Turn PDFs into structured, validated data with AI
By Team DwellFi
AI table extraction pulls structured fields out of a PDF, then does the part a spreadsheet formula never could: it goes and verifies each figure against your own systems and outside sources. Give it 500 invoices and it reads the vendor, date, amount, and PO number from every one. Then it checks each rate against your contracts and each vendor against a public registry, and hands back a single validated table with the exceptions already flagged. Extraction gets the data onto the page. Validation is the part that catches the money.
Most fund-ops teams already know the extraction half. You have seen a tool turn a capital call notice into a row. The half that still eats your evening is the checking, and that is the half this is about.
Key takeaways
- Extraction gets the data onto the page. Validation is the half that catches the errors.
- A formula compares what you put in front of it. An agent goes and finds what you forgot to check.
- Click any cell and the source document opens behind it.
- Exceptions surface before a single payment moves.
What AI Tables means once you get past extraction
Pulling fields into a spreadsheet is the easy 40%. It leaves you holding the hard part, which is proving every number is right. You still have to open the master agreement, find the rate, and compare. You still have to confirm the PO sits inside budget. You still have to check whether that vendor's registration is current or lapsed three weeks ago.
AI Tables folds that work into the same pass. After it extracts a row, it augments the row with columns that require going somewhere and looking something up. Does this rate match the master services agreement? Is this PO inside budget? Is this vendor still registered? A formula cannot answer any of those, because answering them means reading other documents and querying other sources. The agent reads them for you.
How the validation actually works
Start with the 500 invoice PDFs. The system extracts vendor, date, amount, and PO number from each one. Then you tell it, in plain English, what to verify: check the rate against the contracts folder, confirm the PO amount, flag anything over budget, and look up each vendor's registration on the public registry.
So it reads your contracts. It cross-references your PO database. It goes out to the public web for the registrations. What comes back is one table, every cell linked to the document it came from, exceptions already flagged. A handful of invoices billing above the contracted rate. Two vendors with lapsed registrations. A PO sitting over budget. In DwellFi's own worked example, that run surfaces 16 flagged exceptions in about four minutes, every one caught before payment.
That is not a demo trick. It is the same mechanic behind pulling 11 capital calls out of a folder in seconds, or reading a 999-page PDF in under three minutes. The volume is the point. The verification is the difference.
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Why a spreadsheet can't do this
A formula is reactive. It compares values you have already lined up for it, and it does that fast and well. What it cannot do is decide to go find the contract, open it, locate the relevant rate, and check the invoice against it. It will never notice on its own that a registration expired last month. The distance between a lookup and an agent is simple: the agent does the lookup, the verification, and the digging you would otherwise do by hand, at two in the morning, across seven folders.
What kind of work this replaces
Any process shaped like "extract the data, then check it against something else." Invoice and AP validation. KYC document checks. Reconciling a data set against a contract. Building an audit-ready table out of a pile of source documents. If the job today is a long evening in Excel with the contracts folder open in a second window, this is the job.
Why the output holds up in an audit
Because every value carries its provenance. Click a cell and the source document opens. The exceptions are not just flagged, they are explained and traceable back to the line that triggered them. The table you export is structured, and it is defensible. In fund operations, defensible is the only kind of structured that counts.
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Frequently asked questions about AI tables
Can AI cross-reference external sources?
Yes. It queries public registries and the open web, checks extracted data against them, and flags any mismatch. DwellFi's platform reaches sources like the SEC's EDGAR database and Form ADVs, alongside your private documents.
What formats can it ingest?
PDFs, scans, images, emails, and spreadsheets, including workbooks running 50-plus tabs. Handling the format and the volume is the whole point.
Can I define my own schema?
Yes. Name your columns, set the data types, and write your own validation rules. Or point the system at your documents and let it propose the schema, then edit inline as you go.
Bring a stack of PDFs. Leave with a validated table.