AI Expense Receipt Capture
Handles expense capture and reconciliation for finance teams and operations managers.
What This Automation Does
- 1Accepts a receipt through a web form or a chat message
- 2Reads the image and extracts merchant, date, total and tax
- 3Validates the extracted fields against a defined schema before anything is written
- 4Appends or updates the matching row in the expense sheet
- 5Replies to the submitter with exactly what was recorded
Why we built it
We built this because expense capture fails at the point of entry, not at the point of approval. A receipt photographed and forgotten is a receipt retyped by someone in week four, or never claimed at all. Reading the image at the moment of submission and confirming what was recorded closes that gap.
Problem It Solves
Expense capture and reconciliation
Best For
Business Benefits
- Remove manual re-keying of receipt data
- Catch a misread at submission rather than at month end
- Keep the expense sheet current instead of reconstructing it later
- Give submitters a record of what was captured
What the AI does here
Tool-calling AI agent
A model that decides which action to take and calls the connected tools itself, rather than following a fixed branch.
Tools the agent can call×2
The specific capabilities exposed to the agent - search, HTTP calls, sub-workflows, or a system of record it can read and write.
Schema-validated output
The model's response is parsed against a defined schema, so a malformed answer fails loudly instead of flowing downstream as bad data.
Conversation memory
Prior turns are carried into the next call, so the system holds context across an exchange instead of treating each message as the first.
LLM chain×2
A fixed sequence of model calls - summarisation, drafting or classification - with no autonomous tool selection.
Language model call×3
A direct model call for classification, extraction, drafting or scoring at a defined point in the flow.
As built, this one runs on OpenRouter. Model and provider are chosen per engagement against your own accounts, cost tolerance and data-residency requirements — nothing here is fixed to one vendor.
Tools Connected
Holds the expense log that each captured receipt is appended to or updated in.
Second submission channel, and where the submitter gets confirmation of what was recorded.
Routes the vision and extraction calls to the chosen language model.
Reads text off the receipt image before the model structures it.
Available as
A build we deliver for you
Adapted to your systems, your data and your process — not dropped in as-is. Scoped and quoted per engagement.
Architecture
Workflow Architecture
How this automation moves data, applies logic, connects tools, and produces business outcomes.
Receipt Submitted
triggerSubmission Validation
validationReceipt Field Extraction
aiExpense Log Update
databaseSubmitter Confirmation
notificationLogged Expense
output25
Operational steps counted in the workflow
4
Google Sheets, Telegram, OpenRouter…
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