AI Coding Tools
Cursor
Cursor, the AI-native editor that knows your whole codebase — adopted with the review discipline that keeps it an asset, by a team that codes with it daily.
Overview
Cursor is an AI-first code editor built as a fork of VS Code. The distinction from other AI coding tools is where the intelligence lives: rather than bolting an assistant onto an existing IDE as an extension, Cursor is built around the model. It indexes your whole codebase so the AI has real project context, then exposes that context through a set of tightly-integrated affordances — inline edit with Cmd-K, a chat panel that can apply changes directly to files, Tab completions that predict multi-line edits rather than the next token, and an agent mode (Composer) that plans and makes changes across many files at once. Because it is a VS Code fork, you keep the familiar keybindings, settings and most of the extension ecosystem you already rely on.
It sits in a clear position between two other tools. Above autocomplete-in-your-existing-IDE, like GitHub Copilot, which suggests the next lines but does not reason over your project or edit across files. Below terminal-native agents, like Claude Code, which run as a headless agent driving your repository from the command line. Cursor is the middle ground: a full editor you sit inside, where the AI is native and codebase-aware, and where you stay in the loop reviewing each change as it lands. Model choice is part of the design — it can route to models across several providers rather than tying you to one — and behaviour can be steered per-project with rules files under .cursor/rules.
We do not sell Cursor and we are not its vendor. We use it daily on our own engineering, and we help teams adopt it productively. That word matters: the value of Cursor is not automatic. A team that drops it in without review discipline gets faster production of code nobody fully understands. A team that adopts it deliberately — with rules that encode house conventions, a review culture that treats AI edits as untrusted until read, and a clear sense of where the tool helps and where it hurts — gets a genuine step change in throughput. The difference is entirely in the adoption, and that is where our work is.
Best for — Established teams of engineers who will review what the AI produces, wanting codebase-aware AI inside a familiar full editor — adopted with the rules, review discipline and data-handling decisions that make it a net gain.
Why teams choose Cursor
Codebase-aware edits, not blind completions
Because Cursor indexes the whole project, its suggestions and agent edits are grounded in your actual code — your types, your helpers, your conventions — rather than generic snippets. That is the difference between an assistant that fits your codebase and one that keeps proposing patterns you then have to unpick.
A familiar editor, so adoption is cheap
As a VS Code fork, Cursor keeps the keybindings, settings and extensions your team already knows. Nobody has to learn a new editor to get the AI benefit, which removes most of the friction that kills tool adoption and lets the team focus on using the AI well rather than relearning the basics.
Multi-file work that would otherwise be tedious
Composer plans and applies changes across many files from a single instruction — the rename that touches thirty places, the migration that threads through several layers. Reviewed properly, it collapses hours of mechanical editing into a diff you read and approve, which is where a large part of the real time saving comes from.
Why businesses choose Cursor
- You want AI that understands your whole project, not just the file on screen, and you want it inside a full editor rather than a terminal agent or a completion-only plugin.
- Your team already lives in VS Code and you want the AI benefit without paying the cost of migrating everyone to a new tool.
- You do real multi-file work — refactors, migrations, feature threading — and want an agent that can plan and edit across the codebase while you review the result.
- You want to standardise how AI is used across a team, encoding your conventions in rules files rather than leaving each engineer to prompt in their own inconsistent way.
What we build with Cursor
The capabilities this technology is genuinely strong at — and what we most often build with it.
Whole-codebase indexing and context
Cursor builds an index of your repository so the model can retrieve relevant code as context for any request. This is what lets it answer questions about the project, and make edits that reference your existing types and helpers, instead of treating each file as an island. We help teams understand what gets indexed and configure it sensibly for their codebase.
Composer, the multi-file agent
Composer (agent mode) takes an instruction, plans the changes, and applies them across multiple files, showing you a diff to review and accept. It is the feature that separates Cursor from autocomplete — and the one that most needs review discipline, because a confidently wrong plan touches many files at once.
Inline edit with Cmd-K
Cmd-K lets you select code and describe a change in natural language, applied in place — rename this, extract that, handle this edge case. It keeps you in the flow of editing rather than context-switching to a chat panel, and is the tool of choice for small, localised, easily-verified changes.
Tab: predictive multi-line edits
Cursor’s Tab goes beyond next-token completion to predict multi-line edits — including changes at your cursor and the follow-on edits they imply elsewhere in the file. It anticipates the mechanical parts of an edit you have started, which is where a large share of the day-to-day speed-up quietly comes from.
Chat that applies changes
The chat panel is codebase-aware and can turn a discussion into applied edits with a click, so a conversation about how to fix something becomes the fix. We coach teams to treat those applied changes as proposals to be read, not answers to be trusted, before they hit the working tree.
