# Decision support for specialist matching at a global asset manager

Finding the right specialist match took about three days, in a setting with real accountability. An audit-grade agent now returns ranked, cited recommendations, and a person makes the call.

3 days → about 1 hour

About 95 percent faster

Global asset manager, over 1 trillion in assets under management. A person owns every call.

## The problem

Matching a request to the right specialist meant searching several sources, weighing the fit and writing up the reasoning for someone accountable to sign off. It took about three days, and in a regulated setting the reasoning mattered as much as the answer.

## How the agent works

The agent is built to support a decision, not to make it.

1. It gathers candidates from the sources the team already trusts.
2. It scores and ranks them against the criteria for the request.
3. Every recommendation cites the evidence behind it, so a reviewer can check the reasoning rather than trust it.
4. A person reviews the ranked list and owns the final call.

The human-in-the-loop pattern was designed together with the client's risk function, so the controls were part of the system from the first version rather than added after an audit question.

## What changed

Specialist matching went from about three days to about an hour, roughly 95 percent faster, with a person still owning every decision and a cited trail behind each one.

## What a project like this needs from you

Access to the systems the work already lives in, usually a mailbox and the system of record, with a login the agent can use safely. The rules written down, or a person who knows them and has an afternoon to explain them.

A set of real past cases to test against. Replaying last month through a new version before it goes live is what lets a team trust the change. A named owner for the decisions the agent will not make, and a period where it runs alongside the team in shadow mode before it acts on anything.

## Common questions

### Can AI make decisions in regulated financial services?

It can support them. The pattern that passes a risk review is an agent that recommends with its reasoning cited and a named person who makes and owns the decision. The controls are designed with the risk function from the start.

### What makes an AI recommendation audit-grade?

Every claim traces to a source a reviewer can open, the ranking criteria are explicit, and the final decision and its owner are recorded. If a reviewer cannot see why the agent ranked something first, the recommendation is not ready for a regulated setting.

### How do you keep a human in the loop without slowing everything down?

Put the person at the decision, not at every step. The agent does the gathering, scoring and write-up, which is where the days went, and the person reviews a ranked, cited shortlist in minutes instead of building it from scratch.

## Field notes behind this[TrustTraceability Beats Fluency Why Sourced AI Gets Adopted in Regulated Work](https://shurco.ai/insights/traceability-beats-fluency-why-sourced-ai-gets-adopted-in-regulated-work/)[FinanceFive Questions Every Finance Team Should Ask Before Trusting an AI Recommendation](https://shurco.ai/insights/five-questions-every-finance-team-should-ask-before-trusting-an-ai-recommendatio/)[DesignHuman in the Loop Is a Design Decision Not a Disclaimer](https://shurco.ai/insights/human-in-the-loop-is-a-design-decision-not-a-disclaimer/)

## Other problems I have solved[ManufacturingAn AI quoting agent for a manufacturer, from days to hoursThe quoting inbox took one to three days per quote. An agent now reads the enquiry and the bill of quantities and prices it against the live catalogue, behind a rules engine that keeps every price explainable.](https://shurco.ai/solutions/ai-quoting-for-manufacturers/)[Field operationsAutomating multi-site field reporting, from days to minutesDaily reporting across many sites took about three days to compile because the data lived in several places. Collector agents now gather it in parallel, a reconciler merges it and a reporter writes the report.](https://shurco.ai/solutions/multi-site-field-reporting-automation/)[DistributionAutomating sales order entry from emailed purchase ordersPurchase orders arrive by email as PDFs, spreadsheets and free text, and someone keys every line into the ERP. For one wholesale distributor that became a checked pipeline, then the same approach ran across every country in a group.](https://shurco.ai/solutions/sales-order-entry-automation/)[StaffingAutomating timesheet processing for a weekly payroll runHundreds of emailed timesheets a week, a hard payment deadline and no room for a wrong payslip. This is how the reading, checking and reconciliation moved to a pipeline of agents.](https://shurco.ai/solutions/timesheet-to-payroll-automation/)

## Have the same problem

Each system above was built around one client's process. Yours will differ in the details, which is where the work is. [Tell me what your team spends too long on.](https://shurco.ai/#contact)

Web version https://shurco.ai/solutions/specialist-matching-decision-support/