Languages
TypeScript
The type system that turns whole categories of runtime bugs into red squiggles you fix before lunch.
Overview
TypeScript is a superset of JavaScript that adds a static type layer, checked at compile time and then erased entirely — the code that runs in your browser or on your server is plain JavaScript. That single design decision explains almost everything about how it behaves in practice: it is exceptionally good at catching mistakes while you write, and it makes precisely zero promises about what happens once the process is running. Understanding that boundary is the difference between a team that gets real value from types and one that spends its days fighting them.
We have shipped TypeScript across the stack — Node services, React and Next.js front ends, build tooling, infrastructure scripts — on codebases ranging from a scrappy startup MVP to a monolith with several thousand modules and a dozen engineers touching it daily. Our view is unromantic. On small throwaway projects, types can be a tax. On anything that has to be maintained, changed by people who did not write it, or refactored under time pressure, the type checker becomes the single most valuable member of the team: it never gets tired, never forgets an edge case, and never approves a rename that silently broke a call site three folders away.
This page is written for the engineering lead deciding whether to commit, and for the founder who has heard TypeScript is “best practice” and wants to know what that actually buys. We will be specific about the wins — compile-time safety, refactoring confidence, editor tooling, shared types between front end and back end — and equally specific about the costs, because pretending there aren’t any is how teams end up with 200-line generic types nobody can read.
Best for — Teams building non-trivial, long-lived applications — SaaS products, internal platforms, API-heavy front ends — where correctness, maintainability, and safe change over months or years matter more than the fastest possible first commit.
Why teams choose TypeScript
Bugs caught before they ship
An enormous share of everyday JavaScript defects — undefined is not a function, reading a property off null, passing a string where a number was expected, forgetting a case in a switch — are type errors in disguise. TypeScript surfaces them as you type, in the editor, before the code ever runs a test let alone reaches a user. That feedback loop is measured in seconds, not in a support ticket three weeks later.
Refactoring you can actually trust
The real payoff arrives the day you have to change something central. Rename a field, tighten a function signature, split a module — the compiler walks every dependent line and lists exactly what broke. On a large codebase this converts refactoring from a nerve-wracking, test-and-pray exercise into a mechanical one: fix the red, ship the change, move on.
Tooling that pulls its weight
Because the types are known statically, editors give you accurate autocomplete, go-to-definition, inline documentation, and reliable automated renames across the whole project. This is not a cosmetic nicety — it measurably speeds up the daily work of reading and navigating code, which is where engineers spend most of their time.
Why businesses choose TypeScript
- It is JavaScript with guardrails, not a different language — every JS library, pattern, and hire transfers directly, and you can adopt it one file at a time.
- It has effectively won the ecosystem: the major frameworks ship types, most popular packages are typed, and “does it work with TypeScript?” is rarely a question any more.
- The safety compounds with codebase size — the bigger and longer-lived the project, the more the type checker pays you back, which is exactly the regime where mistakes are most expensive.
- It is backed by Microsoft with a large, active community, a stable release cadence, and no realistic risk of being abandoned.
What we build with TypeScript
The capabilities this technology is genuinely strong at — and what we most often build with it.
Structural typing
TypeScript checks types by shape, not by name — if an object has the properties a function needs, it fits, regardless of what class or interface it was declared as. This maps naturally onto how JavaScript objects actually behave and makes it painless to describe the loose, duck-typed data that flows through real applications.
Generics
Generics let you write functions and data structures that are reusable across types while staying fully type-safe — a single sort, cache, or API-client implementation that preserves the caller’s exact types. Used with restraint they eliminate duplication; used without it they become the main source of unreadable type code, which is why we treat “can a mid-level engineer read this in six months?” as the deciding constraint.
Discriminated unions and exhaustiveness
Model a value as “one of these specific shapes” and the compiler forces you to handle every variant — add a new state to a union and every switch that forgot it lights up red. This is one of the highest-value patterns in the language for modelling application state, API results, and event streams correctly.
