Reconciled foundations ready for the AI revolution.

The foundation everything else sits on. Numbers you can defend, the deeper commercial analysis you have been putting off, and a data layer that is genuinely ready for AI.

LiveIngesting

XXero

NNetSuite

SStripe

P&L

✓

Balance Sheet

✓

Cash Flow Statement

✓

Board Pack

✓

AI Ready

Step1/4 · Misaligned sources

For every stakeholder in the value creation journey.

If you run a fund

A reporting layer that is the same shape across every PortCo. KPI definitions matched to your LPA (limited partnership agreement), and quarterly packs that need no analyst to translate. It stops you stitching disparate finance teams' outputs together by hand, and gives you one AI-ready data layer across the portfolio.

If you run a portfolio company

A monthly close your CFO can defend, line by line. A board pack that ties to source, so the next sponsor question does not become a fire drill. Numbers your finance team owns and runs after we step back; the foundation that future AI tooling can rely on.

Symptoms

Numbers do not tie between systems.
Monthly close takes five days.
Board packs need verbal explanations.
Numbers presented to the board cannot be fully defended on the source.
You can see the top level numbers, but not the cross cuts. No view by segment, cohort or entity without a manual exercise.
AI initiatives stall because the underlying data cannot be trusted.

What we do

Reconcile the GL across every source: your ERP, CRM and billing systems, whether that is NetSuite, Salesforce, Stripe or the rest of your stack.
Build a single reporting layer your finance team owns and your investor reads.
Match KPI definitions to your sponsor's LPA, line by line.
Compress the close cycle from days to hours.
Where there is no data warehouse, we would generally suggest putting one in place, and we help you do it.
Structure and document the data so it is ready for AI, not just for reporting.

You walk away with

A reconciled monthly close (zero variance).
A board pack template that ties to source.
A KPI register, with definitions matched to your investor.
An audit pack, with every entry traceable.
An AI-ready data foundation, clean and well-defined enough to build on.

Reconciled in weeks.

Wk 001

Source map

A 90-minute call. An inventory of every system that touches finance.

Wk 102

Pull & match

Every ledger, every line. Variances flagged.

Wk 2–303

Reconcile

Variance to zero. Audit pack assembled.

Wk 404

Hand off

Board pack template, KPI register and definitions document.

See for yourself

Foundational Data in the field.

All case studies →

HEALTHCARE SOFTWARE · FOUNDATIONAL DATA

Monthly ARR loss cut by 79%.

Reduction in average monthly ARR loss across the period.

Read the case study →

CLOUD HR SOFTWARE · FOUNDATIONAL DATA

One source of truth for ARR, across every invoicing system.

Single source-of-truth customer cube, consolidating multiple systems.

Read the case study →

TECHNOLOGY · FOUNDATIONAL DATA

Fast-moving ARR, finally trackable.

Key sales metrics tracked and cross-cut in one place.

Read the case study →

What people ask first.

It usually is, and that is the work. Zero variance is the standard DVS reconciles to, and all DVS needs to get there is access to your source systems.