Tie-Out · AI finance warehouse

Give your finance team back the days it spends explaining numbers.

Tie-Out answers "why did work in progress go up in March?" in plain English, in seconds, with named drivers, totals that tie to the cent and the working shown. When there is no approved way to answer, it says so.

Why did work in progress go up in March?

+7.0m

Opening40.0m
Work done+12.0m
Invoiced−5.0m
Closing47.0m

Insight: work done outran invoicing. One client carries 30% of the rise, on a job billed at 4% of the work done.

Action

Three jobs billed below 20% hold 2.1m ready to invoice. Ask their owners, M. Rivera, J. Okafor and L. Novak, to bill them. WIP drops 4.5%.

Every answer opens to its working: drivers by job, tied to the cent. Illustrative figures and names.

In production at a global professional services group, on the approach Tie-Out packages

1,670person-days a year handed back (estimate)
$57Mcash released*
4 daysfaster monthly close
60 secondsfor quarterly variance commentary

First release in 90 days, full production in six months. *Collected by an accounts receivable and work-in-progress programme that ran on the platform. Read the case study

Insight, then action

AI variance analysis that ends in a next step

The questionWhat the answer showsThe next step
FP&A"Why did we miss plan this year?"

Small misses per line adding up to a double-digit profit miss, with hiring and freelance cover read as one story.

Fix the hiring plan behind both lines, then check the re-forecast.

CFO"Which entities earn the capital they hold?"

Entities ranked by return on capital, with loss-makers and client-funded entities called out.

A fix-or-exit list: loss-makers to fix first, capital to move.

Treasury"What have we not billed that we should have?"

Work in progress with no invoice at all, by job, client and owner.

Send the list to the job owners and bill it this month.

Controller"Intercompany receivables spiked in November. Why?"

How much reversed the next month, and the counterparties that explain the rest.

Confirm or settle the balance with those counterparties before the close.

CFO"Why does the ERP disagree with consolidation?"

Every difference with a named reason: timing, top-side adjustment or an entry that exists only at group level.

Clear each difference at source before group reporting goes out.

The foundation

Built on three layers, inside your own infrastructure

01

A finance warehouse shaped like your group

Ledger, receivables, payables, WIP, jobs, budget and intercompany eliminations in one place. ERP and consolidation reconciled, every difference explained.

02

A knowledge layer your team owns

Metric definitions, finance playbooks and the known ways numbers go wrong, written down and versioned. Without an approved method, it declines.

03

One chat and a 14-page report pack

Plain-English questions answered in minutes, without a ticket to the reporting team. Every report drills to the transactions.

Your data stays where it is.

Tie-Out runs on your BigQuery, Databricks, Snowflake or Microsoft Fabric, with the model called through your own cloud account (Microsoft Foundry on Azure today). No copy of your data leaves your environment.

The business case

How it pays back

Time, every year

Commentary drafted by the AI layer and reviewed by your team. Reference: an estimated 1,670 person-days a year.

Cash, once

Unbilled work and late receivables found by job, client and owner, then collected. Reference: $57M, collected by an AR and WIP programme that ran on the platform.

Risk, avoided

ERP and consolidation differences caught on every load, before a wrong number reaches the board. Reference: reconciled on every load, to one dollar.

Count your time saving: entities × closes a year × hours per commentary pack × share drafted ÷ 8 = person-days a year.

The rollout

From first session to production

1

Demo session

45 minutes with your CFO, FP&A and treasury leads, asking your own questions.

2

Pick the first balance

The one that takes your team longest to explain, and the people who explain it today.

3

First release in 90 days

That balance answered on your own ledger, with its playbook written down.

4

Full production in six months

More balances and entities, and your team owning the knowledge layer. Fixed price for most projects.

Before you ask

Four questions a CFO asks first.

Data, accuracy and ownership

Where does our data live?

In your own infrastructure: your BigQuery, Databricks, Snowflake or Microsoft Fabric, with the model called through your own cloud account (Microsoft Foundry on Azure today). No copy of your data leaves your environment.

What if the AI gets a number wrong?

Governed figures come from queries against agreed definitions. The AI writes the commentary, which is checked against those figures before you see it. Anything else is marked unverified.

Which systems does it work with?

Any ERP and group consolidation platform whose data lands in BigQuery, Databricks, Snowflake or Microsoft Fabric. Tie-Out is built on top of that platform.

Who owns it after go-live?

Your team. Metric definitions and playbooks are written down and versioned, so your controllers can review and extend them.

Next step

Start with one balance.

Bring the question your team spends the most time answering. We walk through Tie-Out on a demo group's books in 45 minutes, and no data from you is needed.

We reply within one business day.