Strict mode and null safety
With strict settings enabled, null and undefined become tracked, distinct types rather than silent landmines. The compiler refuses to let you dereference something that might be absent until you have handled that case — closing off one of the most common runtime crashes in all of JavaScript.
Type inference
You do not annotate everything. The compiler infers most types from how values are used, so idiomatic TypeScript reads much like clean JavaScript with annotations only where they add clarity — at function boundaries and public interfaces. Good code leans on inference and reserves explicit types for the places that document intent.
Declaration files and the type ecosystem
Third-party libraries ship type definitions (bundled, or via DefinitelyTyped) so external code participates in the same safety net as yours. You get autocomplete and error-checking against libraries you did not write, and you can author your own declarations to type legacy or untyped dependencies.
Use cases
Full-stack applications with shared contracts
A Next.js or React front end and a Node back end sharing the same type definitions for every request and response. Change the API and the front end fails to compile until it is updated — the class of “the shapes drifted apart” bug simply stops occurring.
Long-lived business platforms
Internal tools and SaaS products that live for years and pass through many hands. Types encode the domain rules directly into the code, so an engineer who joins in year three can change things safely without having lived through the decisions of year one.
Design systems and shared libraries
Component libraries and utility packages consumed across many teams, where a strong public type surface is the contract. Consumers get precise autocomplete and immediate feedback when they misuse a component, and the library authors can evolve internals without breaking callers unnoticed.
Migrating an existing JavaScript codebase
Turning a maturing JS project into a maintainable one incrementally — renaming files to .ts, tightening compiler settings gradually, and typing the highest-churn, highest-risk modules first, all while shipping features throughout.
When TypeScript is the right choice
- Your codebase is large enough, or long-lived enough, that no single person holds all of it in their head — types become the shared, machine-checked memory of how the system fits together.
- You are refactoring aggressively or plan to, and you need the compiler to tell you every place a change ripples out to, rather than discovering it in production.
- You share data structures between the front end and back end — API responses, form payloads, event shapes — and want a single source of truth that flags a mismatch the moment either side drifts.
- You are onboarding engineers regularly; well-typed code is self-documenting in the editor, and new hires can explore an unfamiliar module through autocomplete instead of guesswork.
- You already have JavaScript in production and want to harden it incrementally, file by file, without a risky big-bang rewrite.
TypeScript: pros and cons
Strengths
- Catches a broad class of bugs at compile time, before they reach staging or production.
- Makes large-scale refactoring safe and fast, because the compiler finds every affected call site.
- Delivers first-class editor tooling — autocomplete, navigation, safe renames — that raises day-to-day productivity.
- Enables a single shared definition of your data shapes across front end and back end, killing an entire family of integration mismatches.
Trade-offs
- Adds a build/compile step and configuration surface that plain JavaScript does not require.
- Types are erased at runtime — they guarantee nothing about untrusted input, so you still need validation at your boundaries.
- The type system is powerful enough to invite “type gymnastics”: clever generic code that is hard to read, slow to compile, and worse than the problem it solved.
- There is a real learning curve for engineers new to structural typing, generics, and the compiler’s stricter modes.
How we structure a TypeScript codebase
We organise types around the domain, not around the framework. Core data shapes — the entities and events your business actually cares about — are defined once, in a shared location, and everything else derives from them. Utility types let us project one shape into another (a database row into an API response, an API response into a form model) without redeclaring fields, so a single change to the source type propagates everywhere it is used. The goal is one source of truth per concept and zero hand-maintained duplicates that can silently disagree.
At the edges of the system — HTTP handlers, message consumers, anything reading untrusted input — we do not trust types alone, because they are erased at runtime. We validate incoming data with a runtime schema library and derive the static type from that schema, so the compile-time type and the runtime check can never drift apart. Inside that validated boundary, the rest of the codebase can rely on types with confidence; outside it, we assume nothing. This “validate at the boundary, trust within” discipline is, in our experience, the single most important architectural habit for using TypeScript honestly.
