Languages
TypeScript Development Company
A type system that turns a whole category of production incidents into red underlines you fix before lunch, and that is honest about the ones it cannot touch.
Overview
TypeScript is JavaScript with a static type layer bolted on top, checked when you compile and then erased completely. The code that actually runs in a browser or a Node process is ordinary JavaScript with the annotations stripped out. That one design decision explains nearly everything about how it behaves in practice. It is exceptionally good at catching mistakes while you are writing, and it makes precisely no promises about what happens once the process is running and real data arrives from somewhere you do not control. Teams that understand that boundary get enormous value from types. Teams that do not either underuse them or, worse, trust them to guard the front door.
The reason to adopt it has very little to do with type annotations being pleasant to write and everything to do with what it does to a codebase over time. Untyped JavaScript degrades in a specific way: as it grows, the knowledge of how the pieces connect drifts out of the code and into the heads of the people who wrote it. When those people leave, that knowledge leaves with them, and what remains is a codebase where nobody is quite sure what shape a function receives, what a field can be, or whether a rename is safe. Types stop that drift. They are machine-checked documentation that cannot go stale, because a lie is a build failure.
We work in TypeScript across the whole stack: Node and serverless services, browser applications and their component libraries, build and deployment tooling, database access layers, infrastructure scripts, and the sharp edges in between. We have taken it on greenfield projects where strict mode was on from the first commit, and we have taken it into mature JavaScript codebases file by file while the team kept shipping features every week. The second is harder, more common and, done properly, entirely undramatic.
Our view is unromantic. On a throwaway script or a two-week prototype that will genuinely be deleted, types are overhead you do not need and we will say so. On anything that has to be maintained, modified by people who did not write it, or refactored under time pressure, the compiler becomes the most reliable contributor on the project. It never gets tired at half past five, never forgets the edge case, and never approves a rename that quietly 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 that TypeScript is best practice and reasonably wants to know what that actually buys.
Best for: Non-trivial, long-lived applications and services where correctness, safe change and the ability to hand the codebase to somebody else matter more than the fastest possible first commit.
Why teams choose TypeScript
Defects caught in the editor rather than in production
A very large share of everyday JavaScript failures are type errors in disguise: reading a property off something that turned out to be null, passing a string where a number was expected, calling a function that was undefined because an import was wrong, forgetting a branch in a switch after adding a new case. TypeScript surfaces those as you type, seconds after you make the mistake, rather than as a support ticket three weeks later attached to a stack trace nobody can reproduce.
Refactoring you can genuinely trust
The real payoff arrives the day something central has to change. Rename a field, tighten a signature, split a module, change a status from a string to a union of specific values, and the compiler walks every dependent line and lists exactly what broke. On a large codebase this converts the most frightening kind of change into the most boring, which is precisely what you want, because the alternative is a team that avoids necessary changes and lets the design rot around a decision nobody dares revisit.
Editor tooling that pays back daily
Because shapes are known statically, editors give accurate autocomplete, reliable go-to-definition, inline documentation at the call site and safe project-wide renames. Engineers spend far more of their day reading and navigating code than writing it, so this is not a cosmetic nicety. It is a compounding improvement to the most common activity on the project.
Contracts that cannot silently drift apart
When two parts of a system share a type definition, they cannot disagree without somebody being told at build time. Change the shape on one side and the other side stops compiling. That removes the whole category of failure where two teams each believed the field was optional, or where the API started returning null for something the client had always assumed was present.
Why businesses choose TypeScript
- It is JavaScript with guardrails rather than a different language. Every library, every pattern and every hire transfers, and you can adopt it one file at a time instead of committing to a rewrite.
- The benefit compounds with size and age. The bigger and longer-lived the codebase, the more the compiler pays you back, which is exactly the regime where mistakes are most expensive to make and hardest to find.
- It keeps a codebase changeable. Systems do not usually fail because of one bad decision, they fail because nobody could safely undo the bad decisions as they accumulated. Types are what make undoing them tractable.
- It makes handover realistic. A well-typed codebase can be given to another team, or picked up after six months away, without an archaeology phase, because the contracts are in the code and checked on every build.
