Turn unclear business requirements into build-ready decisions.
PayReality helps AI and automation delivery teams uncover conflicting requirements, compare possible interpretations, and give engineers clear decisions they can implement.
Start today: a focused, five-day Policy-to-Behaviour Scoping Sprint on one stalled workflow.
Customer files a claim for storm damage to their vehicle.
Their coverage changed while the claim was still open, mid-renewal.
Does the original policy apply, or the updated one?
The code isn't always what's holding you back.
The project is moving. Engineers are building. Then a business rule turns out to mean different things to different stakeholders. Operations expects one behaviour. Compliance expects another. The requirements don't settle the disagreement, and delivery slows while everyone works out what should happen.
Make the disagreement clear before it becomes rework.
PayReality brings together the requirements, policies and stakeholder notes a workflow actually runs on, and finds where they genuinely disagree, not just where the code is incomplete.
We don't force an agreement. We show the responsible business owner exactly what's disputed, what each interpretation would do, and what it would cost to get it wrong, so the decision gets made deliberately instead of by whichever engineer implemented it first.
Claim opened during a coverage renewal window. The incident happened before the new terms took effect. Handler applies the original policy.
Same renewal window, same kind of claim. A second handler applies the updated policy instead, because the claim itself was opened after the renewal date.
Find the question behind the exceptions.
Many difficult cases may depend on the same unresolved requirement. We look for the smallest, highest-value set of decisions whose resolution would unblock the most implementation work, not every exception treated as its own isolated problem.
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 requirements
The requirement or an earlier decision doesn't say clearly enough what should happen here. Typical response: get an authorised decision that resolves the gap.
03A legitimate exception
Both outcomes 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 outcomes 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 stakeholders weighed them differently. Typical response: ask the decision 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 claims, two reviewers reading them differently, and how we help an authorised owner reach and test a decision.
From unresolved requirements to a decision your engineers can build.
A claim opened under one policy. Coverage changes mid-claim. Now what?
Select a claim, see two reviewers read it differently, then compare the proposed rule against claims it never saw. Illustrative, not a customer result.
Claims C-0301, C-0302 and C-0305 look like the version the claim was opened under decides it. Claims C-0311 and C-0317 break that pattern: one opened under Version A but still got updated terms, one opened under Version B but still got original terms. This is an observation, not yet a confirmed interpretation.
2. Two reviewers, two interpretations
Claims Ops (fictional, for this illustration)
"We apply whichever policy version was active when the claim was opened, full stop."
Compliance (fictional, for this illustration)
"Coverage should follow the policy version in force on the date the incident actually occurred, whenever the claim happens to be processed."
Both reviewers are reading the same claims. Neither has resolved the question yet, so this is exactly where comparing interpretations starts, not where it ends.
3. Candidate interpretations
Why might these two claims have gotten different coverage decisions?
Possible interpretations, not conclusions:
- The two claims may simply have different incident dates relative to the change, overlooked until checked (missing information).
- The policy documents may not clearly state which date governs eligibility (an unclear requirement).
- An approved exception may apply to one claim that isn't visible in these recorded fields (a legitimate exception).
- One reviewer may have made a mistake applying the existing rule (an incorrect decision).
4. Decision, approved by the business owner
Approved by Priya Shah, Head of Claims Operations (fictional, for this illustration):
"Coverage is determined by the policy version in force on the date the incident occurred, not the date the claim is opened or decided. If the incident happened before the coverage change took effect, apply the original terms. If it happened on or after the effective date, apply the updated terms."
5. Proposed rule
CLAIM C-0402Agrees with what actually happened
CLAIM C-0407Disagreement: flagged for review
CLAIM C-0414Unresolved: missing information
CLAIM C-0429Outside what this decision 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 decision 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 point isn't that PayReality chooses the correct answer. It's that PayReality helps the team see the decision that must be made, and the consequences of each option, before an engineer has to guess.
Policy-to-Behaviour Scoping Sprint.
One workflow. Five working days. A focused clarification exercise, offered as an early engagement, not an established service with a track record yet.
Every business decision this workflow depends on that isn't actually settled yet, named explicitly.
What each plausible reading of the requirement would actually do, on representative cases.
Who actually has the authority to resolve each one, named by name or role, not left ambiguous.
Bringing the right people together with the evidence already prepared, not a cold meeting.