Development performance: how types scale with the codebase
The performance that matters for TypeScript is not runtime — the compiled output is ordinary JavaScript and runs identically to hand-written JS. What scales is the human and tooling cost of working in a growing codebase. Here TypeScript trends the right way: as a project gets larger, plain JavaScript gets harder to change safely, while typed code holds its shape because the compiler carries the burden of remembering how everything connects. The type checker is, in effect, a regression suite for structure that you get for free on every keystroke.
The honest caveat is compiler performance itself. Type-checking a very large project takes real time, and pathological generic code can slow both the editor and the build to a crawl. We manage this deliberately: project references to break the codebase into independently checked units, incremental builds so only what changed is re-checked, and a firm rule against gratuitously clever types. We also separate transpilation from type-checking in the build — fast tools emit JavaScript quickly while the full type check runs in parallel and in CI — so a slow check never blocks a developer from running their code.
Where TypeScript helps security — and where it does not
It is important to be blunt: TypeScript is not a security feature, and the types are gone by the time your code runs. It will not stop SQL injection, will not sanitise user input, and will not protect an API from a malicious payload of the wrong shape. Anyone who treats a type annotation as a runtime guarantee has misunderstood the tool, and that misunderstanding causes real vulnerabilities. Every piece of data crossing a trust boundary must be validated at runtime, full stop.
Where it does help is indirectly but meaningfully. By eliminating whole categories of logic bugs — null dereferences, mismatched shapes, unhandled cases — it removes the kind of sloppy code paths that vulnerabilities often hide in. When we pair it with runtime schema validation at the edges, the static type and the runtime check reinforce each other: the schema rejects bad input, and the compiler ensures every downstream consumer handles the validated shape correctly. Types also make security-relevant invariants explicit — marking a value as, say, already-escaped or already-authenticated — so misuse becomes a compile error rather than a code-review gamble.
Scaling teams, not just servers
TypeScript’s clearest return is on team scalability. When several engineers change the same codebase concurrently, types act as enforced, always-current contracts between their work: I can change my module’s interface and know instantly which of your call sites I have broken, before either of us merges. This dramatically reduces the “works on my branch, breaks on main” friction that plagues large JavaScript teams and makes code review faster, because reviewers can trust the compiler to have already verified the mechanical correctness and focus on intent.
It also scales knowledge. In a big system, no individual understands every corner, and documentation rots. Types do not rot — they are checked on every build, so they cannot lie about the current shape of the code. A new engineer explores an unfamiliar area through autocomplete and go-to-definition, learning the real contracts rather than an out-of-date wiki page. The practical effect is that a team can keep adding people and modules to a TypeScript codebase for far longer before it collapses into the “nobody dares touch that file” state that eventually claims large untyped projects.
TypeScript integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work with TypeScript
We start strict and stay pragmatic. On a new project we enable strict mode from day one — retrofitting it later is far more painful than living within it from the start. On an existing JavaScript codebase we do the opposite: adopt loose settings first so the project compiles at all, then ratchet the compiler flags up module by module, typing the riskiest, highest-churn code first and leaving stable, low-value files for last or never. Either way the rule is the same: the type system serves the product, not the other way round.
We hold a hard line on readability. Types exist to make code easier to change safely, so a type that is harder to understand than the code it guards has failed at its job. We prefer inference over annotation, plain shapes over clever generics, and small named types over sprawling inline ones. And we always pair static types with runtime validation at system boundaries — the two together give genuine end-to-end safety, whereas either alone leaves a gap. This is the discipline we bring, and because we operate what we build, we live with the consequences of these choices ourselves.
The service behind it
Delivered throughCustom Software DevelopmentWhat we build with TypeScript
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use TypeScript in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Languages
Why teams choose us for TypeScript
We operate what we build
Because we run our own products in production, we feel the cost of a bad type decision directly — the 3am null-dereference, the migration that stalled, the generic nobody could read. That lived experience is why our advice on where to draw the line is practical rather than dogmatic.
Senior-led, not template-driven
A senior engineer owns the type architecture of your project. You are not getting boilerplate strictness applied by rote, but deliberate decisions about where types add safety and where they would only add friction, made by someone who has maintained large TypeScript codebases through years of change.
