We choose the stack to fit the problem, never the other way around.
Every tool listed here earned its place through repeated use on real engagements. We would rather work with fewer, well understood technologies than chase whatever is newest, because the system we hand you needs to be maintainable by your team long after we are no longer in the room.
Frontend Engineering
The interface is where a product either earns trust or loses it in the first ten seconds. We build frontends that feel fast, hold up under real usage, and stay maintainable long after launch.
Next.js
Our default framework for almost every web project. Server components, streaming, and file based routing let us ship fast without sacrificing SEO or accessibility, and it scales cleanly from a marketing site to a full application.
React
The component model behind nearly all of our interface work. We lean on it for its ecosystem maturity and the sheer number of engineers who can pick up a codebase built on it.
TypeScript
Every project we ship uses TypeScript in strict mode. Catching type errors before deployment saves far more time than it costs during development, especially once a codebase has multiple contributors.
Tailwind CSS
Utility first styling that keeps design decisions close to the markup and prevents the slow accumulation of dead CSS that plagues older stylesheet approaches.
Framer Motion
Used sparingly and intentionally for interface animation. Motion should clarify what changed on screen, not distract from it, so we reach for this when it earns its place.
shadcn/ui and Radix Primitives
Accessible, unstyled component primitives that we theme to match each client's design language instead of shipping a generic component kit look.
Backend and APIs
The backend is where correctness actually matters most. We design APIs and services that fail predictably, log clearly, and are simple enough for your future team to extend without us in the room.
Node.js
Our primary runtime for backend services, chosen for its ecosystem and the ability to share types and validation logic between frontend and backend in a single TypeScript codebase.
Next.js Route Handlers
For projects already on Next.js, we often keep the API layer in the same application rather than standing up a separate service, which reduces operational overhead for small and mid sized teams.
Python
Our language of choice for data heavy work, machine learning pipelines, and scripts that benefit from Python's scientific computing ecosystem.
REST and GraphQL
We pick the API style based on the client, not habit. REST for straightforward resource based systems, GraphQL when a frontend genuinely needs flexible, nested data fetching.
Zod
Runtime validation shared between client and server so the same schema defines what a valid request looks like everywhere it matters, instead of duplicating validation logic.
Data and Storage
A system is only as trustworthy as its data layer. We favor boring, well understood technology here because this is the part of a system that is genuinely expensive to get wrong.
PostgreSQL
Our default relational database for nearly every project. Mature, predictable, and capable of handling everything from a simple lead capture table to a complex multi tenant application.
Prisma ORM
Type safe database access with a schema that doubles as documentation. Migrations are tracked in version control, so schema changes are reviewable the same way code changes are.
Redis
Used for caching, rate limiting, and session storage when a project's traffic or complexity justifies it, rather than added by default.
Amazon S3 and Cloudinary
File and media storage, chosen based on whether a project needs raw object storage or built in image transformation and optimization.
Vector Databases
For AI features that require semantic search or retrieval, we use vector storage such as pgvector or a dedicated vector database depending on scale and existing infrastructure.
AI and Agentic Systems
This is where a lot of teams either overreach or underdeliver. We build AI features that are evaluated against real outcomes, run inside your actual permissions and data, and fail safely when they should.
Anthropic API and OpenAI API
We work with both major model providers and choose based on the specific task, cost profile, and latency requirements of the feature, rather than defaulting to one provider across every project.
Retrieval Augmented Generation
For features that need to answer questions grounded in a client's own documents or data, we build retrieval pipelines that reduce hallucination and keep answers traceable to a source.
Tool Calling Agents
Multi step agents that can call functions, query databases, and take real actions inside a product, always scoped to explicit permissions rather than open ended access.
Evaluation Pipelines
We build lightweight evaluation harnesses for AI features so quality is measured against test cases over time, not just judged by how good a single demo looked.
Fine Tuning and Prompt Engineering
Most problems are solved well through careful prompting and retrieval design. We reach for fine tuning only when the task genuinely requires it, since it adds ongoing maintenance cost.
Cloud and Infrastructure
Infrastructure should be invisible when it is working and obvious when it is not. We design for observability from day one rather than bolting it on after the first outage.
Amazon Web Services
Our primary cloud provider for projects that need more control than a managed platform offers, including custom compute, networking, and storage configurations.
Google Cloud Platform
Used when a client's existing infrastructure or specific services, such as BigQuery for large scale analytics, make it the better fit.
Vercel
Our default deployment target for Next.js applications, chosen for the tight integration between framework and platform and the speed it gives us during active development.
Docker
Containerization for services that need to run consistently across local development, staging, and production, particularly for Python based AI workloads.
GitHub Actions
Continuous integration and deployment pipelines that run tests, type checks, and migrations automatically before any code reaches production.
Mobile Development
Mobile work is where we are most deliberate about code sharing, since maintaining two entirely separate native codebases rarely makes sense for a growing product.
React Native
Our default for cross platform mobile apps, letting a single codebase serve both iOS and Android while still allowing native modules where performance genuinely requires them.
Expo
Used to accelerate development and simplify build and release pipelines, especially for early stage products that need to iterate quickly before committing to a bare workflow.
Native iOS with Swift
Reserved for features that require deep platform integration or performance characteristics that cross platform tooling cannot deliver.
Native Android with Kotlin
Same principle as iOS. We reach for native development when the product genuinely needs it, not as a default starting point.
Security and Compliance
Security is treated as a design constraint from the first architecture conversation, not a checklist applied right before launch.
Authentication and Session Management
We implement authentication using hashed and salted credentials, signed session tokens, and httpOnly cookies, and design role based access control into the data model rather than layering it on top after the fact.
Input Validation
Every external input, whether from a form, an API request, or a webhook, is validated against a strict schema before it touches business logic or the database.
Rate Limiting
Public facing endpoints, particularly forms and authentication routes, include rate limiting to reduce abuse and automated attacks.
Environment and Secrets Management
Secrets live in environment variables managed through the hosting platform, never committed to source control, and rotated when a project's risk profile calls for it.
Engineering Practice
The tools matter less than the discipline behind how they are used. This is the part of our stack that has nothing to do with code and everything to do with whether a project stays healthy over time.
Git and Trunk Based Development
Short lived branches, frequent merges, and a main branch that stays deployable at all times, which keeps integration problems small and easy to fix.
Code Review
Every change of meaningful size is reviewed by a second engineer before merging, even on small teams, because a second set of eyes catches what the author's familiarity with the code hides.
Automated Testing
Unit and integration tests focused on the logic most likely to break silently, such as billing calculations, permission checks, and data transformations, rather than chasing coverage numbers for their own sake.
Documentation as Part of Delivery
Architecture decisions, API contracts, and setup instructions are written down as the project progresses, not reconstructed from memory at handover.