- It is backed by a large engineering organisation with a stable, predictable release cadence, an enormous community, and no realistic prospect 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 compatibility by shape rather than by name. If an object has the properties a function needs, it fits, regardless of which class or interface it was declared from. This maps onto how JavaScript objects genuinely behave and makes it painless to describe the loose, duck-typed data that flows through real applications, rather than forcing an inheritance hierarchy onto data that never had one.
Inference over annotation
You do not annotate everything, and code that does is usually worse. The compiler infers most types from how values are used, so idiomatic TypeScript reads much like clean JavaScript with annotations only where they earn their place: at function boundaries, at public interfaces and where an explicit type documents an intention that inference would not capture. Over-annotating adds noise and, worse, freezes a shape that should have been derived.
Discriminated unions and exhaustiveness
Model a value as one of several specific shapes, each tagged with a literal field, and the compiler forces you to handle every variant. Add a new case to the union and every switch statement that forgot it lights up red immediately. This is the highest-value pattern in the language for modelling application state, API results, domain events and anything with a status field, and it replaces whole categories of defensive checks with a design the compiler enforces.
Strict mode and null safety
With strict settings on, null and undefined become distinct, tracked types instead of silent landmines that can inhabit any value. The compiler refuses to let you dereference something that might be absent until you have dealt with that possibility. This alone closes off the single most common runtime crash in all of JavaScript, and it is the reason we turn strict mode on from the first commit on any new project.
Generics, used with restraint
Generics let one implementation serve many types while preserving the caller’s exact types: a cache, a result wrapper, a repository, a typed API client. Used carefully they remove real duplication. Used without restraint they are the main source of unreadable type code in the ecosystem. Our deciding test is plain: can a competent engineer who did not write this read it in six months and change it confidently? If not, we simplify, even at the cost of a little repetition.
Utility and mapped types
Derive one shape from another rather than declaring it twice. A creation payload is the entity without its generated fields. A patch type is the same entity with everything optional. A public view of a record is the record with the sensitive fields removed. Deriving means a change to the source shape propagates automatically, whereas hand-maintained duplicates are two definitions that will eventually disagree without anyone noticing.
Literal types, const assertions and satisfies
Narrow a value to exactly the strings it can be, freeze a configuration object so its literal shape survives inference, and use satisfies to check an object against a contract without widening it and losing the specific keys. These are small, unglamorous tools that turn stringly-typed configuration and route tables into things the compiler understands and can check, which is where a surprising number of production mistakes live.
Branded types for domain invariants
A user identifier and an order identifier are both strings, and nothing stops you passing one where the other belongs until you give them distinct nominal identities. The same technique marks a value as already validated, already escaped or already authorised, so misuse becomes a compile error rather than something a reviewer has to spot. It is a small amount of ceremony that encodes a rule which would otherwise live only in a comment.
Declaration files and the ambient ecosystem
Libraries ship type definitions, either bundled or through community-maintained packages, so third-party code participates in the same safety net as yours. Where a dependency is untyped, or typed incorrectly, we write our own declarations for the surface we actually use rather than reaching for any and losing checking for everything downstream of it.
Compiler configuration and project references
The tsconfig is a real piece of engineering, not boilerplate: strictness flags, module resolution matched to your runtime and bundler, path mapping that stays consistent with what the build actually does, and project references that split a large repository into independently checked units. Getting this right is what keeps type-checking fast and the editor responsive as the codebase grows, and getting it wrong is why some projects feel slow to work in for reasons the team cannot articulate.
Use cases
Full-stack applications with shared contracts
A front end and a Node back end sharing one definition per request and response shape. Change the server and the client fails to compile until it is updated. The failure mode where the two sides drifted apart and nobody noticed until a user hit it simply stops occurring, which is worth more than any individual feature the types provide.
Long-lived business platforms
Internal systems and SaaS products that run for years and pass through many hands. Types encode the domain rules directly into the code, so an engineer arriving in year four can change things safely without having lived through the arguments of year one. This is the case where the return on types is largest and most obvious in hindsight.
Design systems and published libraries
Component libraries, SDKs and shared packages consumed by other teams, where the public type surface is the contract and the documentation at once. Consumers get precise autocomplete and immediate feedback when they misuse something, and the authors can evolve internals confidently because the compiler shows exactly what is part of the promise and what is not.
