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COMING SOON · THE AI-NATIVE SPREADSHEET

Paperwork in.
Answers out.

The spreadsheet that reads your documents and shows its work.

Join the waitlistSee how it works ↓
Beta members are first in line and always keep our best offer
Invoice matcheremail-connected
3 AI columns running
▤ File
T Counterparty
# Amount
◎ Payment terms AI
◎ Matches PO? AI
44
Invoice_88124.pdfvia email · just now
Nordhaul AS
€2,240.00
41
Invoice_88101.pdfvia email
Semtech Corp.
$12,400.00
Net 30
✓ matches PO-2214
42
CloudProvider_July.pdfvia email
CloudProvider
$3,180.50
Net 60 · over policy
◦ no PO found
43
Freight_inv_0921.pdfuploaded
Nordhaul AS
€1,890.00
Net 14
✓ matches PO-2231
✉ dawids-org+invoices@in.quanty.app → appends herelast write 4 min ago · undoable
invoice classified · 0.97row 43 appended → invoice matcher6 AI columns run · 2,214 tokensattachment filed → /Invoices/2026Net 60 flagged · over policyreply sent · "received, ref #43"low confidence → human review12 stale cells recomputedinvoice classified · 0.97row 43 appended → invoice matcher6 AI columns run · 2,214 tokensattachment filed → /Invoices/2026Net 60 flagged · over policyreply sent · "received, ref #43"low confidence → human review12 stale cells recomputed
ONE SOURCE OF TRUTH

Many inputs. One source of truth.

Emails, PDFs, scans and exports pour in. Quanty reads them all and keeps one verified table your team can trust.

Join the waitlist
ASK YOUR DOCUMENTS

Chat with every document at once.

Describe a job, pick a model, and Quanty shows a plan first. Every answer cites the pages it came from.

Good morning, Dawid.

Describe a job, or pick up where you left off.

Ask anything, or describe a workflow, e.g. "check June invoices from @Invoices 2026 against the VAT whitelist"
+ files▣ library▤ templates
⏎ shows a plan first · nothing runs unreviewed
HOW IT WORKS

Just a spreadsheet. Until you add a column.

This is the actual grid: rows are documents, columns are properties you define, everything one click deep.

Home / Invoice matcherEmail-connected · watching +invoices@
Chat with AIExport
+ Add row+ Add columnFilter rows…
1,204 rows · 8 columnsCompletedRecompute stale (12)Review3PipelineRun
File
T Counterparty AI
# Amount AI
Invoice date AI
Payment terms AI
Edit column
Recompute all cells
Duplicate column
Compare models (A/B)
Pin column
Hide column
Delete column
# Δ vs PO PY
+
1
Invoice_88101.pdf
Semtech Corp.
$12,400.00
2026-07-02
Net 30 p.2
0.00
2
CloudProvider_July.pdf
CloudProvider
$3,180.50
2026-07-04
Net 60 p.4stale
+120.50
3
Freight_inv_0921.pdf
Nordhaul AS
€1,890.00
2026-07-05
Net 14edited
0.00
4
Vector_amendment.pdf
Vector GmbH
$18,220.00
2026-07-08
processing
5
Scan_receipt_003.pdf
Baltika Sp. z o.o.
zł4,520.00
2026-07-09
Net 30 p.1
0.00
Page 1Q3 audit+ Add Page
or email +invoices@
1
Every row is a documentDrop in PDFs and scans, or let email feed the sheet. Rows that arrived on their own carry a small bolt.
2
Every column is a propertyPick a type: text, number, date, select, JSON. Write a prompt in plain language and AI, Python or Web fills it for every row.
3
Click a column to work itRecompute stale cells, compare models A/B, pin or hide. Uncertain values queue for human review instead of writing themselves.
WHO IT'S FOR

Hours of reading. Minutes of reviewing.

Wherever documents pile up, the pattern is the same: Quanty does the reading, your team does the judging.

Explore all industries →
Logistics45× faster
1$1,890matches rate card
2$2,340over rate card
3$980matches rate card
4$1,120no shipment
Freight invoices matched to shipments
Invoices, delivery notes and rate cards meet in one sheet that checks itself.
Accounting38× faster
Inbox
212 invoices
Coding
GL accounts
VAT check
whitelist match
Review
7 exceptions
Export
ledger / CSV
A month of invoices coded in minutes
Coding, VAT registers and completeness checks without retyping anything.
Finance30× faster
1$12,400reconciled
2$3,180no statement
3€1,890reconciled
4$7,250reconciled
Reconciliations with cited proof
Bank statements against the ledger, every match with its source line.
Legal32× faster
01Auto-renewal · 90 days
02Uplift capped at 5%
03Unlimited liability
Clause review across contract stacks
Hundreds of contracts screened for the clauses that carry risk.
Recruiting69× faster
1M. Nowakfit 92
2J. Weberfit 88
3A. Kimfit 61
4P. Silvafit 44
CV screening against one profile
Every candidate scored the same way, each score with its evidence.
Real estate36× faster
Rent_roll_2026.xlsxLease_abstracts.pdfBreak_options.csv
Lease terms across a portfolio
Every lease abstracted into one searchable, exportable table.
45×
faster invoice matching
69×
faster CV screening
0
values without a source
AI COLUMNS

The column is the interface.

