and launches that can't hold load.
Two properties make it work. First, architecture comes before code: the system is blueprinted — data models, AI architecture, infrastructure — before engineering begins, so systems are built to extend rather than to be rebuilt. Second, every stage ends in something you can inspect: a scope document, a blueprint, working software, a test report, a live deployment, a performance review.
The ORVINUS engineering process consists of six stages: Discovery (business goal mapping and use-case validation), Architecture (system design and data modeling), Engineering (backend development, AI model integration, and security), Testing and Validation (automated, load, and AI evaluation testing, plus backtesting for trading systems), Deployment (cloud deployment with monitoring and API management), and Optimization (performance tuning, model refinement, and ongoing support).
Discovery to Scale
Discovery
We begin by deeply understanding your business goals, technical requirements, and use-case validation. This ensures alignment before a single line of code is written.
Architecture
Our engineers design the system blueprint — from AI architecture planning to data modeling and infrastructure design.
Engineering
Hands-on development where we build your backend, integrate AI models and implement security controls, in short iterations you can review.
Testing & Validation
Before anything ships, the system is tested against its requirements: automated tests, load and failure testing, AI output evaluation, and — for trading systems — backtests with realistic costs and slippage.
Deployment
We deploy to production with full cloud configuration, monitoring setup, and API management. Your system goes live with confidence.
Optimization
Post-launch, we tune performance, refine AI models, scale infrastructure, and provide ongoing support for continuous improvement.
Process Questions
How long does each stage of the process take?
It scales with scope: an AI-powered MVP typically runs 4–8 weeks end to end, with discovery and architecture in the first 1–2 weeks. Larger infrastructure and trading system builds are phased, with a milestone plan agreed during discovery.
How involved do we need to be during the build?
Discovery is collaborative and needs your time; after that, involvement is milestone-based. You review working software at each checkpoint rather than sitting in daily meetings — though we're responsive throughout.
What happens after deployment?
Stage six never really ends: performance tuning, model refinement, infrastructure scaling, and monitoring continue as long as you want us involved — via retainer, or as a clean handover with documentation so your team takes over.
Start at Stage One
Discovery is free: a call where we map your goals, validate the use case, and tell you honestly whether AI is the right tool. Response within 24 hours.