Migrating a maturing JavaScript codebase
Turning a project that grew faster than its discipline into one that can be maintained, by renaming files a module at a time, typing the highest-churn and highest-risk code first, and ratcheting compiler strictness up as coverage grows, all while continuing to ship features. This is a large part of the TypeScript work we do and it is a programme rather than an event.
Monorepos and multi-package estates
Several applications and shared packages in one repository, where project references keep type-checking fast and a shared types package keeps every consumer honest. The engineering value here is that a breaking change in a shared package surfaces at build time in every dependent, rather than in whichever service happens to deploy next.
Services with heavy external integration
Systems that talk to many third-party APIs, queues and webhooks, where the types describing external payloads are paired with runtime validation at every entry point. Typing the boundary properly is what turns a fragile integration layer into one that fails loudly and specifically instead of propagating a malformed record three services deep.
Build, deployment and infrastructure tooling
Scripts and pipelines that nobody thinks of as production code until the day one of them silently does the wrong thing. Typing this layer is cheap and pays for itself the first time the compiler catches a misspelled environment variable key or a configuration object missing a required field.
When TypeScript is the right choice
- Right when the codebase is large enough, or will live long enough, that no single person holds all of it in their head. At that point types stop being a style preference and start being the shared, machine-checked memory of how the system fits together.
- Right when you refactor, or want to be able to. The compiler enumerates every place a change ripples out to, turning a nerve-wracking exercise into a mechanical one: change the definition, fix everything the compiler lists, ship. Without that, teams stop refactoring, and code that is never refactored is code that gets steadily worse.
- Right when data structures cross a boundary between two pieces of code you own. An API response consumed by a front end, an event published to a queue and consumed by three services, a form payload, a database row mapped into a domain object. One definition, checked on both sides, removes an entire family of integration bugs that would otherwise be found by a user.
- Right when people join the team regularly. Well-typed code is explorable in the editor: autocomplete tells a new engineer what a thing has, go-to-definition tells them where it came from, and neither can be out of date. That is a faster and more accurate onboarding tool than any document you will write.
- Right when you already have JavaScript in production and want to harden it without a rewrite. Gradual adoption is a first-class feature rather than a grudging concession, and a mixed codebase compiles perfectly happily while you work through it.
- Wrong for a genuinely throwaway script, a spike you intend to delete, or a one-page tool for one person. The setup cost is small but not zero, and if nothing will ever be maintained then nothing needs protecting. Use plain JavaScript and move on.
- Wrong if what you actually want is runtime guarantees. If the requirement is that bad data cannot enter the system, types will not deliver it, because they are gone before your code runs. That job belongs to runtime validation, and if the correctness requirements are severe enough, it may belong to a language with a stronger compile-time story such as Rust or Go rather than to a checker layered over a dynamic language. We would rather tell you that than sell you a false sense of safety.
TypeScript: pros and cons
Strengths
- Catches a broad and well-understood class of defects at compile time, before code reaches review, staging or a user.
- Makes large-scale refactoring safe and quick, which keeps a codebase capable of change rather than gradually freezing.
- Delivers genuinely first-class editor tooling: accurate completion, navigation and rename across the entire project.
- Provides one shared definition of your data shapes across services and surfaces, eliminating a family of integration mismatches.
- Gradual adoption is real. Typed and untyped files coexist, so a mature JavaScript codebase can be migrated incrementally while continuing to ship.
- It is JavaScript underneath, so every library, pattern, tool and hire transfers directly, and the compiled output has no runtime cost or dependency of its own.
- The ecosystem has effectively settled on it: major frameworks and most serious libraries ship their own type definitions, so "does it work with TypeScript" is rarely a question any more.
Trade-offs
- It adds a build step and a configuration surface that plain JavaScript does not have. The compiler options are numerous, some interact in non-obvious ways, and module resolution in particular is an area where a project can lose an afternoon to a configuration mismatch between the compiler, the bundler and the package manager. On a greenfield project this is an hour of setup. Dropped into an existing build pipeline with mixed module formats, it is occasionally a genuinely miserable day.