Honest about the trade-offs
We will tell you when TypeScript is the wrong call, when a migration is not worth it yet, and when a clever type should be deleted in favour of a simpler one. UK-registered and accountable, we optimise for the codebase you have to live with, not for looking sophisticated.
Typical timeline
- 01
Assessment
We review the codebase or plan, agree the target compiler strictness, and decide where runtime validation boundaries sit. For migrations we map the highest-risk modules to type first.
- 02
Foundation
We establish the build pipeline, shared type definitions, boundary-validation approach, and CI type-checking, so the safety net is in place before feature work leans on it.
- 03
Build or migrate
We type new features strictly from the outset, or convert existing files incrementally while continuing to ship — ratcheting compiler flags up as coverage grows.
- 04
Harden and hand over
We tighten remaining loose types, tune build and type-check performance, and document the conventions so your team can maintain the discipline after we step back.
How pricing works
- Adopting TypeScript is mostly an investment of engineering time rather than licensing cost — the compiler and tooling are open source and free. The real spend is the upfront effort of configuration, typing existing code, and bringing a team up to fluency. For a greenfield project that cost is marginal, folded into normal development. For a migration it is a deliberate line item, which is why we scope it as an incremental programme with visible milestones rather than an open-ended rewrite.
- We price TypeScript work as part of the engagement it belongs to — a new build, a platform hardening, or a targeted migration — and we are candid about where types earn their keep. If a piece of work is a short-lived prototype, we will tell you the type overhead may not pay off. If it is a system you intend to run and grow for years, the investment reliably pays back in reduced defects, faster onboarding, and cheaper change. We would rather set that expectation honestly at the start than bill you for type work that does not serve the outcome.
Hire TypeScript engineers
Need TypeScript capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire TypeScript engineersCommon questions
Does TypeScript make my application faster at runtime?
No, and any claim otherwise is a misunderstanding. TypeScript compiles down to plain JavaScript, and the types are erased entirely before your code runs — the runtime performance is identical to equivalent hand-written JavaScript. Its value is in development: fewer bugs, safer refactoring, and better tooling. If you need runtime speed, that is an architecture and profiling question, not a language-choice one.
Can I add TypeScript to an existing JavaScript project without a full rewrite?
Yes — this is one of its best properties. TypeScript is designed for gradual adoption: it will happily compile a mix of typed and untyped files, so you can convert one module at a time and tighten the compiler settings incrementally. We typically start with permissive settings so everything builds, then type the highest-risk, most-changed code first and ratchet strictness up over time, all while continuing to ship features. A big-bang rewrite is almost never the right approach.
If types are erased at runtime, do I still need to validate input?
Absolutely, and this is the most important thing to understand about the tool. Types exist only at compile time; at runtime your program is plain JavaScript with no knowledge of them. Data arriving from an API, a form, a queue, or a database can be any shape at all, and TypeScript will not check it for you. We validate everything crossing a trust boundary with a runtime schema and derive the static type from that schema, so compile-time and runtime stay in lockstep.
Isn’t TypeScript just slower to write than JavaScript?
There is a modest upfront cost — annotations to write, a compiler to satisfy, concepts to learn — and on a tiny throwaway script it may not be worth paying. But the calculation flips quickly on anything real. The time you spend satisfying the compiler is time you would otherwise spend debugging the same mistakes later, when they are far more expensive to find. On a codebase that gets maintained and refactored, TypeScript makes you faster overall, not slower, mainly by making change safe.
How do you stop TypeScript types from becoming an unreadable mess?
By treating readability as a hard requirement, not a nice-to-have. The type system is powerful enough to express extraordinarily clever things, and that power is a trap — a type harder to understand than the code it protects has failed. We lean on inference rather than over-annotating, prefer simple named shapes to sprawling generic contortions, and apply a plain test: can a mid-level engineer read and change this in six months? If not, we simplify it, even at the cost of a little repetition.
Building on TypeScript?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.