Quotes as data you can compare
A quote — or estimate, or quotation — is a proposal: what something will cost before any order is placed. Whether you are the one sending it or the one weighing several against each other, the useful part is the numbers, and they arrive trapped in a PDF. FlowParse reads each quote with AI and exports clean Excel rows, so you can compare, track and convert quotes instead of squinting at attachments.
This is the pre-order sibling to converting invoices and purchase orders. A quote is the first document in the sales cycle, and getting it into a spreadsheet early is what lets you line up supplier options, forecast a pipeline, or roll an accepted quote straight into an order without re-keying it.
The result is a structured table — the header fields in their columns, every line item with quantity and unit price, and the subtotal, tax and total — ready to sort, compare and build on.
What FlowParse pulls from a quote
FlowParse reads a quote by meaning, so it captures the fields that matter across any layout: the quote or estimate number and date, the validity or expiry date, the supplier and customer, each line item with its description, quantity and unit price, and the subtotal, tax and grand total.
The validity date is a field quotes carry that invoices don't, and it matters — a quote is only good until it expires. Capturing it as its own column lets you see at a glance which proposals are still live and which need re-quoting, which is impossible when the date is buried in the PDF body.
Unit prices come through as real numbers, so a like-for-like comparison across suppliers is a sort away rather than a manual read-off.
Comparing supplier quotes side by side
The most common reason to get a quote into a spreadsheet is to compare it with others. Three suppliers quote the same job, each in their own format, and the only way to choose well is to line the numbers up. FlowParse converts each quote to the same structured columns, so a comparison that used to mean flipping between PDFs becomes one sheet you can sort by total or by line.
Because the line items are structured, the comparison goes deeper than the bottom line. You can see which supplier is cheaper on which item, spot a line one quote includes and another omits, and catch the quote that looks lowest overall but excludes delivery. That is the difference between choosing on a headline figure and choosing on the real cost.
For a procurement decision that has to be justified, a clean comparison table is also the evidence — the reasoning is visible and repeatable, not a gut call from a stack of attachments.
How to convert a quote to Excel
Upload the quote
Drop in one or many quotes or estimates — digital or scanned — from any supplier.
Let AI extract it
The quote number, validity, line items, unit prices and totals are read in seconds.
Review the preview
Check the editable preview; low-confidence fields are highlighted for a quick correction.
Export to Excel
Download a clean .xlsx — or CSV, Google Sheets or a file to build an order from.
Tracking quotes as a sales pipeline
For a business that sends quotes, each one is a potential sale, and a structured log of them is a pipeline. Converting your outgoing quotes to Excel gives you a table of every proposal — customer, value, date sent, validity — that you can total, filter by status and watch convert or expire.
That turns quoting from a scatter of sent PDFs into a forecast. Sum the open quotes for a weighted pipeline value, filter for those expiring this week to chase, and see your win rate by comparing accepted to sent. None of that is visible while the quotes live only as documents in an outbox.
It also feeds cash-flow planning: a pipeline of dated, valued quotes is the leading indicator of the revenue that invoices will confirm later.
Every line and unit price captured
A quote's detail lives in its line items — the breakdown of what is being priced and at what rate. FlowParse captures each line with its description, quantity and unit price in its own row, so the make-up of a quote is data you can analyse, not prose you have to read.
That line-level structure is what powers real comparison and real order-building. You can total by category, check a unit price against a previous quote, or pull the exact lines a customer accepted into an order — all of which need the lines as rows, not as a formatted block inside a PDF.
Turning an accepted quote into an order
When a quote is accepted, its content becomes the order — the same lines, quantities and prices, now committed. If the quote is a PDF, that means re-typing it into a purchase order or sales order, with every transcription error carrying straight into the deal.
With the quote already structured in Excel, the accepted lines become the order in a copy rather than a re-key. The prices you agreed are the prices that get ordered, because they are the same data — no drift between what was quoted and what ends up on the purchase order or invoice.
Convert many quotes at once
Whether you are collecting bids or reviewing a season of proposals, quotes come in batches. Upload a set and each is read into the same spreadsheet, so a round of supplier bids or a quarter of your own quotes becomes one table to sort and analyse.
For a consolidated view, Smart Merge combines many quotes into a single dataset with a source-file column, so a full comparison across a dozen bids — or a pipeline across a quarter of proposals — totals in one place rather than a dozen files.
That makes a bid analysis or a pipeline review a minutes-long job instead of an afternoon of copy-paste.
Keeping track of what's still live
A quote has a shelf life. Prices are held only until the validity date, after which they may change or lapse. When quotes sit as PDFs, that expiry is invisible until a customer tries to accept a stale price or a supplier withdraws one you were counting on.
By capturing the validity date as a column, FlowParse makes the live-versus-expired status a filter. You can chase quotes about to lapse, re-request ones that have, and avoid the awkwardness of building an order on a price that is no longer on offer. The pipeline stays honest because its dates are visible.