- Types are erased at runtime, and this is the single most important thing to understand about the tool. A declaration that a value is a number is a note to the compiler, not a check. Data arriving from an HTTP request, a queue, a database driver, a third-party SDK or a JSON file can be any shape at all, and TypeScript will cheerfully let you treat it as whatever you claimed it was. The gap between the types you wrote and the values that actually flow through the system is where teams get hurt, and closing it takes runtime validation, which is separate work.
- The type system is powerful enough to invite showing off. Conditional types, deep recursion, elaborate mapped and template literal types and generics nested four levels deep can express extraordinary things, and the result is frequently a type nobody on the team can read, that produces an error message forty lines long, and that slows the editor to a crawl. A type that is harder to understand than the code it guards has failed at its job, and this failure mode is common enough that we treat readability as a hard constraint rather than a preference.
- The escape hatches undermine everything if they are used casually. One any at a boundary quietly disables checking for everything downstream of it, and an assertion with as tells the compiler to stop asking questions and is often simply wrong. These exist for good reasons and they are needed occasionally, but a codebase that reaches for them whenever the compiler complains has all the build friction of TypeScript and very little of the safety.
- Third-party types are not always correct. Community-maintained definitions can lag the library they describe, and definitions that are subtly wrong are more dangerous than no definitions at all, because they produce confident, false assurance. Occasionally the honest fix is to write your own declarations for a dependency, which is work nobody plans for.
- Type-checking a very large project takes real time, and the editor experience degrades with it. Pathological generics, enormous union types and sprawling barrel files that force the compiler to load half the repository to answer a question all make it worse. This is manageable with project references, incremental builds and discipline, but on a big monorepo it is an ongoing engineering concern rather than something you configure once.
- There is a genuine learning curve. Structural typing, generics, variance, discriminated unions and the stricter compiler modes take experienced JavaScript engineers a while to become fluent in, and during that period productivity dips and the temptation to reach for any is at its highest. Budget for it honestly rather than assuming a strong JavaScript team is instantly a strong TypeScript team.
- It does nothing at all for runtime performance. The output is the same JavaScript you would have written, and anyone expecting a speed improvement has misunderstood the tool. If runtime performance is the problem, this is not the solution to it.
How we structure a TypeScript codebase
We organise types around the domain rather than around the framework. The core shapes, meaning the entities and events the business actually cares about, are defined once in a shared location, and everything else is derived from them: the creation payload, the update patch, the public projection, the database row mapping. One source of truth per concept, and no hand-maintained duplicates that can silently disagree. When a field changes, it changes in one place and the compiler finds the rest.
At the edges of the system we do not trust types at all, because there is nothing there to trust. Every HTTP handler, queue consumer, webhook receiver, form submission and configuration file is untrusted input, and we validate it at runtime with a schema, deriving the static type from that schema so the compile-time shape and the runtime check are the same object and cannot drift. Inside that validated boundary, the rest of the codebase relies 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, and its absence is the most common reason a well-typed codebase still fails in production.
We keep module boundaries deliberate. A package or module exposes a narrow, explicitly typed public surface and keeps its internals to itself, which means the compiler can tell you what is a breaking change and what is not. We avoid sprawling barrel files that re-export everything from everywhere, because they blur ownership, create import cycles and make the compiler load far more than it needed to answer a simple question. Where types are shared between applications, they live in a package with a real name and a real owner rather than in a folder that everybody reaches into.
Finally, we prefer generated types over hand-written ones wherever a source of truth already exists. Database schemas, API specifications and query builders can all produce types mechanically, and a generated type cannot drift from the thing it describes. Hand-writing an interface to mirror a table is a duplicate waiting to go stale, and stale types are worse than no types because people believe them.
Development performance and how types scale with the codebase
The performance that matters here is not runtime. The compiled output is ordinary JavaScript and runs identically to the equivalent hand-written code, because the types are gone before execution. What scales is the human and tooling cost of working in a codebase as it grows, and this is where TypeScript trends the right way. Untyped JavaScript gets harder to change safely as it gets larger, because the knowledge of how things connect lives outside the code. Typed code holds its shape, because the compiler carries the burden of remembering how everything connects and rechecks it on every keystroke.
