AI use case · Is your data ready for document intelligence?Extracting the data is easy. Trusting it is the work.
Document AI promises to read every contract and invoice and fill your systems for you. Reading is the easy part. The hard part is matching what it reads to the right supplier, customer and contract, picking the latest version, and proving where each value came from.
Starts with a 30-minute call. Commits you to nothing. This use case is assessed with your team, not from exports alone.
Assessed with your team, not from exports alone
Documents can’t be judged from a spreadsheet export. We assess document intelligence with your team: a working session on a sample of the real documents, alongside exports of the records the extracted values have to match.
What it needs
What document intelligence needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostLineageAccuracyThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where document intelligence breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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Documents ↔ Finance
Supplier names on invoices that match no supplier record
What it does to the AIExtracted invoices land on new, duplicate suppliers.
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Documents ↔ CRM
Signed contract terms that differ from the CRM record
What it does to the AISomeone has to decide which is right; the AI will pick one silently.
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Documents ↔ Documents
Several versions of the same contract
What it does to the AITerms extracted from a superseded draft.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
What accuracy do you measure per field, not per document?
A document can be 95% right and still have the wrong amount. The fields that matter need their own numbers.
- Q2
What happens when an extracted name matches no record?
Creating a new record is the easy default, and the fastest way to duplicate your supplier master.
- Q3
Do you keep a link from each value to the page it came from?
Without it, nobody can check an extraction, and auditors won’t accept it.
- Q4
Who reviews low-confidence extractions, and is that logged?
The review queue is where the cost lives. Ask how big it will be on your documents.
How a Truth Score Read tests it
Document intelligence, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Scope it with your team
A working session on a sample of your real documents: formats, versions, and which fields matter to the decision.
- 02
Match to the records
Exports of suppliers, customers and contracts, so extracted values can be tested against what your systems hold.
- 03
Test the trail
Whether each value can be traced to its page and its latest version, and who would check it.
- 04
Give the verdict
Which document types are ready now, which need work, and which should stay manual.
This use case includes a working session with your team; the memo’s scope line says exactly what was reviewed.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Document intelligence, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callCan you assess document intelligence from exports alone?
No. Documents have to be looked at, so we assess this use case with your team in a working session on a sample, alongside exports of the records the extracted values must match. We say so in the scope line of the memo.
Why does lineage matter for document AI?
Because an extracted value is only useful if someone can check it. A link from each value to its source page and version is what lets finance, legal or an auditor trust the output.
Can AI extract data from inconsistent documents?
Modern tools cope with varied layouts far better than older ones. The harder problem is downstream: matching what was read to the right record, and knowing which version of a document is current.
Do you need to keep copies of our documents?
No. We review a sample with your team under NDA and keep findings and quality metrics, not your documents. Raw material is deleted after the engagement.
Is your data ready for document intelligence? Find out before you sign.
A working session with your team, exports from the systems behind it, and a signed verdict. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.