Turn recurring decision problems into approved, tested improvements.
Similar cases that get different decisions aren't proof something is broken. They're a reason to investigate. We help organisations tell the difference, and turn the ones that matter into tested improvements.
Make expert judgement useful beyond the case in front of you.
Start today: an early product delivered through one supervised engagement on a recurring decision problem.
Customer requests a refund for a damaged pair of headphones.
Policy covers refunds for items reported faulty within 30 days.
The order also had an unrelated item. Does the refund cover that too?
An answer that lives in one person's head helps exactly once.
An expert resolves a difficult case. Too often, the reasoning stays in a conversation, or in someone's head. The next team runs into the same uncertainty and starts over.
We're building PayReality to preserve that useful learning, establish exactly where it applies, and test it before it shapes future decisions, not just file it away as a one-off exception.
Customer reports a damaged item, photo attached. Handler approves the refund without escalating.
Customer reports a damaged item, no photo. A second handler escalates the same policy for manager review.
Surface the consequential missing information, unclear criteria and unresolved questions hiding in cases and expert corrections.
Help the authorised owner examine a proposed answer or policy change, which cases it would affect, and what uncertainty remains.
Preserve approved reasoning, its scope, its evidence and who's responsible for it, and revisit it as later outcomes come in.
The first engagement below already puts the first two abilities to work on one real problem. Retaining and revisiting what's been learned across many decisions is the direction we're building toward, not something available broadly today.
Different decisions, different possible causes.
A different outcome is a reason to investigate, not proof that a new rule is needed.
01Missing information
A fact that mattered wasn't recorded for one of the cases. Typical response: check whether that fact is captured consistently before concluding anything else.
02Unclear or conflicting criteria
The policy or an earlier answer doesn't say clearly enough what should happen here. Typical response: get an authorised answer that resolves the gap.
03A legitimate exception
Both decisions may be correct, if an approved exception applied to one case and not the other. Typical response: confirm the exception and record why it applied.
04An incorrect decision
One of the two decisions may simply have been a mistake applying the existing rule. Typical response: correct that case, without rewriting the rule for everyone.
05Competing priorities
Two valid considerations pointed in different directions, and different reviewers weighed them differently. Typical response: ask the policy owner which should take precedence, and say so explicitly.
06Necessary human judgement
The situations may be similar on paper but genuinely different in substance, in a way a fixed rule shouldn't try to collapse. Typical response: leave it to judgement, deliberately, not by default.
07Insufficient evidence to tell yet
There may not be enough recorded cases yet to know which of the above is actually going on. Typical response: keep watching before acting on a theory.
The worked example below shows this play out on one real pair of cases, two reviewers reading them differently, and how we help an authorised owner reach and test an answer.
The complete improvement cycle, and where today's product reaches.
From a recurring exception to a tested, usable change.
Select a case, see two reviewers read it differently, then compare the proposed rule against cases it never saw.
Cases 0118, 0119 and 0124 look like photo evidence decides it. Cases 0131 and 0145 break that pattern: one approved with no photo, one escalated with a photo. What actually lines up across all five is whether the fault was reported before the 30-day deadline. This is an observation, not yet a confirmed explanation.
2. Two reviewers, two interpretations
Reviewer A (fictional, for this illustration)
"Refund requests after the deadline are refused, full stop."
Reviewer B (fictional, for this illustration)
"Customers who reported the fault before the deadline may remain eligible, even if the claim is processed later."
Both reviewers are reading the same cases. Neither has resolved the question yet, so this is exactly where investigating candidate explanations starts, not where it ends.
3. Candidate explanations
Why might these two refund requests have gotten different outcomes?
Possible explanations, not conclusions:
- The two customers may simply have reported their faults at different times (missing information until that's checked).
- A different policy version may have applied to one of the two cases (unclear or conflicting criteria).
- One reviewer may have made a mistake applying the existing rule (an incorrect decision).
- An authorised exception may have applied to one of them that isn't visible in these recorded fields (a legitimate exception).
- The available records may simply be incomplete (insufficient evidence to tell yet).