The honest caveat is compiler performance itself. Type-checking a large project takes real time, and the editor uses the same machinery, so a slow project is not just a slow build, it is a laggy autocomplete and a delay before an error appears. The usual culprits are identifiable: excessively clever generic code, very large union types, deep conditional types, and barrel files that force the compiler to pull in the world. We manage this actively with project references so the repository is checked in independent units, incremental builds so only what changed is rechecked, and a firm rule against gratuitous type gymnastics.
We also separate transpilation from type-checking in the build pipeline. A fast bundler or transpiler strips types and emits JavaScript almost instantly, so developers are never waiting on the type checker to run their code, while the full check runs in parallel locally and as a required step in continuous integration. That arrangement gives you the fast feedback loop of plain JavaScript during development and the full guarantee before anything merges, which is the right way round. A type check that is slow enough to be skipped is a type check that will be skipped.
Where TypeScript helps security, and where it does not
It is worth being blunt: TypeScript is not a security control, and the types are gone by the time your code runs. It will not stop SQL injection, will not sanitise user input, will not prevent cross-site scripting and will not protect an endpoint from a payload of the wrong shape. Anyone treating an annotation as a runtime guarantee has misunderstood the tool, and that particular misunderstanding produces real vulnerabilities, because it creates confidence where there is no check. Everything crossing a trust boundary must be validated at runtime, without exception.
Where it helps is indirect but genuine. By eliminating whole categories of logic error, null dereferences, mismatched shapes, unhandled cases, forgotten branches, it removes the kind of sloppy code path where security bugs tend to hide. Paired with runtime schema validation at the edges, the two reinforce each other: the schema rejects malformed input at the door, and the compiler then ensures every downstream consumer handles the validated shape correctly, including the case it would otherwise have forgotten. Neither alone is sufficient. Together they are close to what people imagine types give them on their own.
Types can also encode security-relevant invariants explicitly. Marking a value as already escaped, already authorised or already validated with a distinct nominal type turns misuse into a compile error rather than something a reviewer has to notice at half past five on a Friday. Conversely, an assertion that silences the compiler is exactly the place a security assumption goes unrecorded, which is why we treat every use of an escape hatch as something that needs a reason next to it. And the type packages themselves are dependencies like any other, part of the supply chain, worth locking and scanning along with everything else.
Scaling teams, not 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. One engineer can change a module’s interface and know immediately which of another’s call sites they have broken, before either of them merges rather than after both have deployed. That dramatically reduces the friction of parallel work and it makes code review faster and better, because the reviewer can trust the compiler to have verified the mechanical correctness and spend their attention on intent, edge cases and design.
It also scales knowledge, which is the constraint people underestimate. In a large system no individual understands every corner, and written documentation rots quietly because nothing forces it to stay true. 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 and learns the real contracts, not last year’s intentions. This is the difference between a codebase that can absorb new people and one where onboarding depends on a specific person having time to explain it.
The practical effect is that a team can keep adding people and modules to a TypeScript codebase for considerably longer before it reaches the state that eventually claims large untyped projects, the one where a particular file has become untouchable because nobody can predict what depends on it. That state is not inevitable, but avoiding it requires that something checks the connections continuously, and a compiler is far better at that than a code review culture, however diligent.
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, strict mode is on from the first commit, because retrofitting strictness later is far more painful than living inside it from the beginning: every unchecked assumption you allow in month one is a file you have to revisit in month nine. On an existing JavaScript codebase we do exactly the opposite. We start with permissive settings so the project compiles at all, then ratchet the compiler flags upward module by module, typing the highest-churn and highest-risk code first and leaving stable, rarely-touched files for last or never. Either way the rule is the same: the type system serves the product, not the reverse.
For a migration we work in a visible sequence rather than an open-ended effort. Get the build in place with checking permissive. Type the shared domain shapes first, because everything else derives from them. Then work outward through the modules that change most often, since those are where safety pays back fastest. Turn on one strictness flag at a time across the whole project rather than all of them at once, so each step produces a finite, fixable list of errors instead of thousands. Throughout, feature work continues, because a migration that stops delivery is a migration that gets cancelled halfway.