Rules files and model choice
Rules under .cursor/rules steer the model’s behaviour per-project — coding standards, patterns to prefer, things never to do — so the AI’s output matches your house style consistently. Combined with the ability to choose models across providers, this gives a team a configured, repeatable workflow rather than ad-hoc prompting.
Use cases
Refactors and framework migrations
Large mechanical changes that thread through many files — renaming a concept, moving off a deprecated API, migrating a framework version. Composer proposes the whole change set and a human reviews the diff, turning a tedious multi-hour edit into a reviewable proposal.
Working in an unfamiliar codebase
Onboarding onto a large or inherited project, where Cursor’s indexing lets you ask how something works and where a change should go, grounded in the actual code. It shortens the time to a first useful contribution — provided the engineer verifies rather than trusts the answers.
Feature work with boilerplate
Building out a feature that spans a form, a handler, a type and a test, where much of the code is predictable plumbing. Tab and inline edit remove the mechanical typing while the engineer keeps design decisions and the tricky logic firmly in their own hands.
Standardising an AI workflow across a team
Rolling out a consistent, reviewed way of using AI across engineers — rules files encoding conventions, agreed practice on what the agent may do unreviewed, and a shared understanding of where the tool helps and where it must be kept on a short leash.
When Cursor is the right choice
- You have an established codebase and a team of competent engineers who will read and review what the AI produces. Cursor’s indexing turns whole-project context into edits that fit your existing code, and experienced engineers get the most from it because they can tell a good suggestion from a plausible-but-wrong one at a glance.
- You want AI assistance without leaving a full editor — you value the debugger, the extension ecosystem, the git integration and the muscle memory of VS Code, but want the AI to be native and codebase-aware rather than a sidebar that only sees the current file.
- You are doing substantial multi-file work — a refactor, a framework migration, wiring a feature through several layers — where Composer’s ability to plan and edit across files saves real time, provided a human reviews the diff before it is committed.
- You are standardising an AI workflow across a team and want to encode conventions once. Rules files let you steer the model’s behaviour per-project so everyone gets edits that match your patterns, rather than each engineer prompting from scratch.
- Wrong for: teams without the discipline or seniority to review AI output critically. Cursor makes it trivial to accept sweeping, confident edits that are subtly wrong; on juniors especially, heavy reliance can erode the fundamentals they need to catch those errors. If a team cannot or will not review carefully, Cursor accelerates the creation of problems, not the solving of them.
Cursor: pros and cons
Strengths
- The AI is native and codebase-aware: whole-project indexing means edits reflect your real code and conventions, not context-free autocomplete, and Composer can reason and act across many files at once.
- It is a VS Code fork, so the learning curve is near zero and most of the extension ecosystem, keybindings and settings carry straight over — adoption does not cost you a re-tooling project.
- Behaviour is steerable and portable via .cursor/rules files, so house conventions are encoded once and applied consistently across the team rather than living in each person’s head.
- Model choice across providers means you are not locked to one model’s strengths or pricing, and you can pick the model that suits a given task — reasoning-heavy work versus quick edits — inside the same editor.
Trade-offs
- The agent can make sweeping, confident edits that are subtly or badly wrong, and the ease of accepting them is itself the hazard. Cursor demands genuine review discipline; without it you get fast production of code nobody actually understands, which is worse than slow production of code you do.
- Indexing large or proprietary codebases raises real data-handling questions — code and context are processed to give the AI its awareness. Privacy mode and enterprise controls mitigate this, but the decision must be made deliberately for sensitive or regulated code, not assumed away.
- It is a VS Code fork, which means it can lag upstream VS Code releases and occasionally trip over extensions that expect the exact upstream build. For most teams this is minor; for teams pinned to bleeding-edge VS Code features or unusual extensions it is a real friction.
- It is a paid subscription with per-seat cost, and heavy reliance can erode juniors’ fundamentals — the ability to write and debug code unaided — if a team leans on it as a substitute for understanding rather than an accelerator on top of it.
How it fits your workflow
Cursor is a desktop editor, so architecturally the questions are about how it sits in your development workflow rather than how it scales a server. The core moving part is the codebase index: Cursor processes your repository to build the context the AI draws on, and understanding what that index contains, how it is refreshed, and what leaves your machine is the first thing we help a team get right. For most projects this is straightforward; for proprietary or regulated code it is a deliberate decision about privacy mode and enterprise controls, made before the tool is rolled out rather than discovered afterwards.
