TypeScript is non-negotiable.
Every project, every file, every function. Full type safety from database schema to UI component. No exceptions.
typescriptlang.org ↗TypeScript is not optional in our workflow. Every project starts with strict mode enabled. Database schemas generate TypeScript types through Prisma or Drizzle. API responses are typed end-to-end with tRPC or typed fetch utilities. Component props are explicitly typed. Form validation schemas (Zod) double as runtime validators and compile-time types. This means an entire category of bugs (wrong property names, missing fields, type mismatches) simply cannot ship. The compiler catches them before the code leaves the developer's machine.
TypeScript is the single highest-leverage tool in our stack. It catches bugs at compile time instead of production. It makes refactoring safe: rename a field in the database schema and the compiler tells you every file that needs to change. It serves as living documentation: hovering over a function shows you exactly what it expects and returns. For our clients, this translates to fewer bugs, faster iteration, and a codebase that the next developer can understand without tribal knowledge.
Full-stack TypeScript development across the whole app
When clients ask for a TypeScript development partner, what they are really asking for is a codebase that does not accumulate silent bugs as it grows. That is what full-stack TypeScript buys, and we apply it everywhere rather than in patches. The database schema generates types, the API layer is typed end to end, form validation doubles as both a runtime check and a compile-time type, and component props are explicit. Because those types flow through the whole stack, a single change (renaming a database column, adding a required field) surfaces as a compile error in every file that still expects the old shape, before the code ever reaches a user. That turns a whole category of production incidents into problems the compiler catches on the developer machine. It also makes the codebase self-documenting: hovering a function shows exactly what it expects and returns, so the next developer moves without tribal knowledge.
Adding TypeScript to an existing JavaScript codebase
Not every project starts clean. A common engagement is taking a JavaScript application that has grown fragile and bringing it under type safety without stopping feature work. We do it incrementally rather than in one disruptive rewrite. We turn on the compiler in a permissive mode first, add types at the boundaries where bugs actually originate (API responses, form inputs, shared utilities), then tighten toward strict mode file by file as coverage grows. Each step is safe to ship on its own, so the team keeps delivering while the codebase gets steadily more reliable. Along the way the compiler surfaces the latent bugs already hiding in the code: the field that is sometimes undefined, the function called with the wrong argument. By the end you have the same safety net a greenfield TypeScript project starts with, earned without a risky big-bang migration.
How we apply TypeScript
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Strict mode from the start
New projects begin with strict mode enabled and the compiler set to fail the build on type errors. Turning this on at day zero is far cheaper than retrofitting it later, and it sets the expectation that untyped code does not ship.
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Types from the data source
We generate types from the database schema and derive validation schemas that serve as both runtime guards and compile-time types. The data source becomes the single origin of truth, so the shape of a record is defined once and reused everywhere.
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Typed boundaries end to end
We type the seams where systems meet: API responses, form inputs, and third-party integrations. Those boundaries are where untyped code usually lets bugs in, so making them explicit stops mismatches from propagating silently through the rest of the app.
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Refactor with confidence
With types flowing through the stack, renaming or restructuring is a guided task: the compiler lists every file that needs to change. That safety is what lets us keep improving a codebase over time instead of freezing it out of fear.
End-to-End Type Safety
Database schema to API response to UI component. A single type change propagates through the entire stack.
Runtime Validation
Zod schemas that serve double duty as TypeScript types and runtime validators for forms and API inputs.
Safe Refactoring
Rename a database column and the compiler identifies every file that needs to update. No grep, no guessing.
Systems we pair TypeScript with
Node.js
We write TypeScript on the server as well as the client, so validation logic and types are shared across the network boundary. One language and one set of types across the whole stack removes an entire class of integration bugs.
Next.js
Our Next.js apps are typed from route handlers to component props. A change in the data layer propagates as compile errors through every page that used the old shape, making large refactors routine instead of risky.
React
We type every component, prop, and hook. Typed React turns missing props and wrong shapes into compile errors caught during development rather than runtime failures a user finds first.
SvelteKit
Svelte 5 has strong TypeScript support, so our SvelteKit projects carry the same end-to-end type safety, from load functions through to the component layer, that we apply everywhere else.
