Healthcare
Build an AI-powered document parser
Kliv is an AI app builder. Describe the document parser you need, and it builds an intake app where originals are kept, extracted fields wait for review, and verified data lands in tables.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a document-parsing product you configure from a fixed form — it’s an AI that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the storage, the queue, the tables, the review flow, and the screens. What you get is a real app that’s yours. You can run it on your own accounts, keep the source in your own Git repository, and change it later by asking for changes. It isn’t a template.
On this page, that app is a document intake pipeline. Kliv builds all kinds of web apps; this is one example.
Why build your own document parser?
Document parsing is useful only when the output can be trusted. In a clinic group, a referral is not data just because a field was extracted from a scan. Someone still has to confirm the provider, visit count, diagnosis code, and expiry date.
Kliv can build the full intake app around that fact: receive the document, keep the original, propose fields, let a human review them, and only then write verified rows that scheduling and reporting can use.
Paper in, columns out
The document passes through clear stages before it becomes trusted data.
The intake shelf
Every arriving document is stored as its original file, private by default, with a lifecycle status on the record. The original is never edited in place.
The index
Each scan gets a machine description and index entry when it lands, so last month’s referral can be found by what it contains.
Proposed fields
Extracted values sit beside the source image as proposals. A proposed value is not treated as a fact.
The review queue
Coordinators confirm or correct each field with the original in view. Every confirmation has a name and timestamp.
Verified tables
Confirmed values become referral rows with provider, visit count, expiry date, and whatever else the app needs for scheduling.
The audit trail
The record shows who confirmed what, when, and from which document. The source image stays one click from every row it produced.
An example: Marisol’s clinic intake team
Here’s how one clinic group might use a document parser built with Kliv. It’s only an example — you would describe your own documents, fields, review rules, and billing if any.
A fax arrives as an original
A referral lands, is stored as the original scan, and is indexed. No field has been treated as verified yet.
The app proposes fields
Ten values appear beside the scan. Nine look confident, and the handwritten visit count is flagged for review.
A coordinator signs off
A coordinator confirms nine fields and corrects the visit count from 8 to 6 while looking at the scan. Her name and the time go on the record.
Verified data drives scheduling
The referral row now exists. The expiry date can trigger reminders, and the visit count can decrement as appointments complete.
A sister clinic is billed from the meter
If another clinic uses the pipeline, page processing can accrue on the same meter that creates the monthly invoice, such as 22¢ per page.
Describe what you want to build
Kliv builds from your description, so the more detail you give, the closer the first version. Name the documents, the fields, the review step, and where verified data should go. Here are three to build on:
Referral intake parser
Faxes become verified referral rows.
“Build a referral intake app for a five-clinic therapy group. Upload faxed referrals, keep the original scan, propose patient name, referring provider, diagnosis code, authorized visits, and expiry date, require a coordinator to confirm each field, and send verified referrals to the scheduling table.”
Lab report inbox
Reports route to the right clinic.
“Build a lab report inbox where incoming PDFs are indexed, routed to the ordering clinic, and key values such as patient name, collection date, test name, and result wait for reviewer approval before appearing on the patient timeline.”
Invoice review queue
Supplier bills proposed, then approved.
“Build an invoice intake tool where supplier PDFs become proposed line items with vendor, date, subtotal, tax, and total. A bookkeeper approves or corrects each field, and approved invoices export as clean rows.”
What does the reading
Kliv contributes the file store, image description, semantic indexing, and typed server-side functions. The field-by-field extraction pipeline is your app’s own logic, built to your spec in conversation. Whatever proposes a value, nothing skips the review queue.
Proposals stay separate. Unconfirmed values live in their own tables and cannot join clinical workflows by accident. The boundary between guessed and verified is structural.
Usage can be priced. If sister organizations use the pipeline, processing can accrue on a meter and the monthly invoice can come from that same meter, such as 22¢ a page, with payment through your own Stripe account.
The source can come with you. The app source syncs to a repository under your name, so you can extend the pipeline in your own editor and keep Kliv in step.
For the wider category of supervised AI applications, see the AI-apps solutions page.
Common questions
What exactly is Kliv?
Kliv is an AI that builds custom web apps from a description. For document parsing, it can build the intake app, file storage, review queue, verified tables, and audit trail around your documents.
Do I get a real app, or a template?
A real app. Kliv builds the records, screens, extraction flow, review queue, and permissions for your process. It is not a generic upload form.
Can I change it after it’s built?
Yes. Ask for new fields, a different review rule, another clinic role, or a new export. Kliv updates the app.
Does Kliv itself read the documents?
Kliv provides platform capabilities such as file storage, image description, semantic indexing, and server-side functions. The extraction logic belongs to your application and is refined from your description.
Do extracted values become data automatically?
No. They stay proposals until a reviewer confirms them. Only confirmed values move into verified tables.
How is patient information protected?
Originals are private files by default, and queues and tables can be scoped by role and clinic. A coordinator sees the intake records they are allowed to see.
What happens when a field is wrong?
The reviewer corrects it with the scan in view. The record keeps the correction, reviewer, timestamp, and original image.
Can verified data feed scheduling?
Yes. Once a referral is confirmed, fields such as expiry date and visit count can drive scheduling reminders and visit balances.
Can we bill other clinics for using it?
Yes, if that is part of your app. You can meter processing per page or document and bill through your own Stripe account.
Can I search old documents?
Yes. Scans can be described and indexed when they arrive, so old documents can be found by what they contain.
Can I move off Kliv later?
Yes. The app source can sync to your own Git repository, and the records and files remain under your control.
Can it handle non-healthcare documents?
Yes. The same pattern can handle lab reports, invoices, applications, or any document type where proposals should be reviewed before becoming data.
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Stop retyping the same documents
Describe the documents, fields, reviewers, and verified tables you need. Kliv builds the parser and the review system around them.