4. Answer, approved by the policy owner
Approved by Priya Shah, Head of Customer Operations (fictional, for this illustration):
"Eligibility depends on when the fault was reported, not when the claim is processed. If it was reported within 30 days, approve it, photo evidence or not. If it was reported after 30 days, escalate it for manager review rather than refusing it outright, since late reporting can still have a legitimate reason."
5. Proposed rule
CASE 0202Agrees with what actually happened
CASE 0207Disagreement: flagged for review
CASE 0214Unresolved: missing information
CASE 0229Outside what this change covers
1 of 4 agree with what actually happened or the stated test, 1 of 4 disagree and are flagged for review, 1 of 4 stay unresolved for missing information, and 1 of 4 falls outside what this change was even meant to cover. These are counts from a fixed illustrative dataset, not an accuracy rate, and not a result measured for any customer.
The approved answer here stays linked to the cases and scope it came from, so it can be reused on the next matching case and reconsidered if the circumstances change, the same link the larger ambition above depends on.
A usable change package, not a list of questions.
When an improvement is the agreed job, a report that only raises questions isn't the deliverable. The first engagement is built to end with something your team can actually use.
The answer an authorised expert actually gave, and exactly what it does and doesn't cover.
A rule, an information requirement, or whatever form the answer actually takes, written so your team can use it.
The real cases the question and the answer are grounded in, not a generic example.
How the proposed change performs on separate cases it was never shaped by, including disagreements and unresolved cases.
What's still uncertain, stated plainly, and a clear handoff to whoever is responsible for applying it.
Applying the change depends on a supported system, your permission and release controls. Where those exist, we can include implementation and verification as separately scoped work. Where they don't, the package is handed to your team with that boundary stated, not a one-click release we don't yet offer.
One bounded problem, taken from investigation to a tested result.
Start with one recurring problem
Cases that keep returning to the same expert, a rule that still reads as ambiguous after it was written down, an exception nobody's reduced to logic yet, or a proposed change whose consequences are hard to predict. The cause isn't assumed in advance: it could be a missing criterion, a data or integration fault, inadequate training, or a judgement call that should stay a judgement call.
Agree what a useful result means
Before work begins: the problem and its real consequences, who owns the workflow, which experts take part, the scope and which cases are suitable, how data is handled, what counts as acceptance, and who is responsible for implementation.
Deliver a usable result
The approved interpretation and its scope, a proposed rule or information requirement in a form you can use, the supporting cases and test results behind it, the uncertainties that remain, and a handover to whoever implements it. A list of questions on its own isn't the deliverable when an improvement was the job.
Make the implementation boundary clear
Applying a change depends on a supported system, the right permissions and release controls. Where those exist, implementation and verification can be included. Where they don't, the change package is handed over with that boundary stated plainly, not implied away.
Measure our contribution
What you could already accomplish without us, what we added, how much time both teams spent, what was found or missed, and what actually changed and was tested. Separate from whether it was ever applied, and from what could be observed afterward.
Not another model, rules engine or workflow tool.
Each of these is useful, and we expect to work alongside them, not replace them. None is built to answer the question we focus on: what does this workflow still need to know to decide reliably?
Underneath all four: discovering the recurring decision uncertainty across many cases, identifying the few questions whose answers would resolve the most of it, resolving them with authorised experts, and testing the resulting logic against real cases. This is a hypothesis we're building toward, not a claim that other approaches can't do this. We expect it to work alongside the AI models, workflow tools and rules engines you already use, not replace them.
Recognise this in your own workflow?
Cases repeatedly escalate to the same experienced employees.
The same questions get asked across many similar cases.
AI agents frequently request human intervention, or exceptions get handled inconsistently.
Important decisions depend on knowledge that lives in people's heads, not in explicit rules.
We're focused on mid-sized and larger organisations that have already deployed AI-powered workflows, or are running serious pilots, including insurance, financial services, customer operations and shared services. These are the problems we're built for, not confirmed customer segments.