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. We prefer inference to annotation, plain named shapes to clever generic machinery, and small composable types to sprawling inline ones. When a type needs a comment explaining what it does, that is usually the signal to write a simpler one. We also delete types that are not carrying weight, because unnecessary abstraction in the type layer costs the same as it does anywhere else.
And we always pair static types with runtime validation at system boundaries, deriving the type from the schema so there is only one definition. That combination gives genuine end to end safety, whereas either alone leaves a gap that people mistakenly believe is covered. This is the discipline we bring, and because we operate what we build, we are the ones who live with it when we get it wrong.
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
Weighing up TypeScript?
A short call with engineers who build in it and operate the result. If TypeScript is the wrong tool for what you are doing, we would rather tell you now than bill you later.
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 systems in production, we feel the cost of a bad type decision directly: the null dereference at three in the morning, the migration that stalled at sixty per cent, the generic that nobody could read and everybody worked around. That lived experience is why our advice about where to draw the line is practical rather than dogmatic.
Senior-led, not applied by rote
A senior engineer owns the type architecture of your project. You are not getting a template of strictness flags copied from a blog post, you are getting deliberate decisions about where types add safety and where they would only add friction, made by somebody who has maintained large TypeScript codebases through several years of change and several changes of team.
Migrations that finish
Most stalled TypeScript migrations we see failed for the same two reasons: they tried to do everything at once, and they stopped feature delivery. We sequence the work by where risk and change actually concentrate, keep the project compiling and shipping throughout, and turn strictness up in steps that each have a defined end.
Honest about the trade-offs
We will tell you when TypeScript is not worth it, when a migration should wait, and when a clever type should be deleted in favour of a simpler one. We optimise for the codebase you have to live with rather than for looking sophisticated in a code review.
Typical timeline
- 01
Assessment
We review the codebase or the plan, agree the target compiler strictness, decide where runtime validation boundaries sit, and for a migration map which modules to type first based on where change and risk actually concentrate.
- 02
Foundation
We establish the build pipeline with transpilation separated from type-checking, the shared domain type definitions, the boundary validation approach, and a required type-check step in continuous integration, so the safety net exists before anything leans on it.
- 03
Build or migrate
New features are typed strictly from the outset, or existing files are converted incrementally while the team keeps shipping, with compiler flags ratcheted up one at a time as coverage grows so each step is a finite piece of work.
- 04
Tighten
We remove the remaining escape hatches, replace hand-written types with generated ones where a source of truth exists, and tune compiler and editor performance with project references and incremental builds.
- 05
Hand over
We document the conventions, the strictness decisions and the reasoning behind them, and walk your team through them, so the discipline survives after we step back rather than eroding one exception at a time.
How pricing works
- Adopting TypeScript is an investment of engineering time rather than a licensing cost. The compiler and the tooling are open source and free. The real spend is configuration, typing existing code, and bringing a team to fluency. On a greenfield project that cost is marginal and folded into normal development. On a migration it is a deliberate line item, which is why we scope it as an incremental programme with visible milestones rather than as an open-ended effort with no defined end.
- Most engagements begin with a short paid assessment: we read the codebase, look at where change actually happens, and come back with a sequenced migration plan and an estimate, or with a recommendation not to bother if the code in question is stable and about to be replaced.
- Fixed-scope work suits a defined migration, a defined new build, or a specific piece of hardening such as establishing shared contracts between an API and its consumers. Monthly senior engagement suits ongoing product work where the typing discipline is simply part of how the code gets written rather than a separate project.
- We are candid about where types earn their keep. If a piece of work is a short-lived prototype, we will tell you the overhead may not pay back. If it is a system you intend to run and grow for years, the investment reliably returns in fewer defects, faster onboarding and cheaper change, and we would rather set that expectation at the start than bill you for type work that does not serve the outcome.
- Third-party costs are billed to your own accounts, unmarked-up. Hosting, continuous integration minutes, error tracking and any commercial tooling are contracts you hold at the vendor’s price. We take no margin on anybody else’s software.
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 of how it works. TypeScript compiles to plain JavaScript and the types are erased entirely before your code runs, so runtime performance is identical to equivalent hand-written JavaScript. Its value is in development: fewer defects, safe refactoring and far better tooling. If runtime speed is your problem, that is an architecture, algorithm and profiling question, and choosing a language will not answer it.