The second architectural piece is where Cursor sits relative to the other AI tools you might use. We treat it as one instrument in a set: Cursor for codebase-aware work inside a full editor, a completion-only tool where that is all you need, and a terminal agent for headless, scripted automation. The rules files under .cursor/rules are the configuration layer that makes Cursor’s behaviour repeatable across the team — checked into the repository like any other project convention, so the AI’s output is shaped by your standards rather than each engineer’s prompting habits.
Underpinning all of it is the review boundary. The single most important architectural decision is that AI-generated edits are untrusted until a human has read them, and that your existing gates — code review, CI, tests — sit downstream of Cursor exactly as they would for human-written code. Cursor changes how quickly code is produced; it must not change how carefully code is admitted. We design the adoption so the tool accelerates the writing without weakening the controls that keep the codebase sound.
Productivity, honestly measured
The performance that matters with Cursor is engineering throughput, and the honest picture is that it is real but conditional. On mechanical, well-understood work — boilerplate, refactors, edits in code the engineer already grasps — the speed-up is large and low-risk, because a wrong suggestion is obvious and cheap to reject. Tab and inline edit remove the typing; Composer removes the tedium of repetitive multi-file changes. This is where the tool pays for itself several times over.
The risk to throughput is the hidden cost of review debt. An edit accepted without being understood is a future bug and a future maintenance burden, and enough of them turn apparent speed into a slower, more fragile codebase. So the genuine performance win comes from using Cursor hardest exactly where verification is easy, keeping a human firmly in the loop where it is not, and resisting the temptation to accept a large confident diff because reading it is more effort than writing it would have been. Measured over a quarter rather than an afternoon, the teams that gain most are the ones that stayed disciplined.
Code privacy and data handling
The central security question with Cursor is that your code is processed to give the AI its context — indexing and requests involve sending code and surrounding context to models. For open or low-sensitivity code this is a non-issue; for proprietary, confidential or regulated code it is a decision that must be made explicitly. Cursor offers a privacy mode and enterprise controls that constrain how code is handled and retained, and we help teams choose and configure the right posture for the sensitivity of what they are working on, rather than leaving it on defaults nobody checked.
Beyond data handling, AI-generated code is an untrusted input to your codebase and must be treated as such. The same threats apply as to any code: it can introduce a vulnerability, mishandle a secret, or pull in a dependency that should not be there — stated with total confidence. Our answer is discipline, not faith: AI edits go through the same review, secret-scanning, dependency checks and CI as human-written code, secrets never sit in prompts or rules files, and the review boundary is non-negotiable. The tool writes code faster; it does not earn the right to skip the controls that keep code safe.
Scaling across a team
Scaling Cursor is an organisational question, not an infrastructure one: the challenge is rolling it across a team so it makes everyone more effective without quietly degrading the codebase or the engineers. The lever for this is shared, checked-in rules files that encode conventions once, so every engineer gets edits aligned to your standards rather than a dozen personal prompting styles producing inconsistent code. Agreed practice on what the agent may do unreviewed, and on where the tool must be kept on a short leash, is the other half of a consistent rollout.
The failure mode to design against as you scale is fundamentals erosion, particularly among juniors. A team that leans on Cursor as a substitute for understanding, rather than an accelerator on top of it, produces engineers who cannot debug what the AI cannot fix — and a codebase full of code its own authors do not really understand. We help teams scale adoption in a way that keeps review culture intact and keeps engineers growing: seniors reviewing AI-assisted work, juniors expected to understand what they accept, and the tool positioned as leverage for capable people rather than a crutch that hollows out the capability.
Cursor integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we help you adopt it
We start with an honest fit assessment: is your team’s seniority and review culture ready to get value from Cursor, and where in your work will it actually help versus where it will just accelerate mistakes. Then we handle the data-handling decision up front — what your code sensitivity demands of privacy mode and enterprise controls — because that is a governance choice, not a setting to stumble into after the tool is already indexing a proprietary repository.
From there the work is practical enablement. We set up rules files that encode your conventions, establish the review discipline that treats AI edits as untrusted until read, and coach the team on the tools that pay off — Tab and inline edit for verifiable mechanical work, Composer for reviewed multi-file changes — and on the traps to avoid. Because we use Cursor daily on our own engineering, the guidance is first-hand and blunt: where it genuinely speeds us up, where it has burned us, and how we keep it an asset rather than a liability. You get a team that adopts the tool with eyes open, not one that discovers its failure modes in production.
The service behind it
Delivered throughAI DevelopmentWhat we build with Cursor
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 Cursor in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in AI Coding Tools
Why teams choose us for Cursor
We use Cursor every day
This is not a tool we read about — we code with Cursor daily, so our advice on where it speeds us up and where it has caught us out is first-hand and honest, not repeated from its marketing.
