Your AI workflow may not need more automation.
We help businesses discover and prioritise the questions that matter most across their AI workflows: the missing information, unclear rules and recurring uncertainty that prevent reliable decisions, then turn the answers into decision logic that can be tested against real cases.
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?
Automation breaks down where decisions get complicated.
AI workflows can handle routine tasks while still struggling with exceptions, incomplete information and ambiguous policies. When that happens, cases return to human experts, the same questions get answered repeatedly, and decisions can vary from one case to the next.
Adding another AI model doesn't necessarily resolve the underlying problem.
The first step is looking across many cases at once, not one at a time, to find which rules remain unclear and where human judgement is genuinely required.
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.
Find the uncertainty behind the repeated exceptions.
Understand the cases
Examine available historical cases, decisions, escalations and relevant policy documents.
Goal: Understand how the workflow behaves in practice, not just how it's supposed to work on paper.
Find recurring uncertainty
Identify recurring failure patterns, missing information, ambiguous policies and decisions that depend on expert judgement.
Goal: Find the underlying reasons similar cases keep requiring intervention.
Discover the critical questions
Identify and prioritise the questions whose answers are most likely to change the decision or reduce unnecessary escalation.
Goal: Avoid asking experts to clarify everything. Focus on the questions that matter.
Build explicit decision logic
Work with authorised experts to resolve the questions, document approved rules, and define when a case should be handled automatically or escalated.
Goal: Make important decision criteria explicit, reviewable and testable.
Test and improve
Replay historical cases, or evaluate representative examples, against the proposed logic. Identify where decisions improve, where exceptions remain, and where human judgement is still necessary.
Goal: Measure whether the changes actually improve the workflow before relying on them in production.
From a recurring exception to a tested rule.
An AI claims-handling workflow escalates roughly 1 in 6 cases to a human reviewer, almost always where a claim involves more than one named policyholder.
When a claim has multiple policyholders, who is authorised to accept a settlement on the group's behalf? The policy wording doesn't say.
An underwriting lead resolves the question and the answer becomes an explicit rule: any one named policyholder may accept a settlement below a set threshold; above it, all named parties must confirm.
A historical replay would measure how many multi-policyholder cases could have decided automatically under the new rule, which would still correctly escalate, and where human judgement remains necessary. This illustrative example does not report actual replay results.
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.
Make more of your AI workflow's decisions understandable and repeatable.
These are outcomes the approach is designed to help with, not results we're claiming to have already measured for customers.
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
Questions about the approach.
Is this another AI chatbot or agent?
No. We don't build conversational agents or general-purpose automation. The focus is narrower: finding the few questions, across many cases, whose answers would resolve the most recurring uncertainty in an AI workflow, then helping resolve and test them.
Do we have to replace our existing AI models, rules engine or workflow tools?
No. The intended approach is designed to work alongside what you already have. We're not positioning this as a replacement for your AI models, orchestration layer or rules engine, but as a way to discover and validate what those systems need to know.
Is this available today as a finished product?
We're developing and validating this approach, not offering a mature, generally available platform. The first step is a conversation about a specific workflow, to explore whether recurring decision uncertainty is a problem worth addressing together.
Do we need to share sensitive case data to get started?
No. The first conversation only needs a general description of the workflow and the kind of cases that keep requiring intervention, not confidential case material.
What if our rules are already well documented?
Then this is quicker, not irrelevant. Documented rules can still contain ambiguity, conflict with each other, or leave judgement calls unstated. The approach is the same either way: find out what the workflow still needs to know.
Where does your AI workflow still depend on human judgement?
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.