AI Solution

Process reconciliation

Most companies run processes that span several systems, and the data they produce has to be reconciled, every cycle. We build that reconciliation as an AI line, shaped to the process you actually run.

The numbers should tie out. They rarely do.

Every company has its own workflows, and almost all of them end the same way: data sitting in two or three systems that is supposed to agree, and a person whose job is to check that it does. Reconciliation is quiet, repetitive, and unforgiving, and it lands on someone every single cycle.

Take one we are asked about often. Payroll. Three records describe the same set of salaries, and all three should match: what each employee's contract says they are owed, what the payroll vendor actually paid, and what accounting booked against each cost center.

They live in three different systems, in three different shapes. The contract terms sit in Workday. The vendor's actual payouts arrive as files on SharePoint. The booked figures are pulled from Oracle. Each cycle, someone exports all three, lines them up by hand, and hunts down every place they disagree: a name paid the wrong band, a cost center that does not add up, a joiner counted twice. It is days of work, and the same breaks tend to come back next month.

The solution, in one picture

From three systems that disagree to one reconciled view, every cycle.

Built for the payroll example here, but the same line reshapes to whatever process you run. It pulls each source, normalises them, matches across all three, and routes only the genuine breaks to a person.

Contracts

What they're owed

Salary, broken down

Payouts

What was paid

The vendor's files

Booked

What was recorded

By cost center

Match

Lined up and matched

Normalised to one shape
Line by line and in total

Flag

Flagged with a cause

Known ones auto-resolved
The rest get a hypothesis

View

One reconciled view

Leadership summary
Queue, with context

TEAM

Review

Your team

Only the genuine breaks

The three records, side by side

It brings the sources into one shape.

It pulls the contract terms from Workday, the vendor's actual payouts from the files on SharePoint, and the booked figures from Oracle, and normalises all three so they can finally be compared like for like.

Matched, then only the breaks

It finds where they disagree, and why.

Claude matches the three line by line and in aggregate, and where they disagree it flags the break with a likely cause. The known patterns resolve themselves; only the genuine, novel breaks reach a person.

It learns, so the queue shrinks

Days of work, closed in under an hour.

Every break your team resolves teaches it a new rule, so next cycle there is less to chase. The same line reshapes to any process where two or three systems are supposed to tie out and rarely do.

The blueprint

What makes it work.

The connectors into Workday, SharePoint, and Oracle are specific to this payroll example. Swap them for your sources and the rest of the line, normalise, match, flag, learn, stays the same.

Three sources, one shape. Contract terms from Workday, vendor payouts from SharePoint, and booked figures from Oracle are each pulled and normalised so they can be compared like for like. Workday SharePoint Oracle
Matched two ways. Claude matches the records line by line and in aggregate, so a total that ties but a line that does not is still caught. Claude
Breaks, with a cause. Known variance patterns are resolved automatically; the rest are flagged with a hypothesis, not just a red cell. Claude
Only the genuine breaks reach people. Your team sees the novel cases, with the full trail attached, instead of re-checking everything by hand.
It learns every cycle. Each resolution your team makes becomes a rule, so the human queue shrinks month after month. Claude
Reshapes to your process. Payroll is the example here; the same line fits any process where two or three systems are supposed to agree and rarely do. Power BI