We adopt it with review discipline
The value of Cursor is entirely in how it is adopted. We bring the review culture, rules and guardrails that turn it into a genuine throughput gain, rather than a faster way to ship code nobody understands.
Senior engineers who judge the output
Getting value from Cursor takes engineers who can tell a good suggestion from a confident wrong one. Ours are senior, so the AI is leverage on top of real judgement — not a substitute for the understanding that catches its mistakes.
Honest about the trade-offs
We will tell you plainly where Cursor helps, where it hurts, what it does to juniors’ fundamentals, and what indexing your code means for data handling — so you adopt it with eyes open rather than discovering the downsides later.
Typical timeline
- 01
Fit and data-handling assessment
A few days establishing whether Cursor suits your team and code, and deciding the privacy mode and enterprise-control posture before any proprietary code is indexed.
- 02
Setup and rules
Configuring the editor, writing the initial .cursor/rules that encode your conventions, and integrating Cursor with your existing review, CI and git workflow.
- 03
Team enablement
Working with your engineers on the review discipline and the features that pay off, so the tool is used well from the start rather than learned by trial and error in production.
- 04
Embed and review
Letting the practice settle, then reviewing how it is actually going — refining rules, correcting habits that have drifted, and confirming the tool is a net gain rather than accumulating review debt.
How pricing works
- A short adoption engagement — a fixed-scope assessment of whether Cursor suits your team, the data-handling posture your code demands, and a rollout plan with rules and review practice in place before people start relying on it.
- Hands-on team enablement — working alongside your engineers to establish rules files, review discipline and a consistent AI workflow, so adoption lands as a productivity gain rather than a scatter of individual habits.
- Ongoing senior engineering that uses Cursor as part of how we deliver, where you want the throughput benefit applied to real work with the review discipline already built into how we operate.
- Note that Cursor’s own per-seat subscription is a separate, ongoing cost paid to the vendor, distinct from our engineering or advisory fee.
Hire Cursor engineers
Need Cursor 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 Cursor engineersCommon questions
How is Cursor different from GitHub Copilot?
Copilot is autocomplete inside your existing IDE — it suggests the next lines but does not reason over your whole project or edit across files. Cursor is a full editor, a VS Code fork, where the AI is native and codebase-aware: it indexes your project, can answer questions about it, and can make multi-file changes through its agent. Copilot sits inside the editor you already have; Cursor replaces the editor to put the AI at the centre. If all you want is smarter completion, Copilot is lighter; if you want project-aware edits and an agent, Cursor is the stronger tool — at the cost of switching editors.
How does Cursor compare to Claude Code?
They occupy different positions. Claude Code is a terminal-native agent that drives your repository from the command line, headless — good for scripted, autonomous work and for engineers who live in the terminal. Cursor is a full editor you sit inside, where you review each AI change as it lands in the file. Cursor keeps you closer to the code with a visual diff and the editor’s tooling around you; a terminal agent gives more autonomy and fits automation better. Many teams use both, for different jobs. We use both ourselves and can help you decide which suits which part of your work.
Is our code safe if Cursor indexes it?
That is the right question to ask before rolling it out. Cursor processes your code to give the AI its context, so for proprietary or regulated code you must decide the data-handling posture deliberately. Cursor offers a privacy mode and enterprise controls that constrain how code is handled and retained, and we help you choose and configure the right setting for your code’s sensitivity before any of it is indexed — rather than leaving it on defaults nobody reviewed. For open or low-sensitivity code it is a non-issue; for confidential code it is a governance decision, not an afterthought.
Will Cursor make our juniors worse engineers?
It can, if it is used as a substitute for understanding rather than an accelerator on top of it. Heavy reliance can erode the fundamentals juniors need — the ability to write and debug code unaided, and to spot when an AI edit is confidently wrong. The way we adopt it protects against that: juniors are expected to understand what they accept, seniors review AI-assisted work, and the tool is positioned as leverage for capable people, not a crutch. Used with that discipline, it speeds up learning; used without it, it hollows out the capability your team depends on.
Since it is a VS Code fork, will our extensions and setup still work?
Mostly, yes — that is a real advantage. Cursor keeps VS Code’s keybindings, settings and most of its extension ecosystem, so the learning curve is near zero and adoption does not cost you a re-tooling project. The honest caveat is that, as a fork, Cursor can lag upstream VS Code releases and occasionally trip over an extension that expects the exact upstream build. For most teams this is minor; if you are pinned to bleeding-edge VS Code features or unusual extensions, it is worth checking before you commit, which is part of the fit assessment we run.
Building on Cursor?
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.