For CFOs and group finance teams

One governed source for all your data. Any AI model can ask it anything.

AccuraSee unifies your systems into one governed source in your own EU tenant — identity-aware, row-level secure, fully audited. Then any model can answer over it. Here's the whole picture:

One governed source in your own EU tenant — and any AI model can answer over it:

Diagram: AccuraSee adapts to your ecosystem. Three lanes of your own, unchanged systems feed into one governed AccuraSee source of truth. One — ready-to-go connectors such as Fortnox and any ERP, line-of-business or API source. Two — or we build the foundation for you in an on-premise, governed database such as a SQL database. Three — and the documents too: folders, spreadsheets, documents, presentations and PDFs. Everything converges into one governed source of truth that is read-only, governed, and stays in your environment by default, hosted on Microsoft Azure in Sweden Central with sign-in via Microsoft Entra ID. Optionally, that governed source feeds onward to any AI client or your dashboards — Claude, ChatGPT, Microsoft Copilot and Google Gemini.

1Connect to any sourceready-to-go connectors
Fortnoxaccounting / ERP
Your ERP / LOB
Any API source
2Or we build the foundationon-prem, governed
SQL database
on-prem · your environment
3And the documents toocontracts, sheets, decks, PDFs
Folders
Spreadsheets
Documents
Presentations
Contracts · PDFs
AccuraSee AccuraSee One governed source of truth Read-only · governed · stays in your environment
Hosted on Azure · Sweden Central Sign-in via Entra ID
To any AI client
Claude
ChatGPT
Copilot
Gemini

Examples, not a fixed catalogue — if it has an API or a database, we can read it, and new read-only sources are usually quick to add.

The problem

You already have the data. Getting answers out of it still takes days.

Answers take days

Every question routes through an analyst or a BI vendor and comes back as a report.

Business logic is trapped

Every key number's meaning lives inside one vendor's dashboard files — invisible to anything else.

Access is all-or-nothing

Shared service accounts mean the database never knows who is asking. No real row-level control.

Confidential data is only hidden

Sensitive figures are protected by "who can open the report" — not by the data layer itself.

We don't bolt AI onto the current setup. We use AI as the reason to finally fix the data foundation that always needed fixing.

How it works

Three moves from raw systems to plain-language answers.

1

Unify

We read from where your data already lives and bring it together into one governed model — nothing ripped out.

2

Govern

A semantic layer defines every metric once; identity and row-level rules are enforced in the view layer.

3

Ask

Connect any AI client and ask in plain language. Answers come back sourced, permission-aware, and logged.

The product

This is what the governed source looks like.

One live dashboard over every company in the group — consolidation, budget vs actual and cash flow read from the same model your AI answers come from.

See AccuraSee
AccuraSee dashboard: group-level revenue, budget vs actual and cash-flow panels
What you can do

Every rung runs on the same governed source. Start by asking; grow into operating.

01

Ask anything

Plain-language Q&A, sourced and permission-aware.

02

Proactive monitoring

From "you go look" to "it tells you."

03

Document intelligence

Contracts and invoices read and cross-checked.

04

Forecasting

Turns the warehouse from a rear-view mirror into a windshield.

05

Agentic operations

Routine work drafted by agents — supervised by people, not performed by them.

06

ESG automation

Sustainability and compliance reporting off the same foundation.

Each rung is additive — you can stop at any rung and still have value.
See it in action

Real questions, answered the way it works on your data.

Pick a question. Every answer follows the same shape — a clear verdict, the numbers, how it was done, and the honest caveats.

Which units are over budget this quarter?

Try a question
Which units are over budget this quarter?
4 of 18 units over budget
−6.2% margin vs plan
Three are property-related; one is a one-off cost. Read from the governed finance model.
−2%North
−7%West
−9%Central
−5%South
−1%East
Highlighted bar = what the verdict hinges on
MethodBudget vs actual joined on the unit dimension; margin-vs-plan metric pulled from the semantic layer.
SourcesFinance model · cost ledger (cited on every figure)
CaveatsMarch accruals not yet posted for two units — flagged, not assumed.

Figures illustrative (synthetic data).

Built for trust

Governance gets stronger, not weaker. A security upgrade, not a compromise.

Identity-aware by design

Real per-user identity on every query via Entra ID / OAuth 2.1 — where shared accounts finally reconcile with accountability.

Row-level & attribute security

Per business unit, per object, public-vs-confidential flags — the filter is injected in the governed view layer.

Full audit trail

Every question and answer logged per user — who asked what, when, and the query that ran.

Correctness, verified

Source citation on every answer, a golden-set reconciliation harness, and predefined tools — no free-form SQL.

EU residency & GDPR

Region pinned in the EU; processing under a signed DPA with named, controllable sub-processors.

You own it

Source code, audit logs and the architecture live in your tenant. Documented, with a clean exit. No lock-in.

We say it straight: Your data stays safely in your own EU environment. The AI never gets direct access to it — instead, only the exact parts you choose to release are sent for processing by an approved EU model that retains nothing (zero-retention), governed by a DPA — so you always know exactly what happens to your data.

Proven in production

In production at a Nordic real-estate group.

150+
companies on one governed source
8
systems unified — finance, property, operations & more
2.7M+
rows unified into one governed source
100%
answers source-cited & permission-aware

Figures generalized for public use.

For the first time we can ask the whole portfolio a question and get a trustworthy answer back in seconds — with the working shown.
Translated from Swedish · sponsor, a Nordic real-estate group
Read the case
No lock-in

Works with the models you already use.

Claude ChatGPT Gemini Copilot

Protocol-agnostic via MCP — the open standard that lets AI models talk to your data. Swap the model with no re-plumbing of your data layer.

The journey

From asking, to monitoring, to operations.

A small first step that earns the next. Each phase is additive — you never pay to rebuild the foundation.

Year 1 · Foundation

Fix the foundation & ask anything

One governed layer, real per-user security, and plain-language answers over your whole business.

Year 2 · Awareness

Monitoring & document intelligence

Agents flag deviations and risks; contracts and invoices are read and cross-checked automatically.

Year 3 · Autonomy

Forecasting, agentic ops & ESG

Routine financial and operational work runs itself — supervised by people, not performed by them.

Why us

Boutique focus, real platform muscle, phased and de-risked.

01

A boutique, not a Big-4

The most direct credible route that honors the hard constraints — EU data, your tenant, no lock-in — without Big-4 overhead.

02

A real platform behind it

The platform's agentic financial and operational capabilities are already built and ready to switch on — proof, not a slide.

03

Phased & additive

Every phase delivers standalone value and gates the next. Start with a low-risk pre-study.

Ask your business data anything. Get an answer in seconds.

Start with a discovery call — we'll scope a low-risk pre-study and show you what's answerable on your data.

What to expect: a 30-minute call · we map your data sources · a live answer on your data — no pressure.