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, LangChain, and Pinecone.
Tech Stack
Every tool here is one we have shipped production code with. No aspirational listings. Each choice has a rationale — here is what we use and why.
Frontend
UI frameworks and tooling for web applications. Framework choice depends on project requirements, team familiarity, and ecosystem needs.
React
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.
Next.js
React metaframework for SEO-critical applications, static sites with dynamic sections, and projects that benefit from server components and edge rendering.
Vue.js
Preferred for rapid prototyping, admin dashboards, and projects where a lighter learning curve matters. Excellent reactivity model.
Nuxt
Vue metaframework for full-stack applications with SSR/SSG. Powers this site. Chosen when Vue is the base and we need file-based routing, server routes, and SEO out of the box.
TypeScript
Non-negotiable on every project. Catches bugs at compile time, improves refactoring confidence, and serves as living documentation for APIs and data models.
Tailwind CSS
Utility-first CSS for all new projects. Eliminates naming debates, reduces stylesheet bloat, and pairs well with component-based architectures.
Backend
Server-side runtimes and frameworks. Choice depends on performance requirements, existing infrastructure, and the nature of the workload.
Node.js
Primary runtime for most web backends. Non-blocking I/O makes it ideal for API servers, real-time applications, and microservices with high concurrency.
Python
Used for data-heavy workloads, ML pipelines, scripting, and automation. First choice when the project involves AI/ML integration or scientific computing.
NestJS
TypeScript-first Node.js framework for enterprise backends. Chosen when projects need strict architectural patterns, dependency injection, and built-in module organization.
Express
Lightweight HTTP framework for simple APIs, webhooks, and middleware-heavy services. Used when NestJS would be overkill.
Databases
Data storage selected based on query patterns, consistency requirements, and data shape.
PostgreSQL
Default relational database. Used for transactional systems, complex queries, and anything requiring ACID compliance. Supports JSON columns, full-text search, and pgvector for embeddings.
MongoDB
Document store for content-heavy applications, flexible schemas, and rapid iteration phases. Chosen when data is naturally hierarchical or schema evolution is frequent.
Redis
In-memory store for caching, session management, rate limiting, and pub/sub messaging. Used alongside primary databases, not as a replacement.
Cloud & Infrastructure
Hosting and infrastructure decisions driven by cost, latency requirements, and operational complexity.
AWS
Primary cloud for enterprise workloads. EC2, ECS, Lambda, S3, RDS, SQS. Chosen when projects need fine-grained infrastructure control or specific managed services.
Vercel
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.
Cloudflare
CDN, DDoS protection, DNS, and Workers for edge compute. Applied to nearly every production deployment for performance and security.
Docker
Containerization for all backend services. Ensures environment parity from development to production. Used with Docker Compose locally, ECS or Kubernetes in production.
AI & Machine Learning
AI tooling for building intelligent features into applications. Model choice depends on task, cost, latency, and data privacy requirements.
OpenAI API
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.
Anthropic Claude
Claude for long-context analysis, code generation, and tasks requiring careful instruction following. Preferred for safety-sensitive applications and complex reasoning chains.
LangChain
Orchestration framework for chaining LLM calls, tool use, retrieval-augmented generation, and agent workflows. Used when applications need more than a single API call.
Pinecone
Managed vector database for semantic search, recommendation systems, and RAG pipelines. Chosen for production workloads where uptime and scalability are non-negotiable.
pgvector
PostgreSQL extension for vector similarity search. Used when the dataset fits in Postgres and adding a separate vector DB would be unnecessary overhead.
DevOps & CI/CD
Automation, testing, and monitoring to keep deployments reliable and repeatable.
GitHub Actions
CI/CD for all repositories. Runs linting, type checking, tests, and deployments on every push. Branch protection ensures nothing merges without passing checks.
Automated Testing
Vitest for unit tests, Playwright for E2E. Coverage thresholds enforced in CI. Testing strategy depends on project risk profile — critical paths always covered.
Monitoring & Observability
Sentry for error tracking, Datadog or Grafana for metrics and logs. Alerts configured for error rate spikes, latency thresholds, and resource exhaustion.
Have a project in mind?
We pick the right tools for the job, not the other way around. If your project has specific technology requirements or constraints, we will work within them.