Operations & public services
Operations and public services
Asset-heavy operations, healthcare and public-sector bodies, and insurers handling warranty or claims all have the same shape of problem: more cases, assets and defects need attention than there is capacity to give it. So the order in which they get handled is a decision, and an expensive one, and it is usually made by severity or by arrival. We order it by what each case will cost if it waits, in money, in contractual and regulatory exposure, and in public harm. And where the rule that sets the priority must never fail quietly, we can prove the logic as well as test it.
The decisions that bind here
The decisions this sector makes every week
The examples below illustrate the pattern and make no claim about any particular institution. What they share is a queue that cannot be fully served and an ordering that carries consequences.
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Which case or asset first?
When remedial capacity is smaller than the backlog, the order of work is a decision, best made against downstream loss, exposure and value ahead of a first-in-first-out default.
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Which claims to handle, and how?
For warranty and insurance, triage sets both cost and fairness. We rank against expected loss, contractual duty and customer or claimant impact together.
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How to schedule constrained remediation?
Crews, inspectors, clinicians and equipment are finite. A feasible remediation plan respects those constraints while still targeting the highest-consequence work first.
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Which rules need proving?
Where a prioritisation or eligibility rule carries safety, legal or public-trust consequences, testing samples behaviour and proof establishes it. We can verify the critical logic formally.
What it costs to get them wrong
Why the order matters more than the speed
Severity is not consequence
Ordering work by how severe it looks ignores what each case risks downstream. A cheap defect that fails safe can wait. A minor one that cascades cannot, and the ticket does not say which is which.
Exposure compounds while queued
A claim, asset or case left waiting accrues contractual penalty, regulatory breach or reputational cost while it sits. The wait is a decision, and it has a price, even when nobody made it on purpose.
Silent failure is the worst kind
A prioritisation rule that is wrong in one rare case can fail without anyone noticing until the consequence arrives, and in this sector the consequence is sometimes a person, and for that logic testing is not enough.
How we engage
How an engagement runs here
We start on one prioritisation or remediation decision, prove a better ordering against your current practice on your own data and, where the logic is critical, verify it as well as test it.
1. Audit
Find the ordering that pays
A Decision Opportunity Audit runs on one backlog or claims stream, establishes the baseline and the stakes, and ends candidly, including the possibility that your current triage is already close to right.
2. Proof
Beat current practice, on your data
A proof of value tests a proposed ordering against your existing rule on a shared benchmark before anyone trusts it. What counts as evidence is fixed in advance. See how we validate.
3. Build and verify
Prove the rules that must not fail
Only what proved out is built and owned. Where the logic is critical, we can machine-check it against a tiny trusted kernel with Rainbow, within stated boundaries, and Find, Prove, Build is the order it happens in.
What we need to see
A record of the cases, assets or claims and how they are currently ordered, the constraints on remedial capacity, and the consequences that ordering is meant to weigh. Where a baseline was never captured, establishing one is the first deliverable. An ordering cannot be improved against a cost nobody records.
The solutions behind it
The methods behind it
Two solutions meet in this sector. The warranty and remediation solution decides where limited remedial effort is worth the most, against cost, exposure, customer value and downstream risk. And verification of critical software proves the decision logic where testing cannot close the risk, using the same methods that underpin Rainbow, our open-source theorem prover.
Where it applies
- Asset-heavy operations with maintenance and remediation backlogs.
- Healthcare and public-sector operations rationing constrained capacity.
- Insurers and warranty providers triaging claims against cost and duty.
- Any operation whose priority rules carry safety, legal or public-trust weight.
The research behind it
The method, published
When we are a fit
- A backlog of cases, assets or claims that outstrips the capacity to clear it.
- Consequences of ordering that can be estimated: loss, exposure, value.
- Or a critical rule whose correctness warrants formal proof.
When we are not
- Capacity comfortably clears the queue, so ordering does not bind.
- No usable record of cases, constraints or the consequences to weigh.
- A problem where conventional testing already closes the risk adequately.
Who you deal with
A partner owns the engagement and stays answerable for it after the system is running. Work is confidential and unattributed by default, which matters more here than almost anywhere: no institution appears in a deck or on this website without written permission. An audit is a fixed fee agreed before it starts, and the references are people who have carried the same accountability you do.
FAQ
Questions from operators and public bodies
Which decisions do you work on in this sector?
Which case, asset or claim to handle first; which claims to handle and how; how to schedule constrained remediation across finite crews, inspectors, clinicians and equipment; and which prioritisation or eligibility rules must be proved, and not only tested.
How do you decide what to handle first?
We order against downstream loss, contractual and regulatory exposure and public value, ahead of apparent severity or a first-in-first-out default. A claim, asset or case left waiting can accrue penalty, breach or reputational cost, so the wait itself is a decision with a price.
When would proving a rule beat testing it?
Where a prioritisation or eligibility rule carries safety, legal or public-trust consequences and must not silently fail. There we can machine-check the logic against a tiny trusted kernel with Rainbow, our open-source prover, within stated boundaries. Testing samples the behaviour, and a proof establishes it.
What data do you need to start?
A record of the cases, assets or claims and how they are currently ordered, the constraints on remedial capacity, and the consequences that ordering is meant to weigh. Where a baseline was never captured, establishing one is the first deliverable. An ordering cannot be improved against a cost nobody records.
Start with the decision
Bring us the queue
One backlog, claims stream or critical rule. A short audit run by a partner will say what the current ordering is costing, and whether a better one, or a proof, is worth building. The fee is fixed before we start.