Heads of AI and AI Transformation
Heads of Intelligent Automation
Operations Directors and Business Process Owners
AI Solutions Architects
Leaders responsible for AI-enabled customer operations
You need a working system in production this week. This is an early, supervised engagement, not a deployed platform.
The decision already runs on documented, uncontested rules nobody disputes. There's little recurring uncertainty for us to help resolve.
No one with the authority to approve an answer can take part. Without that, a question can be raised but never properly resolved.
What this is designed to help with, and what we haven't proven yet.
Fewer avoidable escalations.
More consistent handling of similar cases.
Less repeated questioning of the same subject-matter experts.
Clearer rules and decision ownership.
Better visibility into where uncertainty remains unresolved.
Evidence to decide what should be automated and what still needs human judgement.
Outcomes the approach is designed to help with, not results we're claiming to have already measured for customers.
Finding candidate questions today uses an analysis method we've built and tested on recorded cases, not a fully autonomous system.
We're also evaluating additional AI-assisted analysis, not yet verified in a live customer engagement.
Some early delivery work, including case preparation, is still done by hand, by us.
No customer engagement has yet established a measured business outcome.
The current prototype doesn't deploy changes into your production workflow.
Continuous monitoring and organisation-wide decision mapping are part of the longer-term direction, not available today.
Effort actually spent by both teams, against what the same investigation would have cost without us.
What was found or missed, compared with how the problem was normally handled.
How many revisions the proposed answer needed before the policy owner approved it.
How the approved change performed on separate cases it was never shaped by.
Whether the organisation chose to adopt the change and continue past the first engagement.
What's still uncertain afterward, stated plainly rather than rounded up to a success.
A measurement plan for the first engagement, not a results figure we're reporting today.
Questions about the approach.
What does the first conversation involve?
A conversation about one real, recurring problem: which cases keep requiring a person, what the consequences are, and whether it's worth examining together. No commitment and no case data is needed for this step.
Who needs to take part?
At minimum, the person who owns the workflow and at least one expert who actually reviews the cases. If more than one person could authorise an answer, naming that upfront avoids resolving the same question twice.
Do we need to share confidential case data to start?
No. The first conversation only needs a general description of the workflow and the kind of cases that keep requiring intervention, not confidential material.
What data might be needed later?
Once we agree a problem is worth examining, we'd typically need a bounded set of real or representative cases, their recorded facts, and the decisions made on them, handled the way you require.
Which parts of this are done manually today?
Some early analysis and case preparation is still done by hand, by us. We say so directly rather than presenting manual work as if a finished, automated product did it.
What do we actually receive?
A usable change package: the approved interpretation, a proposed rule or information requirement, the supporting cases, test results against separate cases, and the limitations that remain, handed to whoever owns implementation.
Does this replace our existing AI, rules engine or workflow tools?
No. It's designed to work alongside what you already have, by finding and resolving what those systems still need to know, not by replacing them.
Does PayReality change our production systems automatically?
No. The current prototype doesn't apply changes to your production workflow. Where a supported integration, your permission and release controls exist, implementation can be included as separately scoped, verified work. Otherwise, the change package is handed over for your team to apply.
What happens if the evidence says we shouldn't change anything?
That's a legitimate result, not a failed engagement. The outcome might be a justified decision to keep the current rule, collect one more piece of information, or leave the case to human judgement.
How do we judge whether this was worth it?
By comparing it with what you could already achieve: time spent by both teams, what was found or missed, what actually changed and was tested, and whether you'd choose to continue.
What if two reviewers read the same cases differently?
That's treated as the normal starting point for an investigation, not a problem to hide. The worked example on this page shows two reviewers disagreeing before anyone resolves it, and the authorised owner's answer is what settles it, on the record.
Does a tested change cover every related case?
Not necessarily, and we say so when it doesn't. A proposed change can turn out to be outside what it was designed to cover for a given case, which is a useful result, not a failure, because it shows exactly where a separate rule still applies.
One workflow is where the larger ambition starts.
Tell us about a workflow where cases repeatedly stall, exceptions keep returning to experts, or similar situations receive different decisions. We can explore whether recurring uncertainty is a problem worth addressing together.