Pick the Sender, Timestamp, Amount, and Reference from Any SMS Screenshot
Manually retyping the amount, merchant, and reference number from each SMS into a spreadsheet takes 10-15 seconds per message and introduces typos you have to catch later. This does it in 5 seconds per screenshot, with both bank alerts and order confirmations in the same batch.
5-10s per screenshot · Up to 99% accuracy on printed text
Every SMS Has Two Layers of Data
Message-native metadata and transaction content — both read from one screenshot, no sorting needed.
Contact name or number from the message header — bank codes, retailer IDs, or P2P contacts.
OS thread dates or relative times like "2 hours ago." The AI outputs whatever is in the frame.
Unread dots, dimmed text, or preview vs. full-view — captured when these OS indicators appear.
The currency figure. Bank alerts prefix it; order confirmations embed it. The AI reads it as a transaction signal.
The entity behind the transaction — ALL CAPS merchant names, retailer names, or P2P usernames — identified by semantic role.
Bank auth codes, order IDs, or tracking numbers. The most variable field and the hardest to locate by position alone.
Why SMS Screenshots Are the Hardest "Easy" Extraction
An SMS looks simple — a few lines. But bank alerts follow rigid templates while order confirmations use completely different phrasing. Traditional OCR outputs the whole string as unbroken raw text, leaving you to hunt for each field by eye. As one Reddit user put it: "I screenshot every bank alert and order confirmation, then go through them one by one copying the amount and merchant. It takes 20 minutes and I still miss at least one."
Traditional OCR extracts words by position on a page — x, y coordinates, reading order. SMS is a single line or short paragraph with zero visual structure. The "$42.50" that sits at position 3 in a Chase alert moves to position 7 in a Wells Fargo message.
In a Chase alert the authorization code follows "ref:"; in an Amazon text the order ID appears mid-sentence after "order #"; in tracking notifications the number is the only mixed digit-alphanumeric string. A position-trained tool has to be re-taught for every sender.
These are two different message species. A template that learns Chase's alert structure fails on US Bank, and one trained on Amazon's confirmation format has no rules for PayPal shipping notifications. You end up managing separate processes for each source.
Sender name and timestamp live in the thread header; read status uses visual cues like colored dots. Notification panels show relative timestamps ("2 hours ago") and truncated previews hide message content. Traditional OCR was built for pages, not these OS-level affordances.
Custom Column Extraction tells the AI "find the Amount" — not "look at word 3." The AI reads the message as a financial transaction and locates each field by what it represents, regardless of where it sits or which bank wrote it.
Define Amount, Merchant, Date, and Reference once. The same four columns work for Chase alerts, Amazon orders, and FedEx tracking texts. No per-sender rules, no template switching.
Process a week's worth of SMS in one go — bank alerts, order confirmations, and tracking notifications from different senders. The output is one spreadsheet with every field filled, sorted by date, ready for your budget.
From SMS Screenshots to a Sorted Spreadsheet
Whether you are logging Tuesday's bank alerts, creating an order-tracking spreadsheet, or compiling expense records from a trip, the process is the same three steps.
Upload the screenshots
Bank alerts, order confirmations, delivery notifications — PNG, JPG, or whatever your phone produces. Each SMS screenshot is a separate file. Upload as many as you want in one batch; the processing handles mixed sources without you sorting them first.
Name your four columns
Type Amount, Merchant, Date, and Reference. The Reference column is deliberately generic — it tells the AI to look for an alphanumeric identifier of any length, covering bank auth codes, retailer order IDs, and tracking numbers without needing separate columns for each.
Get a structured spreadsheet
Every row is one SMS screenshot. The Amount column shows $42.50, $129.99, and $15.00 — one from a Chase alert, one from Amazon, one from USPS. Sort by Date, filter by Merchant, and your week's transactions are ready for review without touching a single cell.
Where SMS Extraction Excels — and Where to Watch Out
Extraction works reliably on most SMS screenshots because it reads by meaning, not layout. A few scenarios need extra attention.
When It Works Automatically
- ✓ Bank alerts with explicit dollar signs and ALL CAPS merchant names — the most consistent SMS format, extracting with the highest reliability.
- ✓ Order confirmation texts with visible order numbers or tracking codes — the AI distinguishes identifiers from surrounding text by pattern and context.
- ✓ Full message screenshots — the more context visible, the more reliable field-level extraction.
When to Be Cautious
- ⚠ SMS previews truncated by the phone notification panel — a partial preview may omit the reference and amount, collapse timestamps to "2 hours ago," or hide the sender line.
- ⚠ Payment texts referencing only usernames or phone numbers instead of business names — the AI extracts what is visible, but "sent $20 to Alex" provides less merchant detail than a formal bank alert.
- ⚠ Multi-message transactions where the amount appears in one SMS and the reference in another — each screenshot is processed independently, so a transaction split across two messages needs two screenshots and a manual merge.
Frequently Asked Questions About SMS Data Extraction
Can I extract the Amount from a bank alert and an order confirmation in the same batch?
Yes. Define "Amount" once as a column. The AI reads "$42.50 at AMAZON.COM" (Chase alert) and "Your order for $29.99 has shipped" (Amazon confirmation) by recognizing the currency figure tied to a transaction — not by memorizing where each sender places the dollar sign. Both land in the same column, one row per screenshot.
What if the Reference number is an order ID in one SMS and a tracking code in another?
This is the most common variation across SMS senders. Custom Column Extraction treats "Reference" as a generic unique identifier — the AI finds whichever alphanumeric string serves as the transaction key, regardless of whether it is a 6-digit bank authorization code or a 22-digit USPS tracking number. The same column works for both.
Can I extract data from an SMS that says "2 hours ago" instead of a real date?
Relative timestamps appear when you screenshot from the phone's notification panel rather than opening the full message. The AI extracts whatever visible date text is present — if only "2 hours ago" appears, that is what goes into the Date column. For an absolute calendar date, open the individual message before taking the screenshot.
Do I need a different column set for each bank or retailer?
No. Define the four columns once — Amount, Merchant/Sender, Date, Reference. Bank alerts, order confirmations, delivery tracking, and P2P payment texts all share the same data structure. The AI adapts to each sender's phrasing within the same batch without per-sender rules or templates — that is what makes it different from traditional template-based OCR.
What happens when a screenshot captures only part of the SMS?
Truncated messages — common when you screenshot the notification panel instead of the full text — may miss the Reference number or Amount if that data falls outside the visible window. The AI extracts only what is on screen: a partial message returns partial fields. Screenshot the full message for complete extraction, or check the notification panel first to see what it shows before deciding.
Can the tool extract metadata like the sender phone number, read status, or the original timestamp from an SMS screenshot?
Yes — when those fields are visible in the screenshot. Custom Column Extraction treats the entire frame as data: the sender line (phone number or contact name) in the header, the timestamp in the OS bar or thread header, and visual read/unread indicators like blue dots or dimmed text. Define custom columns for Sender, Timestamp, and Read Status alongside the transaction fields. The AI captures whatever OS-level metadata appears within the visible frame — full-message screenshots naturally include more of these cues than notification panel previews.