Can we add TypeScript to an existing JavaScript project without a rewrite?
Yes, and this is one of its best properties. It is designed for gradual adoption and will happily compile a codebase that is part typed and part not, so you convert one module at a time. We typically start with permissive settings so everything builds immediately, type the shared domain shapes, then work outward through the code that changes most often, turning strictness flags up one at a time so each step produces a finite list of errors rather than thousands at once. Feature delivery continues throughout. A big-bang rewrite is almost never the right approach and is the main reason migrations fail.
If types are erased at runtime, do we still need to validate input?
Absolutely, and this is the most important thing to understand about the tool. Types exist only while you are compiling. At runtime your program is plain JavaScript with no knowledge of them whatsoever. Data arriving from an API, a form, a queue, a webhook or a database can be any shape at all, and TypeScript will let you treat it as whatever you declared without checking a thing. We validate everything crossing a trust boundary with a runtime schema and derive the static type from that schema, so the compile-time shape and the runtime check are one definition and cannot drift apart.
Is TypeScript just slower to write than JavaScript?
There is a modest upfront cost, in annotations, in a compiler to satisfy and in concepts to learn, and on a tiny throwaway script it is not worth paying. The calculation flips quickly on anything real. Time spent satisfying the compiler is largely time you would otherwise have spent debugging the same mistakes later, when they are far more expensive to find and attached to a user complaint. On a codebase that gets maintained and refactored, TypeScript makes a team faster overall, mostly by making change safe rather than by making typing quicker.
How do you stop the types themselves becoming an unreadable mess?
By treating readability as a hard requirement rather than a preference. The type system can express extraordinarily clever things and that power is a trap: a type harder to understand than the code it protects has failed, however impressive it is. We lean on inference instead of over-annotating, prefer simple named shapes to sprawling generic contortions, keep conditional and recursive types to the few places that genuinely need them, and apply a plain test: can a competent engineer read and change this in six months without help? If not, we simplify, even at the cost of some repetition. Repetition is cheaper than incomprehension.
What about the any keyword? Is it ever acceptable?
Occasionally, and it should always be deliberate and visible. The problem with any is not that it is imprecise, it is that it is contagious: it disables checking for everything downstream of it, so one any at a boundary can silently remove the guarantee from a large part of your call graph. Where a value genuinely is not known, unknown is almost always the better choice, because it forces you to narrow it before use. We lint against unmarked any, require a reason next to the ones that remain, and treat a rising count of assertions as a signal that something in the design needs attention rather than as noise to suppress.
Should we use TypeScript on the back end as well as the front end?
If your back end is already JavaScript, yes, without hesitation, and the shared contracts between the two are where the largest single benefit sits: one definition of every request and response, checked on both sides, so drift becomes a build failure instead of a production incident. If your back end is in another language, the argument is narrower and rests on the back end alone. In that case the interesting question is how you keep the client types honest, and the answer is usually generating them from an API specification rather than maintaining them by hand.
How long does a migration to TypeScript take?
It depends almost entirely on the size of the codebase, how consistently it was written and how strict you want to end up, so any number given before reading the code is fiction. What we can say is how it should be shaped. The build and the first strict modules are usually in place quickly. Meaningful coverage of the code that changes often follows over weeks. Full strictness across a large mature codebase is a programme measured in months, run alongside normal delivery rather than instead of it. We assess first and give you a sequenced plan with milestones, so you can stop at whatever point the remaining work stops being worth it, which is a perfectly legitimate outcome.
Which parts of our code will you not bother typing?
Anything genuinely disposable, and anything stable enough that nobody has touched it in years and nobody plans to. The return on typing a module is proportional to how often it changes and how much damage a mistake in it does. A file that has been untouched since it was written and is on its way to being replaced is a poor investment, and we would rather spend the same effort on the code your team edits every week. Being selective is what makes a migration finish.
Building on TypeScript?
Tell us what you are building and where it is stuck. A senior engineer reads it and gives you an honest read on whether TypeScript is the right fit for the problem, or what we would reach for instead.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.