Extract documents.Flag, don’t guess.
Most AI extractors return a confident answer for every field — including the ones they got wrong. A misread total or date flows straight into your books, and nobody notices until it’s a problem.
This kit turns invoices, receipts, and forms into clean structured data — and flags the fields the model isn’t sure about, so a human checks only what’s risky. Speed on the easy fields, a human-checked safety net on the rest.
The danger with most AI extractors.
They return a confident answer for every field — including the ones they got wrong. A misread total or date flows straight into your accounting, and nobody notices until it’s a problem. The fix isn’t more confidence; it’s honesty about uncertainty.
This kit rates every field, validates the result, and flags what’s shaky for a human — so you get the speed of automation on the easy fields without trusting a tool that quietly guesses on the hard ones.
Most extractors return a confident answer for every field — including the ones they got wrong. There's no signal that a value is shaky, so nothing tells you to look.
A misread total or date flows straight into your accounting, and nobody notices until it's a problem — a reconciliation that won't close, a payment that's off.
Totals, dates, and handwriting are exactly where these models quietly fail — the high-stakes fields, extracted with the same false confidence as the easy ones.
Pick a document. Watch what needs a human.
Each field comes back with a confidence rating and a status. Watch the extractor mark what it’s sure of — and flag what it isn’t. The same flag logic the shipped extract.py runs. (Illustrative output; it resets on reload.)
| Field | Value | Conf. | Status |
|---|---|---|---|
| invoice_number * | INV-2043 | high | ok |
| invoice_date * | 2026-03-07 | high | ok |
| vendor_name * | Acme Supplies Inc. | high | ok |
| total_amount * | 1284.00 | low | REVIEW |
| currency | — (none) | low | ok |
| line_items | — (none) | low | ok |
Verdict: 1 field(s) need review
The flagged fields go to a human; the rest you can trust. The extractor never silently guessed.
The kit ships this as runnable code.
extract.py (zero dependencies, runs keyless in --demo mode), editable schemas, and a QA tracker — so you automate the easy fields and catch the risky ones, and the CLI exits non-zero when any field needs review.
Get the AI Document Extraction Kit — $99Illustrative output for orientation — real confidence and accuracy depend on the document and model.
Rate it. Validate it. Flag it.
Three steps stand between a model’s guess and your books. The whole kit is built to enforce them.
Every extracted field comes back rated high, medium, or low — so uncertainty is visible instead of hidden behind a single confident-looking answer.
Dates parse, numbers are numeric, required fields are present. A value that fails its type or is missing is caught before it reaches your systems.
Anything low-confidence, missing, or invalid is marked REVIEW for a human — never silently accepted. You check only what's risky, not every document.
One command, no API key, a real verdict.
extract.py has zero third-party dependencies and a --demo mode that runs with no API key. It exits non-zero whenever a field needs review, so it drops straight into a pipeline. Here’s the actual captured output:
$ python extract.py --demo Document: (built-in sample invoice) Schema: invoice FIELD VALUE CONF STATUS ------------------------------------------------------------------ invoice_number INV-2043 high ok invoice_date 2026-03-07 high ok vendor_name Acme Supplies Inc. high ok total_amount 1284.0 low REVIEW - low confidence currency low ok - absent (optional) line_items low ok - absent (optional) ------------------------------------------------------------------ Verdict: 1 FIELD(S) NEED REVIEW $ echo $? 1
The low-confidence total is flagged REVIEW and the command exits 1 — so auto-accept happens only when every field validated and was confident.
A playbook, runnable code, schemas, and a tracker.
How LLM extraction works, designing a field schema, the validate-and-flag pipeline, prompt templates, and an honest look at accuracy and limits.
A reference extractor: loads a schema, prompts a model, validates, and flags low-confidence fields. Zero third-party dependencies; --demo runs with no API key and exits non-zero on review.
Ready-to-edit schemas for invoice, receipt, and generic documents — define your fields, types, and which are required.
