A global insurance organisation runs Xalios in an environment where every decision has to be explainable, controlled and defensible.
Implementation and solution partners across the globe, able to take one proven process to wider enterprise adoption.
An implementation team that knows the difference between an agent working in a demo and an AI employee running inside a business process.
You don’t have a people problem.
You have a decision-throughput problem.
Your team spends its day approving invoices, triaging requests, checking policies, routing work and gathering information, and making the same decisions over and over again. Adding more people is not the only answer.
It speeds up individuals, but nobody owns the queue, so the backlog is the same on Friday.
It acts fast until the first exception nobody documented, then someone has to unpick what it did.
Six weeks to recruit, three months to train, and the queue has grown again by then.
What if your team only saw the exceptions?
When they log in at 09:00, the routine work is already done.
What is left is a short list of real exceptions, each with the evidence, the reasoning and a recommended action. Your team stays. What goes is the time they spend making small, repetitive decisions.
That’s the difference between an agent that acts and an employee that answers for what it did.
Inducted like a team member with a formal job description, designated intake channels and fixed spend ceilings.
Bi-temporal memory preserves valid time and transaction time. A March invoice is always judged against the terms valid in March.
Every action writes an immutable trace of what arrived, what policy applied, what was permitted and who signed off.
Budgeted like an operational role tied to completed throughput, with unlimited seats for human supervisors.
Replayed as it stood. Not read back from a log.
Open a decision made last March and you see the policy, the rate card and the evidence as they stood in March. Not today’s version, and not someone’s memory of it. Auditability stops being a separate project and becomes a by-product of the work.
The April invoice is still checked against the April rate. That’s the difference between replaying a decision and reading a log of it.
Pick a process and see how an AI employee takes it on.
Each use case sets out the job description, what the AI employee can reach, how it earns trust and what stays with your people. The key figures are the targets a pilot sets out to prove on your real work, and measured results replace them as pilots complete.
Spend approvals
Purchase requisitions approved, returned or routed within budget and policy. Authority grows band by band, and it never approves what it prepared.
Discount approvals
Discount requests in the CRM approved, countered or routed within your bands and margin rules. Months later, why a customer got that discount still replays as it stood.
Control monitoring
Controls tested on schedule and on events, every failure raised to its owner with the evidence. Each decision is audit evidence, and a pass that audit overturns costs it a level.
