Shipped in production. Picked for the job. Every choice has a reason.
Seypro builds with React, Next.js, Vue, Nuxt, TypeScript, Node.js, and Python. Databases include PostgreSQL, MongoDB, and Redis. Infrastructure runs on AWS, Vercel, and Cloudflare with Docker containerization. AI integrations use OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, and Mistral AI with Ollama for private deployments.
UI frameworks and tooling for web applications. Framework choice depends on project requirements, team familiarity, and ecosystem needs.
Default for complex SPAs and projects requiring a large component ecosystem. Used when client teams already work in React or when Next.js SSR is needed.
React metaframework for SEO-critical applications, static sites with dynamic sections, and projects that benefit from server components and edge rendering.
Preferred for rapid prototyping, admin dashboards, and projects where a lighter learning curve matters. Excellent reactivity model.
Vue metaframework for full-stack applications with SSR/SSG. Chosen when Vue is the base and we need file-based routing, server routes, and SEO out of the box.
Non-negotiable on every project. Catches bugs at compile time, improves refactoring confidence, and serves as living documentation for APIs and data models.
Utility-first CSS for all new projects. Eliminates naming debates, reduces stylesheet bloat, and pairs well with component-based architectures.
Server-side runtimes and frameworks. Choice depends on performance requirements, existing infrastructure, and the nature of the workload.
Primary runtime for most web backends. Non-blocking I/O makes it ideal for API servers, real-time applications, and microservices with high concurrency.
Used for data-heavy workloads, ML pipelines, scripting, and automation. First choice when the project involves AI/ML integration or scientific computing.
TypeScript-first Node.js framework for enterprise backends. Chosen when projects need strict architectural patterns, dependency injection, and built-in module organization.
Lightweight HTTP framework for simple APIs, webhooks, and middleware-heavy services. Used when NestJS would be overkill.
Data storage selected based on query patterns, consistency requirements, and data shape.
Default relational database. Used for transactional systems, complex queries, and anything requiring ACID compliance. Supports JSON columns, full-text search, and pgvector for embeddings.
Document store for content-heavy applications, flexible schemas, and rapid iteration phases. Chosen when data is naturally hierarchical or schema evolution is frequent.
In-memory store for caching, session management, rate limiting, and pub/sub messaging. Used alongside primary databases, not as a replacement.
Hosting and infrastructure decisions driven by cost, latency requirements, and operational complexity.
Primary cloud for enterprise workloads. EC2, ECS, Lambda, S3, RDS, SQS. Chosen when projects need fine-grained infrastructure control or specific managed services.
Deployment platform for Next.js and Nuxt applications. Zero-config CI/CD, edge functions, and preview deployments. Used when fast iteration matters more than infrastructure control.
CDN, DDoS protection, DNS, and Workers for edge compute. Applied to nearly every production deployment for performance and security.
Containerization for all backend services. Ensures environment parity from development to production. Used with Docker Compose locally, ECS or Kubernetes in production.
AI tooling for building intelligent features into applications. Model choice depends on task, cost, latency, and data privacy requirements.
GPT-4o and GPT-4o-mini for general-purpose generation, summarization, and classification. Chosen when output quality is the primary concern and data can leave the client environment.
Claude for long-context analysis, code generation, and tasks requiring careful instruction following. Preferred for safety-sensitive applications and complex reasoning chains.
Multimodal model for vision tasks, document understanding, and large-context workloads. Used when projects need image analysis, video processing, or million-token context windows.
Managed AI platform for enterprise deployments. Access to multiple foundation models with VPC isolation, fine-tuning, and IAM-based access control. Chosen for regulated environments.
Open-weight models for private deployment. Strong reasoning at lower cost. Preferred when EU data residency matters or when fine-tuning on proprietary data.
Local LLM runtime for running Llama, Mistral, and Qwen models on-premise. Used when data cannot leave the client environment or for development and testing.
PostgreSQL extension for vector similarity search. Used for RAG pipelines and semantic search when the dataset fits in Postgres without needing a separate vector database.
AI-powered search API for building research and information retrieval features. Used when applications need real-time web-grounded answers with citations.
Automation, testing, and monitoring to keep deployments reliable and repeatable.
CI/CD for all repositories. Runs linting, type checking, tests, and deployments on every push. Branch protection ensures nothing merges without passing checks.
Vitest for unit tests, Playwright for E2E. Coverage thresholds enforced in CI. Testing strategy depends on project risk profile — critical paths always covered.
Sentry for error tracking, Datadog or Grafana for metrics and logs. Alerts configured for error rate spikes, latency thresholds, and resource exhaustion.
