AI that ends in a posted transaction, not a demo.
We connect AI to the systems of record your finance and operations teams already use, starting with Microsoft Dynamics 365 and Microsoft Teams. People stay in control of every decision that moves money.
An employee asks for an advance in plain language. The agent confirms what it is unsure of and builds the request.
Where AI earns its place in finance and operations.
Document extraction
Read receipts, supplier invoices, and forms into structured data. A person confirms the values before anything is posted.
For example: Expense receipts, purchase invoices, onboarding forms
Agents in Microsoft Teams
Let employees raise requests and approvers act from chat, with the agent applying the rules held in your ERP.
For example: Advance requests, approvals, status questions
Assistants over ERP data
Answer questions about spend, orders, or balances from Dynamics 365 data, limited to what each user is allowed to see.
For example: Spend by project, overdue receivables, stock on hand
Triage and classification
Sort inbound email and requests by type and urgency so people start with the work that matters.
For example: Service desk email triage, request routing
Reconciliation and exceptions
Surface duplicates, mismatches, and outliers for review during the month, not at month-end.
For example: Duplicate claims, ledger mismatches, unusual spend
Expense Agent
Extraction, a Teams agent, and duplicate review working together, posting to Business Central.
See the productThe rules we build AI features by.
Start from a measured problem
We pick a process that costs your team hours every week and agree how we will measure the improvement.
People approve, AI assists
Anything that moves money or changes a record of reference goes to a named approver first.
Respect existing permissions
AI features see only what the signed-in user is already allowed to see in the source system.
Every action is traceable
Suggestions, approvals, and posted results are logged so auditors can follow what happened.
Your data is not training data
We use model providers under terms that do not use your content to train general-purpose models.
Stop if it does not pay back
Pilots have an agreed exit point. If the numbers do not justify going further, we say so.
Small, measured, and reversible.
- Step 1Pick the process
One workflow, one owner, one measure of success.
- Step 2Prove it on real data
A time-boxed pilot in your environment, with your documents and your ERP.
- Step 3Harden and roll out
Controls, logging, permissions, and training for production use.
- Step 4Measure and extend
Review results against the baseline, then decide what comes next.