Industries

One engine. Your hardest problems, across every domain.

ThinkerWave isn't eight products. It's one system, pointed at the problems in your field that are complex, contested, and expensive to get wrong. Here's what that looks like where you work — tap a card to jump to the full picture.

Credit & Capital Markets

Your scorecards, your ECL model and your policy each answer the question they were designed for, and each answers it well. The losses come from the interaction nobody modelled: a concentration that only matters if a sector turns, a correlation that was stable until it wasn't, an exposure that looks fine at obligor level and dangerous at portfolio level.

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Portfolio Management

Allocation, hedging and thesis decisions are made under contested views where the evidence supports more than one answer. The expensive failure is conviction — settling on the framing that fits the house view and never seriously working the case against it.

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Cybersecurity

Threat modelling, detection-gap analysis and incident hypothesis work all involve reasoning about an adversary who is deliberately doing things your controls weren't designed to notice. The framing is the work, and the framing is what tooling doesn't give you.

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Drug Discovery & Medical Science

Potency, selectivity, tox, synthesisability, formulation, IP position and trial feasibility all pull against each other, and the weighting between them is a judgment nobody can fully write down at the start.

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Energy & Climate

Grid investment, asset resilience and transition planning require committing capital against scenarios that credible experts model differently. The failure mode is a plan optimised for one scenario and quietly fragile in the others.

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Manufacturing & Processes

Yield loss, quality escapes and capacity decisions turn on interactions between variables owned by different functions. Each function's analysis clears its own domain, and the problem lives in the space between them — the variables were all in tolerance, and the yield still dropped, and nobody could tell you why.

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Sovereign AI

Every serious institution wants the upside of frontier AI without handing its hardest reasoning to a platform it doesn't control and can't fully inspect. For governments, regulated industries, and any organisation where the decision itself is sensitive, the question isn't just whether the answer is good — it's who else saw the problem on the way to getting it.

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Government Policies

Policy appraisal, programme review and major procurement decisions rest on evidence that genuinely conflicts, with distributional effects that different framings weight differently. The decision has to be defensible in public, years later.

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Not on the list? The engine is the same one either way — tell us the problem. Request access → ( Talk to us )

Industries · Credit & Capital Markets

AI built for the credit risk that doesn't show up in any single model.

Your scorecards, ECL model and policy each answer the question they were designed for, and answer it well. The losses come from the interaction nobody modelled — a concentration that only matters if a sector turns, an exposure that's fine at obligor level and dangerous at portfolio level.

A model can only be wrong in the ways it was built to be right. A general AI will summarise your portfolio fluently, without knowing what it failed to consider.

Every angle, not one

Works the book from several framings at once — obligor, sector, vintage, funding.

Criteria you didn't write down

Surfaces the risk criteria that aren't in the policy document.

Checks its own conclusion

Checks its conclusion against your data and flags where the evidence is thin.

Point it at

An independent challenge to your own credit model, ahead of validation.
Portfolio concentration review across obligor, sector and geography at once.
ECL / IFRS 9 assumption review when the macro picture has moved.
Early-warning signal design for a segment that keeps surprising you.
Credit policy stress-testing under assumptions your committee disagrees on.

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Industries · Portfolio Management

AI built for the decisions where every option has a good story.

Backtests confirm the framing you chose. An LLM given your thesis will argue it well. Neither is built to attack it.

Argues against itself

Builds the strongest case against the position, not just for it.

Finds where it breaks

Surfaces the conditions under which the thesis breaks.

Checks its own reasoning

Checks its reasoning against the data before it commits to a view.

Point it at

Stress-testing a live investment thesis against its strongest counter-case.
Allocation under macro scenarios your team genuinely disagrees about.
Factor and crowding exposure that no single risk report shows.
Hedging structure comparison where the cost-benefit is contested.
Drawdown post-mortems: what framing was missing, not what number was wrong.

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Industries · Cybersecurity

AI built for defenders who aren't short of alerts — they're short of judgment.

Threat modelling, detection-gap analysis and incident hypothesis work all involve reasoning about an adversary deliberately doing things your controls weren't designed to notice.

Detections encode known behaviour. A general AI will summarise your telemetry confidently. Neither eliminates hypotheses about what you're not seeing.

Multiple adversary framings

Attacks a defensive problem from several adversary framings at once.

What actually matters here

Works out which control and detection criteria matter for your environment.

Checks before you fund it

Checks its conclusions before they turn into a roadmap you have to fund.