A column is a prompt in plain language: pick an output type, write the question, test on one row, run on every document.

Invoice matcher
Run all
▤ File
# Amount
◎ Payment terms ←
1
Semtech_MSA_2026.pdf
$12,400
Net 30 p.2
2
CloudProvider_TOS.pdf
$3,180
Net 60 · flagged p.4
3
Nordhaul_contract.pdf
€1,890
Net 14 p.1
4
Vector_amendment_v2.pdf
$18,220
→ human review · 0.61 ·
1,204 rows · avg 0.8s per row
Payment terms
Type
TextSelectNumberDate
Model
claude-haiku
Prompt@ references other columns
Extract the payment terms from @File. Flag anything longer than Net 45.
Grounding · citations
links every value to its source page
Confidence gate< 0.90 → human review
TEST ON ONE ROW
◰ CloudProvider_TOS.pdfNet 60 · flaggedp.40.94
Save · run on 1,204 rows
same prompt, same format, same model · on row 1 and row 10,000
EVERY VALUE HAS A SOURCE

Click a cell. See the proof.

Every AI-filled cell opens the exact quote, its page and a confidence score. Approve, fix or rerun, one click each.

▤ File
◎ Doc type
# Value
1
◰ MSA_CloudProv_2026.pdf
Master Agreement
$1,240,000 p.4
#Value· MSA_CloudProv_2026.pdf
$1,240,000conf 0.94
p.4 §4.2 · Fees and Payment
"…total fees payable under this Agreement shall not exceed USD 1,240,000 over the Initial Term…"
Open in source viewer →
✓ Approve✎ Edit↻ Rerun
COMING TO QUANTY

Type a NIP. Get registry proof.

Soon every counterparty is checked against live state registries, not just the document. Try it now: one NIP, three registries and an official evidence ID you keep.

Verify
or try an example: 7740001454
Biała lista VAT
Ministry of Finance · wl-api.mf.gov.pl
·
VIES
European Commission · ec.europa.eu
·
KRS
National Court Register · api-krs.ms.gov.pl
·
ALSO ON THE WAY

Registries prove them. The open web completes them.

Verification is just the start. Describe the companies you need and Quanty fills the sheet from the open web: size, locations, people, news, funding. A full profile for every counterparty, not just a VAT status.

Food wholesalers in Wielkopolska, 20 to 200 employees
Complete · 24 companies · 6 columns
COMPANY
CITY
EMPLOYEES
LATEST SIGNAL
Agrohurt Wielkopolska
Poznań
120
opened a cold storage site
FreshLine Trade
Kalisz
45
expanding EU exports
Polfood Serwis
Leszno
80
won a public tender
Danex Dystrybucja
Konin
35
new contract in Germany
+ 20 more rows

In Quanty this runs as a column: every invoice and every counterparty verified automatically, every result logged with proof.

Join the waitlist
live demo · real registries · nothing you type is stored
UNDER THE HOOD

Every answer comes with context.

Quanty links documents, counterparties and values into a knowledge graph. Every cell it fills draws on the context of your whole system, not just one file.

Invoice 88123
Semtech Corp.
PO-2214
MSA 2026
Email thread
Payment
Credit note 114
Shipment SH-88
Rate card Q3
GL 4400
Bank statement 07
Delivery note 4411
VAT register Q3
Invoice 88124
issued bymatchesgoverned byarrived viasettled byfulfilled bypriced bycorrectsposted tosigned withreconciled inconfirmed byreported infollowsprices
AUTOMATIONS

Forward an email. That's the whole setup.

Rules read like sentences: mail arrives, decide what it is, act. Anything uncertain waits for a human. Nothing writes on its own.

← Automations
Invoice intake● Active
Send test emailSave changes
TRIGGER · email arrives
Inbound email
+invoices@in.quanty.app
Only allowlisted senders · PDF attached
CLASSIFY · AI decision
What is this email? AI
Decide: invoice, credit note, something else.
Confidence < 0.8 → human review
BRANCHES · then do
invoicesave file → append row → run AI columns → reply "received"
credit noteappend row · flag negative → notify #finance
something elsehold in Needs review · no writes
dry run passed · nothing is written until you activate
INTEGRATIONS

Email today. Your whole stack next.

