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Custom AI Agents AI Knowledge Assistant Custom Software & Apps Private AI Infrastructure All services →AI document processing from Seqora turns the PDFs, scans, phone photos and handwritten forms your team retypes into structured, checked records. We build pipelines that split and classify each file, extract the fields, test them against your rules and tolerances, then draft the report or fill the form. Anything the system can't verify goes to a person with a plain-language reason, and the pipeline is built so nothing leaves your office without sign-off. It's built for dental practices, personal injury firms, lenders and benefits teams that key in the same documents every day.
AI document processing is software that reads business documents, including scans, phone photos and handwriting, and turns them into structured, checked data. It splits and classifies each file, extracts the fields you need and tests them against your rules. Then it drafts a report or fills a form, and sends anything it can't verify to a person with the reason written out.
Most of the information your business runs on arrives as a document: a scanned intake form, a phone photo of a bank statement, a handwritten prescription, a stack of medical records. Someone has to read it, find the fields that matter and type them into another system.
Older OCR tools read clean, typed text. They break on handwriting, skewed scans, multi-document PDFs and forms that change layout from one sender to the next, so the work drifts back to your staff.
Pointing a chatbot at the files doesn't fix it either. A model that guesses a missing figure, or decides on its own who counts as an owner, creates errors that are harder to catch than a typo.
AI extracts, rules decide, people approve. The model reads the documents. Explicit rules you can audit make the decisions. Anything uncertain goes to a person, and the pipeline is built so nothing leaves your office without sign-off.
Extraction errors across the first 30 production cases at 100 Boulevard, per the client's reviewer
100 Boulevard · first production week
Text checks passing in a 216-page audit before six bank-specific document sets went to production
AST Defeasance · release audit
AI cost per handwritten prescription in a dental-lab pilot, at about four minutes each
Pilot cost ledger · 2026
Each part is configured to your own document types, fields and target systems. We start with the document type your team spends the most hours keying in.
Files arrive through an upload link, portal or shared folder. Multi-document PDFs, ZIPs and phone photos are split into typed documents, turned upright and matched to known templates.
Vision models read typed text, tables, handwriting and diagram markings. Fields are scored for confidence and come back blank instead of guessed when the value isn't there.
Extracted values are tested against what you already know: amounts within a set tolerance, names matched across documents, terms checked against your vocabulary. Rules decide, not the model.
Reports drafted in your own voice, extracted fields written into structured records, and your partners' own Word and PDF forms filled field by field, all ready for review.
Anything uncertain lands in a queue with a plain-language reason, like a missing page or an amount outside tolerance, so your team works the exceptions instead of rechecking every file.
Every extraction, rule decision and approval is logged. Pipelines can run in your own cloud account, encrypted in transit and at rest, and your documents are never used to train public models.
We work from your real documents, including the messy ones, not a clean demo set. Each stage ends with something you can check.
We collect a sample of your real documents, map every field your team keys in today, and write down the rules people apply in their heads. You get a scope that names each document type, field and rule.
We run a first version on a batch of your documents and meter every model call for cost and time. You see each extracted field, its confidence score, and where the system would have asked a person.
We encode your rules and tolerances, build the drafting or form-filling step, and set up the review queue and audit log. Automated tests check the output against known-good documents before anything goes live.
The pipeline runs alongside your current process first. We watch the flags with your team and tune a rule or a reader whenever the same reason keeps showing up.
Once the first workflow is stable, we add document types one at a time, each with its own templates, rules and tests against known-good documents. When a sender changes a form layout or a partner changes its forms, we update the reader or the form map and rerun the tests before the change goes live.
Four document systems we've built, from Medicaid proof documents to handwritten lab slips. Figures come from client reviews, production logs and our metered pilot ledger.
Inside 100 Boulevard's existing cloud system, we built an AI validation step that splits uploaded PDFs, ZIPs and phone photos into typed documents and picks the matching proof for each income and asset. It checks each amount against a tolerance and writes a plain-language reason for anything it can't verify.
Zero extraction errors across the first 30 production cases, and 576 cases completed in a single production day.
A secure borrower-onboarding portal where AI classifies W-9s, formation documents, EIN letters and loan agreements and reads org charts. A rule engine decides beneficial ownership with a written reason, and each partner bank's own forms are filled for analyst approval and e-signature.
A second engagement after our first project for them. Six bank-specific document sets went to production after a 216-page audit with all 126 text checks passing.