Point it at

Threat model review for a system nobody has re-examined in two years.
Detection-gap analysis against a specific adversary behaviour set.
Incident hypothesis generation and elimination during a hard investigation.
Control prioritisation when the budget covers a third of the backlog.
Third-party and supply-chain risk where the exposure spans vendors.

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Industries · Drug Discovery & Medical Science

AI built for the point where nobody agrees what "good" means.

Potency, selectivity, tox, synthesisability and trial feasibility all pull against each other, and the weighting between them is a judgment nobody can fully write down at the start.

A screening model optimises the objective it was given. An LLM will rank candidates fluently on the criteria you handed it — never telling you the list was incomplete.

Several scientific framings

Works the selection problem from several framings, not the one that produced the shortlist.

3 criteria to 15

Discovers criteria as it goes — on one problem it grew from 3 to 15.

Names the thin evidence

Checks its reasoning against the evidence and flags what's contradictory.

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Candidate prioritisation where the team is split.
Target selection under contested biological evidence.
Translational risk review before a costly next phase.
Trial design trade-offs where endpoints, power and feasibility conflict.
Resolving a literature contradiction that keeps stalling a programme.

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Industries · Energy & Climate

AI built for decade-long bets made on contested forecasts.

Grid investment, asset resilience and transition planning require committing capital against scenarios that credible experts model differently.

Scenario tools run the scenarios you specify. They don't tell you which scenario you should have specified, or where your plan breaks first.

No single central case

Works the decision across genuinely different scenario framings.

Resilience criteria, surfaced

Surfaces the resilience criteria that were never in the investment paper.

Says what's contested

States plainly where the evidence is contested rather than settled.

Point it at

Capital sequencing when the regulatory picture is still moving.
Asset resilience planning against physical risk with disputed forecasts.
Transition pathway comparison for a board that needs trade-offs explicit.
Outage and failure risk where causes interact across systems.
Demand and load planning under structurally uncertain electrification.

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Industries · Manufacturing & Processes

AI built for causes that only appear in combination.

Yield loss, quality escapes and capacity decisions turn on interactions between variables owned by different functions — the problem lives in the space between them.

SPC and root-cause tooling look where you point them. A model trained on past failures can't recognise a failure mode that hasn't happened yet.

Every framing at once

Works the problem across process, material, equipment and human framings.

The variables that decide it

Works out which variables actually decide the outcome.

Checked before you spend

Checks its explanation against the evidence before you act on it.

Point it at

Yield loss with no single identified cause after weeks of investigation.
Quality escape investigations where the audit trail clears every step.
Capacity and footprint decisions with contested cost models.
Maintenance policy where failure data is sparse and expensive.
Make-versus-buy where each function's numbers tell a different story.

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Industries · Sovereign AI

AI built to keep the reasoning under your own control.

Every institution wants frontier AI's upside without handing its hardest reasoning to a platform it doesn't control or can't inspect.

Most AI tools route your hardest thinking through someone else's servers. That's a harder trade for a decision touching critical infrastructure or sensitive data.

Runs in your environment

Runs on your own machine, with model credentials you hold and can revoke.

A trail you can audit

Keeps a full reasoning trail your own team — or regulator — can audit.

Nothing routed elsewhere

Never routes your problem data through a third-party server.

Point it at

Reasoning support for decisions involving sensitive or restricted context.
Independent analysis where data residency is a hard requirement.
Institutional decisions where auditability matters as much as the answer.
Reducing dependency on any single foreign AI platform.
Any problem where "who else can see this" is part of the risk.

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Industries · Government Policies

AI built for options appraised on contested evidence.

Policy appraisal, programme review and major procurement decisions rest on evidence that genuinely conflicts. The decision has to be defensible in public, years later.

Analysis commissioned to support an option supports it. A general AI will produce a fluent appraisal that nobody can audit.

Framings nobody chose

Appraises options from several framings, including ones the sponsor didn't choose.

The weighting made explicit

Surfaces the criteria that decide the outcome, weighting made explicit.

A trail for scrutiny

Checks its reasoning and keeps a trail you can put in front of scrutiny.

Point it at

Options appraisal where the evidence base genuinely conflicts.
Programme risk review before a funding decision.
Procurement evaluation where the criteria are disputed.
Resilience and contingency planning across interacting failures.
Impact assessment where second-order effects are the whole argument.

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Put it on your hardest problem.

Not on the list above? The engine is the same one either way — tell us the problem.

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