The beta starts with inbound email: every sheet gets its own address. More connectors are on the roadmap.

Emailin betaGmailcoming soonOutlookcoming soonMicrosoft Teamscoming soonSlackcoming soonGoogle Drivecoming soon
DUE DILIGENCE

Every source in. Every deliverable out.

Point Quanty at the data room and it reads everything at once. Out come models, decks and alerts in the systems you already use.

Data room
SharePoint
Email inbox
invoices & threads
CRM export
counterparties
Bank statements
CSV / XLSX
Contract stack
MSAs & amendments
Quanty
cross-document reasoning
Financial_model.xlsx
created
Deal_summary.pptx
drafted
Red-flag report
saved to Drive
Email to counsel
sent
#deal-room
notified
WHY NOT JUST A CHATBOT?

One prompt. Two very different answers.

Ask the same question about the same PDF, twice. On the left, a plain chatbot. On the right, the same model inside Quanty.

The full story: why LLMs need this →
What are the payment terms in ◰ CloudProvider_TOS.pdf ?
Without Quanty · plain chatbot
RUN 1
"The payment terms are Net 60. Payment is due within 60 days of the invoice date."
RUN 2 · same question again
"Payment is generally due within about two months of invoicing."
A different phrasing every run. Impossible to sort or audit.No source. You verify by rereading the PDF yourself.Sounds equally confident when it is wrong.
With Quanty
RUN 1
NET_60⚑ flagged · over policyp.4 §2.10.94
RUN 2 · same question again
NET_60identical · served from cache, zero tokens
p.4 §2.1 · "…payments shall be due sixty (60) days from the date of invoice…"
The same typed answer every run: pinned prompt, format and model.Cites page and section. The quote is one click away.Below 0.90 confidence it goes to human review, not into your data.
WHY QUANTY

AI finally reads your paperwork.
Quanty makes it trustworthy.

Pasting your paperwork into a chatbot fails in three predictable ways. Quanty keeps the reading ability and engineers away the rest.

01 · CONTEXT

A model can't hold your filing cabinet in its head.

Paste 300 invoices into a chat and attention thins out: document 214 blends with 209, the key clause slips by, and the tone stays confident.

✕ ONE GIANT PROMPT
Document 3 · read correctly
Document 214 · blended with 209
Document 288 · clause missed entirely
All 300 documents compete for the same attention. Errors hide in the middle.
✓ ONE ROW, ONE DOCUMENT
Row 3 · its own focused read
Row 214 · its own focused read
Row 288 · its own focused read
Quanty gives the model one document and one question at a time. Accuracy does not depend on where the file sits in the pile.
02 · REPEATABLE TASKS

A chat forgets your procedure. A column is the procedure.

In a chatbot you re-explain the task every Monday and get slightly different answers. In Quanty the task is a column: same prompt, same format, same model, on row 1 and row 10,000.

✕ RE-TYPED EVERY TIME
MON "Net 30"
TUE "30 days from invoice date"
WED "about a month"
Three days, three phrasings. You can't sort, filter or audit that.
✓ SAVED AS A COLUMNv4 · pinned
MON NET_30
TUE NET_30
WED NET_30
Same document, same answer. Today and in next year's audit.
03 · TRUST

Confidence is not evidence.

A chatbot answers fluently, right or wrong, and checking means rereading everything yourself. In Quanty every value carries its own proof.

✕ A FLUENT GUESS
"The contract renews automatically on March 1st with a 5% uplift."
No source. No score. Wrong, and it reads exactly like the truth.
✓ A CITED CELL
Renews April 15, 2027 · uplift 3%
◰ Umowa_MSA.pdf · p.12 §9.2confidence 0.98
Click the citation, see the page. Below your confidence bar, it goes to a human instead of the sheet.
IN SHORT

Same model. Different discipline.

A CHATBOT
QUANTY
CONTEXT
All your documents in one prompt, competing for attention
One document, one question per cell
PROCEDURE
Re-typed every time, drifting a little each day
Saved as a column: pinned prompt, format and model
OUTPUT
Prose that changes shape on every run
Typed values that sort, filter and export
PROOF
None. You reread the documents to verify
Every value cites its page; one click to check
WHEN UNSURE
Guesses confidently anyway
Goes to a human review queue, not into your data
RE-RUNS
Full re-read, full cost, new answers
Cached results; only stale cells recompute
LAUNCHING FALL 2026

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