Anystack.Chosenfortheproblem.

Frontend

The part your customers judge you on. Built in whichever framework fits the product, or the one your team already runs.

• React and Next.js

  Server-rendered where speed and search matter, interactive where the work happens.

• Vue and Nuxt

  A lighter component model for teams that already know it, or products that grew up on it.

• Angular

  Large enterprise front ends with strict structure, extended and modernised rather than rewritten.

• TypeScript

  Types across the whole stack, so a renamed field breaks the build instead of a customer's screen.

Backend

Where the business rules live. The language follows the work and your team, not our habit.

• Node.js

  NestJS or Express when one language across client and server keeps the team small.

• Python

  Django or FastAPI for data-heavy products, pipelines and anything that sits near a model.

• Java and .NET

  Spring Boot and ASP.NET Core for enterprise systems, integrations and long-lived platforms.

• PHP and Go

  Laravel for fast business software, Go for services where throughput and footprint matter.

Mobile

iOS and Android, cross-platform or fully native, chosen by what the app has to do.

• React Native

  One codebase for both stores, sharing types and logic with a TypeScript backend.

• Flutter

  One codebase with its own rendering, for apps where pixel-identical UI on both platforms matters.

• Swift and SwiftUI

  Fully native iOS when the app lives on the camera, sensors, audio or heavy graphics.

• Kotlin and Compose

  Fully native Android with the same reasons, and the same care for low-end devices.

Databases

The decision that outlives every screen. Relational, document, search or vector, picked by how the data is read and written.

• PostgreSQL and MySQL

  Constraints, transactions and indexes doing the work the application should not have to repeat.

• MongoDB and DynamoDB

  Where documents genuinely vary in shape and joins are not the access pattern.

• Redis

  Caching, rate limits, sessions and job queues, with invalidation planned rather than hoped for.

• Search and vectors

  Elasticsearch, OpenSearch, pgvector or a dedicated vector store, sized to the retrieval volume.

AI

Models are one component in a system. The engineering around them, retrieval, evaluation, cost control and fallback, is what makes them usable.

• Any model provider

  OpenAI, Anthropic, Gemini or open models, chosen per task on cost, latency and quality behind a swappable interface.

• Agents and orchestration

  LangChain, LangGraph or plain code, used where orchestration earns it, not as a default wrapper.

• RAG

  Chunking, embedding and retrieval tuned against your own documents, with answers traceable to the source passage.

• Evaluation

  A test set and a scoring loop before launch, so a prompt or model change is measured rather than guessed at.

Cloud and delivery

Any cloud, the same discipline. Every environment is built the same way, so releasing on a Friday is a non-event.

• AWS, Google Cloud and Azure

  Compute, storage, managed databases and queues on the cloud you already pay for, or the one that fits your region and compliance.

• Docker and Kubernetes

  The same artefact in development, staging and production, orchestrated only when scale calls for it.

• Infrastructure as code

  Terraform or Pulumi, so every environment can be rebuilt from the repository instead of from memory.

• CI/CD and monitoring

  Tests on every push, gated migrations, alerts that reach a person, and rollback written before the first deploy.