ORVINUSThe Tools Behind the Systems
Every technology we build with, and exactly what we use it for. No résumé padding — if it's listed here, it's running in a production system we shipped.
The ORVINUS technology stack covers five areas: AI and machine learning (OpenAI, Anthropic, LangChain, PyTorch, TensorFlow), backend engineering (Python, FastAPI, Node.js, PostgreSQL), frontend (React, Next.js, TypeScript), automation (n8n, custom workflow engines, multi-agent orchestration), and cloud and DevOps (AWS, GCP, Azure, Docker, Kubernetes).
AI & ML
The model layer: foundation-model APIs, orchestration frameworks, and training libraries we use to build intelligent systems.
GPT-family models for chat, reasoning, and embeddings inside client products — selected per use case for capability, latency, and cost. Used in AI MVP Development, Applied AI.
Claude models for long-context reasoning, document analysis, and agentic workflows where instruction reliability matters. Used in AI MVP Development, Applied AI.
Orchestrating multi-step LLM pipelines — tool calling, retrieval chains, and agent loops with observability hooks. Used in AI MVP Development.
Data-framework layer for RAG: document ingestion, chunking strategies, and index management over private data. Used in AI MVP Development.
Retrieval-augmented generation architecture — grounding model answers in your documents so responses cite facts instead of guessing. Used in AI MVP Development.
Similarity search over embeddings — the retrieval backbone of every RAG system and semantic search feature we ship.
Managed vector search when clients want zero-ops retrieval infrastructure that scales with document volume.
Large language models as system components — integrated, evaluated, and monitored rather than bolted on. Used in AI MVP Development, Applied AI.
Adapting models to domain-specific tasks when prompting hits its ceiling — with evaluation baselines before and after.
Training and serving deep learning models in production pipelines, particularly for structured-data and vision workloads. Used in Applied AI.
Our primary framework for custom neural network research and training — from prototypes to GPU-accelerated production training runs. Used in Applied AI.
Open-model ecosystem: transformers, datasets, and inference endpoints when open-weight models fit the task or budget.
Classical machine learning — gradient boosting, regression, clustering — which still wins on tabular data and small datasets. Used in Applied AI.
Custom architectures designed for the problem at hand — from feed-forward predictors to sequence models for market data. Used in Applied AI, Algorithmic Trading.
Backend
The engine room: languages, frameworks, and databases behind every API, pipeline, and trading engine we deploy.
Our core language for AI systems, quantitative research, and data pipelines — the shortest path from model to production. Used in Applied AI, Algorithmic Trading.
High-performance async Python APIs — the default serving layer for our AI backends and model endpoints. Used in AI Infrastructure.
Event-driven backends and real-time services, especially where the product team also lives in TypeScript.
Lightweight Node APIs and middleware layers when a full framework would be overhead.
Batteries-included Python applications — admin panels, auth, and ORM out of the box for content-heavy products.
Minimal Python services and internal tools where a micro-framework keeps the footprint small.
Our default relational database — transactional integrity for SaaS products, plus pgvector for embedded retrieval. Used in AI Infrastructure.
Document storage for flexible-schema workloads: event logs, scraped datasets, and content systems.
Caching, rate limiting, queues, and real-time state — the performance layer in front of slower storage.
Postgres-plus-auth-plus-storage for MVPs that need a production backend in days, not weeks. Used in AI MVP Development.
Real-time sync, push, and auth for mobile-first products and rapid prototypes.
Typed API contracts where clients need flexible querying over complex data graphs.
The default integration surface — versioned, documented, secured endpoints for every system we hand over.
Live data delivery: streaming market prices, agent status, and dashboard updates without polling. Used in Algorithmic Trading.
Frontend
The product surface: frameworks and disciplines that make AI systems usable — dashboards, copilots, and client-facing apps.
Component-driven UIs for every dashboard, admin panel, and product front-end we build.
Our default full-stack React framework — server rendering, static generation, and API routes in one deployable unit. Used in AI MVP Development.
Type safety across the entire codebase — fewer runtime surprises in systems that move money and data.
The substrate of the web — used judiciously, typed wherever possible.
Semantic, accessible markup and modern layout — the foundation SEO and assistive tech actually read.
Utility-first styling for fast product iteration when the project's design system calls for it.
Production-grade animation — scroll choreography and micro-interactions like the ones on this site.
WebGL 3D visualizations for data-rich interfaces and interactive product experiences.
Every interface ships mobile-first — dashboards included, because operators check systems from phones.
Installable, offline-capable web apps when a native app would be overkill.
Automation
The workflow layer: orchestration engines and integration tooling that let AI systems act, not just answer.
Self-hosted workflow automation — the visual backbone we extend with custom nodes for client processes. Used in Applied AI.
Connecting client systems to the thousands of SaaS tools their teams already run on.
Purpose-built orchestration — like the geekstudio.us engine — when off-the-shelf automation hits its limits. Used in Applied AI.
Coordinating multiple AI agents across a process: routing, hand-offs, and supervision. Used in AI Infrastructure.
Stitching third-party services into one coherent pipeline with retries, idempotency, and audit trails.
Structured data acquisition from web sources — collection pipelines that feed research and pricing systems.
Extract-transform-load flows that turn raw feeds into analysis-ready datasets on a schedule. Used in Applied AI.
Reliable time-based execution — reports, retraining runs, and reconciliation jobs that never get forgotten.
Systems that react to events instead of polling — the pattern behind low-latency automation.
Cloud & DevOps
The reliability layer: infrastructure, deployment, and monitoring that keep our systems at 99.9% uptime.
Primary cloud for enterprise deployments — compute, storage, and managed AI services under fine-grained IAM. Used in AI Infrastructure.
Google Cloud for data-heavy and ML workloads — BigQuery, Vertex, and GPU capacity. Used in AI Infrastructure.
Microsoft-stack clients and Azure OpenAI deployments with enterprise compliance requirements.
Every service we ship is containerized — identical environments from laptop to production.
Orchestration for multi-service systems that need auto-scaling, rolling deploys, and self-healing. Used in AI Infrastructure.
Automated test-build-deploy flows — releases become boring, which is the point.
Our default CI runner: tests, builds, security scans, and deployments on every push.
Zero-ops hosting for Next.js products — preview deployments for every change.
Reverse proxying, load balancing, and TLS termination in front of application clusters.
Infrastructure as code — environments that can be rebuilt from a repo, not from memory.
Metrics, logs, and alerts wired into every deployment — problems page us before they page you. Used in AI Infrastructure.
Encrypted transport, hardened headers, secret management, and role-based access as defaults, not add-ons.
Your Stack, Engineered Properly
Whether we build on your existing stack or design one from scratch, every choice is justified by the workload — not by fashion.