Clinicians capture the exam on a tablet, a vision model reads the handwritten notes and diagram markings, and the system drafts the report in the practice's own voice. Nothing goes out until the doctor and staff have reviewed it.
Running at the practice on secured AWS infrastructure, and the practice has since referred us to another healthcare client.
A pilot that reads handwritten lab prescriptions with two independent vision models, checks every field against dental terminology, and sends anything below the confidence threshold to review instead of guessing.
About four minutes and under $1 of AI cost per prescription on real handwritten forms, with every model call metered.
Decisions such as who counts as a beneficial owner or whether an amount matches are made by explicit rules, not the model. Every ownership row carries a written reason, and so does any amount that can't be verified, so outcomes can be audited.
Models are instructed to return nothing rather than invent a value, and low-confidence fields go to review instead of being guessed. In our dental-lab pilot, two independent models read every handwritten form.
Our Medicaid validation work is backed by 452 automated tests, and we meter the cost and time of every model call in a pilot, so you know the unit cost before you scale.
| Compare | Manual entry or basic OCR | Seqora AI document processing |
|---|---|---|
| Handwriting and photos | Retyped by hand, or misread by OCR | Read by vision models, with low-confidence fields sent to review |
| Multi-document files | Someone splits and renames them | Split, classified and matched to a template automatically |
| Checking | Staff compare figures by eye | Rules test amounts, names and terms against your tolerances |
| When something's off | Found later, if at all | Flagged at intake with a plain-language reason |
| Output | Data typed into a spreadsheet | Drafted reports, filled forms and updated records, ready for sign-off |
| Audit trail | Whatever people remember | Every extraction, rule decision and approval logged |
Handwritten exam notes drafted into reports for doctor review and lab prescriptions read into structured records, as in our dental work. The same pipeline can prepare insurance and authorization paperwork for staff to check.
DentalBuilt to sort records and bills by provider and date, pull treatment and charges into a structured summary, and flag gaps, all for attorney review before anyone relies on it.
PI lawEntity documents read and mapped onto each bank's forms in the onboarding portal we built for AST Defeasance, and statements, pay stubs and benefit letters matched to what an applicant reported, as at 100 Boulevard.
Pricing is a fixed-scope build, which can start with a short pilot on your own documents, plus optional monthly support. These are the things that move the number:
AI running costs are measured, not guessed. We meter the cost of every model call in a pilot. In our dental-lab pilot, handwritten prescriptions came in under $1 of AI cost each, so you see the unit cost before you commit to a build.
Accuracy depends on your documents, so we build every pipeline to check its own output instead of asking you to trust it. At 100 Boulevard, the client's reviewer found zero extraction errors across the first 30 production cases, but that was one review and we don't promise a percentage. Fields are scored for confidence, rules test values against your tolerances, and anything uncertain goes to a person instead of straight through.
Yes. Vision models read handwriting, diagram markings and phone photos that older OCR tools miss. Pages are turned upright and matched to their template first. For hard forms, we can have two independent models read each page and compare the readings, as we did in our dental-lab prescription pilot. Fields that stay uncertain go to review rather than being guessed.
We plan a pilot on your real documents at about two to three weeks, and a first production workflow at about six to eight weeks from kickoff. The timeline depends mostly on how many document types are involved, whether the output is data or filled forms, and how many systems the pipeline has to read from and write to.
Yes, we sign a BAA when the work touches patient data. Pipelines can run in your own cloud account, documents are encrypted in transit and at rest, access is logged, and your documents are never used to train public models. The Dental Trauma Center system runs on secured AWS infrastructure built for HIPAA workloads. See private AI infrastructure for the details.
Usually, yes. We've built pipelines that take documents from upload links, portals, OneDrive and SharePoint, and write results back into systems like Quickbase or onto bank-specific Word and PDF forms, with e-signature through DocuSeal. If your software has an API, the pipeline writes to it directly. If not, a workflow automation step can move the data.
We need a sample of real documents, including the messy ones, a person who knows the rules your team applies, and access to the systems the output goes into. During rollout, someone on your team works the review queue so we can tune the rules. The pipeline, its rules and its documentation are yours to keep.
Further reading: Things you're still doing by hand (and what it's costing you) · AI agent, workflow, or dashboard: how to decide where AI actually belongs
Book a free call and bring a few sample files. We'll show you which fields a pipeline can read, which rules it would check, and what a pilot on your own documents would involve.