Services / AI Copilots for Finance
AI Copilots for Finance. Ask the numbers a question, and get an answer you can check.
A copilot sits on top of the reporting layer and answers questions in plain language: why gross margin moved, which customers are behind, what changed since last month. It is built on your own definitions, it shows the figures behind every answer, and it says so when it does not know. Used well, it clears the queue of ad-hoc requests that sits on a finance team all month.
When this helps
- Finance spends the month answering the same questions by email
- Managers cannot get a number without asking somebody for it
- Variance commentary gets written under time pressure on the last day
- The data exists, but nobody outside finance can reach it
- You want to use AI on the business without sending the ledger to a public model
What is included
The scope is agreed around the decisions in front of you. A typical engagement draws on the following.
Grounded answers
It answers from your data and your metric definitions, not from general knowledge.
Shown workings
Every answer carries the figures and the source behind it, so that it can be checked.
Drafted commentary
A first-draft variance narrative for the monthly pack, for a person to edit and sign.
Permissions
People see what their role allows, with the same boundaries as the reporting layer.
Data boundaries
Deployment chosen so that your financial data stays where your policy requires it to stay.
Refusal behaviour
Where the data cannot answer, it says so. A confident wrong number is worse than no number.
Deliverables
- Copilot, deployed to your team
- Continuous
- Grounded metric definitions
- Once, then maintained
- Drafted monthly commentary
- Monthly
- Access and permission model
- At setup
- Accuracy review
- Quarterly
How it works
A clear sequence, so that you see value early and nothing is built that your team cannot own.
Ground
Connect to the reporting layer and encode the metric definitions it must answer from.
Constrain
Set permissions, data boundaries, and the limits of what it is allowed to assert.
Test
Run real questions from your team against known answers until it is reliable.
Release
Roll out to management, with training on what it is good for and what it is not.
Questions
Does our financial data leave our environment?
That is a decision we take with you before anything is built, and it drives the choice of deployment. Where your policy requires the data to stay in your own tenancy, it stays there.
What stops it inventing a number?
It answers from your reporting layer rather than generating figures, every answer shows its workings, and it is built to say when the data cannot support an answer. We test it against known results before release, and review accuracy quarterly.
Do we need this before we have dashboards?
No, and we would advise against it. A copilot is only as good as the data layer underneath it. If the reporting is not yet reliable, start there.
Other services
Most clients combine two or more services as their needs develop.
Discuss an AI copilot for your finance data. Thirty minutes, without obligation.
Tell us where the business is headed and what stands in the way. Or write directly to alberto.salas@blackbearcorp.com.
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