A documents log, field-level QA, and a live dashboard for flag rate and sampled accuracy — so you keep extraction honest over time.
Four steps, then it runs itself.
Start from the invoice / receipt / generic JSON schemas; define your fields, their types, and which are required.
Pipe in a document (or use --demo). extract.py prompts the model, parses the result, and rates each field's confidence.
Type checks and required-field checks run automatically; anything low-confidence, missing, or invalid is marked REVIEW.
Auto-accept only when everything passed; route flagged docs to a human. Log flag rate and sample-audit accuracy in the tracker.
No accuracy guarantee.
This kit makes no accuracy guarantee. Models misread messy documents — totals, dates, handwriting — which is the whole reason it validates and flags. Anyone promising you a fixed accuracy number is hiding the fields it got wrong.
So always validate extracted data before it enters financial, legal, or operational systems. Keep a human in the loop for flagged or sensitive documents. And sample-audit a share of the auto-accepted ones regularly — the tracker is built for exactly that, so accuracy stays honest over time instead of drifting unnoticed.
Teams turning paper into data.
Operations, finance, and admin teams at SMBs processing repetitive documents — and builders who want a clean, honest extraction pattern to start from rather than a black-box API. No engineers required to see the workflow; light scripting comfort to wire it into yours.
AI Support Deflection Kit
$89The same honest-automation spine for customer support: deflect the repetitive tickets, escalate the rest, always offer a person. Extraction flags the fields it isn't sure of; deflection escalates the questions it shouldn't answer. Two halves of the same discipline.
AI Data Analysis Starter Kit
$79Once documents are structured data, this turns that data into answers — a master prompt, focused prompts, and a five-rule honesty layer that keeps the analysis defensible. Extraction feeds it clean rows; this reads them.
Margin Leak Auditor
$79Another runnable, honest auditor — it sweeps your deal book for realized margin below floor and names the deals losing money. Same shape as this kit: stdlib Python, run-verified output, a verdict you can trust.
The questions ops and finance teams actually ask.
A kit for turning documents — invoices, receipts, forms, contracts — into structured data with AI, while flagging low-confidence or missing fields for review. It includes a runnable reference extractor (Python), ready-to-edit field schemas, a QA tracker, and a playbook.
Every field comes back with a confidence rating. Missing required fields, type failures, and low-confidence values are marked REVIEW so a human checks only those — not every document. An extractor that silently guesses can put a wrong total straight into your books; this one won't.
Light comfort with running a script helps. extract.py has zero third-party dependencies and a --demo mode that runs with no API key, so you can see the workflow first. Non-developers can still use the schemas, tracker, and playbook.
Accuracy depends heavily on document quality and layout, and models can misread totals, dates, and handwriting — which is exactly why the kit validates and flags. Sample-audit a share of auto-accepted documents to track real accuracy over time. The kit makes no accuracy guarantee; the whole design is about catching the misses, not pretending they don't happen.
Yes. extract.py exits non-zero whenever any field needs review (and on errors), so it gates a workflow cleanly — auto-accept only when everything validated and was confident, and route the rest to a human. It loads a JSON schema, prompts a model (set ANTHROPIC_API_KEY for live use; the included model string is current and editable), validates the result, and flags.
An Extraction Playbook (how LLM extraction works, designing a schema, the validate-and-flag pipeline, prompt templates, accuracy and limits); extract.py (the runnable extractor); extraction-schemas.json (invoice, receipt, generic — editable); and an Extraction QA Tracker (documents log, field-level QA, and a flag-rate + sampled-accuracy dashboard).
Operations, finance, and admin teams at SMBs processing repetitive documents — and builders who want a clean, honest extraction pattern to start from rather than a black-box API that guesses.
Fast extraction with a safety net.
Automate the easy fields; flag the rest for a human. A runnable extractor that validates and flags, editable schemas, and a QA tracker. One-time $99, yours to keep.
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