Reading PDFs versus structured rows
Comparing or logging quotes by hand means opening each PDF, reading off the totals, maybe the key lines, and typing them into a sheet — per quote, for every comparison and every pipeline update. It is slow, and a mis-read unit price can steer a purchasing decision the wrong way.
| Task | By hand | With FlowParse |
|---|---|---|
| Compare three supplier quotes | Flip between PDFs, read off totals | Sort one structured table |
| Track a quote pipeline | Re-type each into a log | Batch export to one sheet |
| Build an order from a quote | Re-key the accepted lines | Copy the structured rows |
| See what's expired | Hunt for the validity date | Filter the validity column |
Excel, CSV, Sheets or your CRM
Excel is the default, and the same extraction also produces a CSV to import into a CRM or ERP, a live Google Sheet for a shared bid review, or structured JSON via the API to feed a sales system automatically.
One extraction, every destination — so a quote can populate your comparison sheet, your pipeline tracker and your CRM from a single upload, without processing it three times.
Scanned and emailed quotes too
Quotes arrive in every form — a polished PDF, a scanned letter, a photographed hand-written estimate from a tradesperson. FlowParse runs OCR on scanned and photographed quotes first, then structures the text and flags low-confidence fields for a quick check.
That means even an informal estimate scribbled on a supplier's letterhead becomes comparable, structured data. You are not limited to comparing only the suppliers who send tidy digital quotes.
Accurate numbers for real decisions
A purchasing decision is only as good as the numbers behind it, so accuracy matters. FlowParse reaches around 98% field-level accuracy on standard layouts and cross-checks that the line amounts and tax sum to the stated total, flagging anything that doesn't reconcile in the editable preview.
You confirm the data before it exports, so the comparison you base a decision on is data you have verified — not a figure a quick glance at a PDF might have misread.
Estimates, quotations and proposals
The document goes by many names — quote, quotation, estimate, proposal, bid — and the exact fields vary a little between a builder's estimate and a formal corporate quotation. FlowParse reads them all as the same underlying type: a proposed price with line detail and a validity, mapped to consistent columns whatever the heading says.
That consistency is what lets you compare a formal quotation from one supplier against a rough estimate from another on equal terms. The presentation differs; the structured data doesn't.
Your documents stay yours
Quotes reveal pricing and customer detail, so uploads run over TLS, processing is EU-hosted, and the original PDF is deleted immediately after processing. Your documents are never used to train AI models, and nothing is retained once your export is produced.
There is no store of your quotes and pricing left behind for anyone to see — the data exists only long enough to produce the spreadsheet you asked for.
A cleaner sales and procurement workflow
For sales and procurement teams, quotes are the raw material of the job, and keeping them as PDFs means the analysis everyone actually needs — compare, forecast, convert — is always a manual re-read away. Structured quote data folds that work into the tools the team already uses.
A pipeline that updates from a batch export, a bid comparison built in a sheet, an order created from an accepted quote without re-keying: these are the small efficiencies that add up to a faster, more accurate sales cycle, and they all start with getting the quote out of PDF.
Reading discounts and optional lines
Quotes are where sellers get creative — a headline discount, a bundled option, an 'if you also take this' line, a tiered price by quantity. Those structures are exactly what make two quotes hard to compare, because the real cost hides inside the presentation.
FlowParse captures the lines as they are, including discount and optional lines where the quote separates them, so the true net of each proposal is visible in the data rather than buried in the layout. An option a supplier hoped you'd overlook shows up as its own row, and a discount that only applies at a quantity you won't order is there to be scrutinised.
Structured this way, a comparison rewards the genuinely cheaper quote rather than the most cleverly formatted one. The numbers do the talking, which is what a fair procurement decision needs.
Tracking quote revisions over time
Quotes rarely stay still. A supplier re-quotes after a negotiation, a scope changes, a price is held for another month. Each revision is a new PDF, and without structure the history is a scatter of near-identical documents you can't easily line up.
Converting each revision to structured rows lets you keep a version history you can actually read — what changed between quote v1 and v3, which lines moved, whether the discount improved. That record is useful in the negotiation itself and afterwards, when you want to show how a final price was reached.
It also protects against confusion at acceptance. When several versions of a quote exist, working from the structured latest version — dated and validity-checked — is what stops an order being built on a superseded price.
Who converts quotes to Excel
Procurement teams comparing supplier bids, sales teams tracking a pipeline of proposals, small businesses weighing estimates before they commit, and anyone who has three quotes open and needs the numbers side by side to choose.
If quotes are how money enters or leaves your business, getting them into clean Excel rows is what turns a pile of proposals into a decision you can make quickly and defend later — compared on the real numbers, not the headline figure.
Convert your quotes to Excel
Upload a quote or estimate and get clean, comparable rows — line items, unit prices, validity and totals, ready to compare and convert.