The same values are typed into several places
One result goes into a spreadsheet, a database, and a report, and the copies drift apart.
AI document processing for the files your team retypes by hand.
We read the files and forms that arrive, check every field, write the values to your system of record, and send anything unclear to a person. Your team checks exceptions instead of typing.
Teams we've built for
Every field
Checked before it's written
Values that fail a check wait for a person.
4–8 wks
Typical first release
Most first releases reach users 4–8 weeks from kickoff.
Your repo
Code your team can run
Keep the pipeline and the runbook, or keep us on after launch.
Similar work, already live
“Results get logged automatically when a run finishes now. Before that we were typing them into three different spreadsheets by hand.”
“Honestly the main thing is our inventory numbers are actually right now. It all used to be typed in by hand, now the scanners feed straight into the system.”
“They rebuilt our onboarding, which was half manual and half Zapier flows that kept breaking, and support tickets went way down after that.”
Founded by Colin Burke in 2018.
Process documents with AI without automating mistakes
Data entry looks simple until you count the formats. The same invoice arrives as a PDF, a photo, and an email body. Results sit in three spreadsheets that should agree. Every format needs a rule, and every rule has exceptions.
Models now read documents well, but a fast system that writes wrong data is worse than a slow person. We check every field before it's written, record where each value came from, and send low-confidence cases to a person.
Signs data entry is costing you
Good candidates arrive in volume, follow a known structure, and end up in a system you control.
Documents arrive in mixed formats
PDFs, scans, photos, and email bodies carry the same fields in different places.
Errors show up weeks later
A typo in a total or a date is found by a customer or an auditor.
Busy periods create a backlog
Month-end, peak season, or a new contract leaves a pile of files nobody has time to enter.
How a data entry project runs
We measure accuracy on your real documents before anything writes to a live system.
- / 01
Collect real samples
We gather recent documents of every type and format, including the messy ones.
- / 02
Define each field and check
For every field: where it comes from, what a valid value looks like, and what happens when it isn't valid.
- / 03
Test on files it hasn't seen
We measure accuracy on held-back files and set the confidence level below which a person reviews.
- / 04
Go live with review
Low-confidence fields go to a review queue. The threshold changes only when the numbers support it.
What you get
An intake pipeline with checks and a review queue, connected to your systems.
- Intake from email, upload, shared drives, or an API
- Field extraction for each document type you receive
- Validation against your rules and your existing records
- A review queue for unclear or low-confidence fields
- Writes to your database, ERP, CRM, or spreadsheet, with a source link for each value
- Accuracy reports, monitoring, and a runbook
Accuracy before speed
The goal is fewer errors than manual entry, not only less typing.
Every value has a source
Each entered field links back to the document and page it came from, so a reviewer can check it in seconds.
People handle the doubtful cases
When a field is unclear or fails a check, it waits for a person. It's never written as a guess.
Data entry teams automate first
High volume, a known structure, and a clear destination.
Invoices and receipts
Supplier, amounts, dates, and line items entered into your accounting system and matched to purchase orders.
Order and intake forms
Orders, applications, and requests entered into the system that fulfils them.
Lab and test results
Results logged automatically when a run finishes, instead of typed into spreadsheets.
Scans and stock counts
Scanner and device data written straight to the system of record.
Three fixed-price ways to start
Start with something real users can try. Then decide if it's worth going bigger.
Sprint
1 week$5,000
Test extraction on your real documents and ship one working intake path.
MVP
~1 monthFrom $20K
One document type end to end: intake, extraction, checks, review queue, and system writes.
Scale-up
2+ months$75K–$250K
Several document types or sources, higher volume, and audit requirements.
Questions before we start
How accurate is AI document processing?
It depends on your documents, so we measure it on them. We test on files the system hasn't seen and set the review threshold from the results before anything goes live.
Can it read scans and handwriting?
Scans and photos usually work. Handwriting varies. We test your samples first and tell you which ones need a person.
Where does the data go?
Into the system you already use: a database, ERP, CRM, accounting tool, or spreadsheet. We connect to it. We don't replace it.
Is our data used to train models?
No. We use model providers and settings that don't train on your data, and we can run models in your own cloud when the data requires it.
Which documents can it process?
Invoices, receipts, purchase orders, forms, applications, contracts, lab results, and most other files with a repeatable structure. We test your real samples before we agree the scope.
How much does AI document processing cost?
A one-week sprint is $5,000. One document type end to end usually fits the MVP tier, from $20,000. We agree the price before we start.
galactus / contactaccepting projects
Send us a sample of what your team retypes.
Tell us the document types, the volume, and where the data ends up. We'll tell you how we'd automate it.