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Where ThinkerWave fits next to the tools you already use.

You have good tools. This isn't a replacement for any of them, and anyone who tells you their product replaces your whole stack is selling you something. Open a row below to jump to the full comparison.

The strongest examples of AI that actually does work rather than describing it — reading your codebase, running tests, iterating until it passes. Brilliant on software, where the tests decide what's correct. The closest neighbour, and the one worth getting right first.

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Compare · Claude Code & Cursor

Both work autonomously on your machine. They're built for different kinds of correctness.

What Claude Code and Cursor are genuinely great at

They're the strongest examples of AI that actually does work rather than describing it. They read your codebase, make changes across many files, run commands and tests, and keep iterating until the thing they built passes. They run locally, on your machine, with your credentials. If you write software, they're excellent, and ThinkerWave doesn't compete with them.

Where it stops — and this isn't a flaw, it's the design

Their power comes from a fast, hard ground truth: the code compiles or it doesn't, the tests pass or fail. That loop is why they can be trusted to work unsupervised. The problems ThinkerWave is built for have no such loop. There's no test suite for "is this the right credit policy," "is this candidate worth two more years," "is this reserve adequate." Nothing external tells you the answer was wrong until it's expensive.

Works from genuinely different approaches.

It discards its own assumptions and tries again, because there's no compiler to say the first framing was wrong.

Works out the criteria for a good answer.

The equivalent of writing the test suite that didn't exist — including criteria nobody specified. On one problem it went from 3 given criteria to 15.

Builds its own checks and runs its output against them.

On a logistics problem, one version reported zero conflicts; the next built a check, found 258 the first had missed, and corrected itself with no human involved.

Use Claude Code or Cursor when	Use ThinkerWave when
The work is software	The work is a decision
Correctness is executable — tests, types, a compiler	Correctness is a judgment nobody has fully written down
You know what "done" looks like	Working out what "good" means is the hard part
You can review the diff	You couldn't verify the answer in an afternoon if you tried
Being wrong costs a rerun	Being wrong costs a write-off

Most teams use both · One does the work where the machine can check itself against a compiler. The other does it where the machine has to construct the check first. One thing we won't claim: running locally is not our differentiator. These tools run locally too.

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Compare · ChatGPT & Claude

Use them for the work that's easy to check. This is for the work that isn't.

What ChatGPT and Claude are genuinely great at

Extraordinary general tools — drafting, explaining, summarising, coding, thinking out loud with you. Fast, cheap, and available to everyone in your company today. Most AI value in most organisations comes from exactly this, and it should.

Where it stops

Everything they produce arrives with the same confident tone, whether it's right or wrong. On a summary, that's fine — you'll spot a mistake in seconds. On a portfolio review, a reserving assumption or a candidate shortlist, you won't spot it for months. They give one answer, from one framing, with the criteria you happened to supply.

Many approaches instead of one.

It works the problem from more than one framing, rather than committing to the first one that sounds right.

Criteria it works out, not ones you had to specify.

You don't need to know which criteria matter in advance — it surfaces them as it works.

It checks its own output, and knows when to stop.

It will tell you when it can't stand behind an answer, rather than hand you a confident guess. A chat assistant won't do that.

Use ChatGPT or Claude when	Use ThinkerWave when
You can check the answer in under a minute	Verifying would take a week
The task is defined	The framing itself is the hard part
Speed matters most	Being right matters most
You want a draft	You want a decision that survives scrutiny

Most teams use both · And the ratio should be lopsided: chat AI for the hundred easy things a day, this for the handful of decisions a quarter that are worth being careful about.

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Compare · Agent frameworks

They execute the plan you specify. This works out what the problem requires.

What agent frameworks are genuinely great at

Frameworks like these turned LLM calls into real pipelines: tools, retries, memory, orchestration, observability. If you have a repeatable process, they're the right way to build it, and building it in-house gives you control a vendor can't.

Where it stops

A framework runs the plan you designed, with the tools you registered, against the success criteria you defined. That's a strength when the process is known. It's the wrong shape entirely when nobody knows the right process yet — it will execute your first idea faithfully, and report success against criteria that were incomplete from the start.

Treats the framing as the problem itself.

Several approaches are worked in parallel rather than executing one plan, and its own assumptions get discarded along the way.

Derives the criteria as it goes.

Instead of running against the success criteria it was handed, it works out which ones actually matter.

Verifies against its own output, not a checklist.

Completion isn't the finish line — it checks the result the way a person double-checking their own work would.

Use a framework when	Use ThinkerWave when
The process is known and repeatable	The process is what you're trying to work out
You want control over each step	You want the problem worked, not the steps run
Volume matters — thousands of runs	Stakes matter — a handful of expensive decisions
Success is task completion	Success is an answer that holds up

Most teams use both · Frameworks for the pipeline work that runs every day. This for the problems nobody has a pipeline for.

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Compare · Your own models, optimisers & BI

They answer the question they were built for. This is for when that's the wrong question.

What your own models are genuinely great at

A well-built scorecard, solver or dashboard is precise, auditable, fast and defensible — and usually better than any general AI at the specific job it was designed for. They're the backbone of every serious risk, pricing and operations function, and nothing here suggests replacing them.

Where it stops

A model can only be wrong in the ways it was built to be right. An optimiser returns a solution to the model it was given — constraint conflicts included. A dashboard shows the cut somebody chose in advance. When the failure is in the framing, none of them can tell you, because none of them is looking.

Comes at it from framings your model wasn't built around.

A challenge from outside the assumptions baked into the original model.

Surfaces criteria that weren't in the specification.

The measures nobody wrote into the original brief, but that decide the answer anyway.

Checks its own conclusion against your data.

Before reporting it — including saying plainly where the evidence is thin.

Use your model when	Use ThinkerWave when
The question is stable and well-specified	The question itself is in doubt
You need a repeatable, auditable number	You need to know what the number is missing
Regulation requires that specific model	You're challenging that model before validation
It runs a thousand times a day	It's a decision you make four times a year

Most teams use both · The best use of this is as an independent challenge to a model you already trust — worked from angles the model was never built to see.

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Compare · A consulting engagement

Good consultants bring judgment. So does this, at a different speed and price.

What a strong consulting firm is genuinely great at

Pattern recognition across dozens of comparable situations, senior people who've seen the failure modes, and — often the real product — an independent view your organisation will actually act on. That is worth a lot, and it isn't going away.

Where it stops

It's expensive, it takes weeks to months, and the reasoning leaves when the team does. The analysis usually explores the number of framings that fit inside the budget, and the working papers rarely come with a full record of what was considered and rejected.

Many angles, without the cost of each extra one.

Working an additional framing doesn't cost another week of someone's time.

Criteria made explicit.

Not left implicit in a senior person's head, where it can't be checked or challenged.

Leaves the entire trail.

What it tried, what it discarded, and why — exactly what you can't buy back after a project ends.

Use consultants when	Use ThinkerWave when
You need organisational buy-in from an outside name	You need the analysis itself
The problem needs people in rooms	The problem needs relentless, checked reasoning
Delivery includes change management	Delivery is an answer and its trail
Once, at scale	Repeatedly, whenever the question comes up

And sometimes both · For problems too complex to hand to any tool alone, the team that built ThinkerWave will work them alongside yours.

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"Every tool above is strong where the answer is checkable. ThinkerWave is built for the problems where you can't check the answer quickly, and being wrong is expensive."

The one line that separates all of them · See the product →

Put it on your hardest problem.

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