For every decision that gets resolved, the exact criteria and test cases your engineers can implement against.
We propose this as the starting engagement, not a proven, repeatable product with results to cite yet. Pricing is discussed directly once we understand the workflow.
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 hasn't this team actually agreed on yet?
PayReality finds and prepares the decision. Your business owner still makes it.
Underneath all four: bringing the requirements together, finding where they genuinely disagree, comparing what each interpretation would do on representative cases, and handing the responsible owner a decision with its consequences made explicit, not a confident guess. This is an early approach we're validating, not a claim that other tools can't eventually do this. We expect it to work alongside the AI assistants, workflow tools and rules engines you already use, not replace them.
Recognise this on your current project?
A fixed-price project is stalling because stakeholders haven't agreed what the system should do.
The same requirement gets interpreted differently by different people, and nobody has reconciled it.
Engineers keep escalating the same kind of ambiguous business rule.
A go-live date is at risk because of unresolved disagreement, not unfinished code.
We're focused on AI and automation delivery agencies, especially those working fixed-price, where requirement ambiguity directly affects delivery and profitability. These are the problems we're built for, not confirmed customer segments.
Delivery leads at AI and automation implementation agencies
Solution architects on fixed-price automation projects
Enterprise AI programme owners
Business analysts bridging stakeholders and engineering
You need PayReality to make the business decision for you. We prepare the decision; your business owner still makes it.
The requirements are already clear and agreed. There's no real ambiguity to resolve.
No one with the authority to decide can take part in the clarification session.
What this is designed to help with, and what we haven't proven yet.
Fewer fixed-price projects stalled on unresolved business rules.
Less engineer time spent chasing stakeholder clarification.
Clearer decision ownership before implementation starts.
Fewer late-stage requirement disputes that trigger rework.
A documented record of why a decision was made, not just what was built.
Evidence of which interpretation stakeholders actually agreed to.
Outcomes the approach is designed to help with, not results we're claiming to have already measured for customers.
PayReality doesn't make the business decision. It prepares the decision for the people accountable for making it.
We can't guarantee stakeholders will agree. Some disagreements are commercial or political, not a clarity problem.
This is an early product direction, not a proven enterprise deployment.
We don't yet have validated customer savings or paying customers to point to.
Some early analysis and facilitation is still done by hand, by us.
PayReality doesn't implement the resulting decision in your systems. Your engineers do, from the brief.
Questions about the approach.
What is the Policy-to-Behaviour Scoping Sprint?
A bounded, five-working-day clarification exercise on one workflow: we bring together the requirements, identify the decisions that are actually unresolved, compare interpretations, and prepare them for the people who can decide.
What does the first conversation involve?
A conversation about one real, stalled workflow: which requirement is actually disputed, what it's costing in delay or rework, and whether a focused scoping sprint is worth running together. No commitment and no project data is needed for this step.
Who needs to take part?
At minimum, someone who can see the requirements and project history, and at least one person with the authority to resolve a disputed decision. Naming that authority upfront avoids resolving the same question twice.
Do we need to share confidential project data to start?
No. The first conversation only needs a general description of the workflow and where it's stalling, not confidential material.
What do we actually receive?
A decision brief: the register of unresolved decisions, the competing interpretations and their consequences, the authorised answer for each one resolved, and build-ready acceptance criteria your engineers can implement directly.
Does PayReality replace our business analysts or product owners?
No. It's designed to work alongside them, by surfacing and preparing the decisions that need a business owner's attention, not by replacing the people who own the requirements.
Does PayReality make the decision for us?
No. PayReality identifies the disagreement, compares what each interpretation would do, and prepares the decision. The responsible business owner decides.
What happens if stakeholders still can't agree?
That's a legitimate, and useful, result. Knowing exactly which decision is blocking delivery, and why the disagreement hasn't resolved, is itself valuable even before it's settled.
How much does the scoping sprint cost?
It depends on the workflow and scope. We discuss pricing directly once we understand what you're trying to clarify, rather than quoting a fixed number up front.
How do we judge whether this was worth it?
By comparing it with what the ambiguity was already costing: engineer time spent chasing clarification, delivery slipped, and rework from decisions made without one source of truth, against what the sprint found and resolved.
Have a workflow stuck between requirements and implementation?
Let's look at where the uncertainty is, and whether a focused clarification exercise could help.