AI document processing for invoices and contracts
Hashlogics builds AI document processing that reads invoices and contracts, checks every figure, posts clean ones to your ERP, and sends exceptions to a person.
- 4.8 on Clutch across 22 reviews
- PremiumAudit.io: audit cycle cut 75%, calculation errors cut 95%
- In production for SmartREI, Tankaware, ZyloBase, KiwiWrite
Reading a document is the easy part now. The work that decides whether you can trust the result is the check that comes after it. Every total, line and party name gets matched against your purchase orders, your vendor list and your contract rules. A clean document posts on its own. A document that fails a check goes to a person, with the failed rule named, so they fix one field instead of retyping the page.
Should you buy a document AI product or build a pipeline?
Start with a product if it fits. ABBYY, Rossum, Nanonets, Azure Document Intelligence and AWS Textract all read invoices and forms, and most teams should test one before paying for a build. If your documents are standard invoices and your checks are simple, a product plus your accounting system's own import may be all you need.
A custom pipeline pays when the reading is not the hard part. That is usually true in three cases. Your checks need data that lives in your own systems, such as open purchase orders, rate cards, well numbers or policy terms. Maybe your documents aren't invoices at all: bills of lading, field tickets, loss runs, certificates of analysis. Or the result has to post into a system the product does not write to, with your approval rules in front of it.
In a custom pipeline we often still use one of those products as the reading step. You pay for the part that is specific to you: the checks, the review queue and the posting. Our list of document parsing APIs compares the reading options, and the glossary entry on intelligent document processing defines the terms.
- CaptureEmail inbox, upload or scanner.
- ReadModel pulls fields and line items.
- CheckMatched to POs, vendors and your rules.
- ReviewFailed checks go to a person.
- PostWritten to your ERP with an audit trail.
Five stations. A document only reaches Post after every check passes or a person approves the fix.
What does the pipeline do at each step?
Capture takes documents from wherever they arrive today: a shared inbox, a portal upload, a scanner folder or an API. Each one gets an ID and a copy of the original, so you can always see what came in.
Extraction is a model reading the page. We use the Claude API, the OpenAI API, Azure Document Intelligence or AWS Textract, picked by testing them on your documents. Each field comes back from the model with the place on the page it came from.
Validation is where your rules live. Does the vendor exist in your system? Do the lines plus tax equal the total? Does the invoice match an open purchase order and a receipt? Is the rate the one in the contract? Each check passes or fails on its own, and each failure has a reason a person can read.
Review is a queue for your team. A reviewer sees the document, the extracted fields and the failed check side by side. They fix the field or reject the document. Each fix is logged, and it becomes test data for the next round of tuning.
Posting writes the clean result into NetSuite, QuickBooks Online, SAP or the system you run. It writes through the system's own API, with the same approval steps your team uses now.
How accurate is AI invoice extraction on your documents?
Nobody can give you an honest accuracy number for documents they have not seen. So we measure it on yours before anything goes live.
A test set from your own files
You give us a sample of real documents, including the messy ones. Your team marks the right answer for each field. That set is the yardstick for every model and every change.
Measured per field, not per page
An invoice can have every field right but one and still post the wrong total. We report accuracy for each field you care about, so you see where the risk sits.
You set the bar for posting on its own
You decide which fields must pass before a document posts without a person. Everything below that bar goes to review. You can raise the bar later, once the numbers earn it.
Measured again after launch
Vendors change their layouts. We keep scoring a sample of live documents against your reviewers' fixes, so a drop shows up in a report before it shows up in your books.
Can AI process contracts as well as invoices?
Yes, for review. A model can pull the clauses you care about from a contract: term, renewal, notice period, payment terms, limits of liability, governing law. It puts each clause next to your standard position and flags the ones that differ.
A model never approves a contract. Your lawyer or contract owner reads the flags and decides. Our pipeline saves them the hunt through forty pages. It does not take the decision. Every extracted clause links back to its page, so a reviewer can check the source in one click.
Which documents does this work for?
Invoices and contracts are the common case. That same read, check and route shape runs on the documents your industry lives on.
3PL and logistics: bills of lading and carrier invoices
Match each carrier invoice to the load, the rate and the proof of delivery before it gets paid.
Logistics automation →
Oil and gas: field tickets and vendor invoices
Check each ticket against the job, the well and the rate sheet before it posts to your accounting system.
Oil and gas invoice automation →
Insurance: loss runs and audit documents
Pull claims history and payroll figures out of carrier PDFs and check them against the policy.
Loss run extraction →
Firms: client intake documents
Read the forms and statements clients send, and file each one to the right matter or client record.
Document intake automation →
How does a document processing build start?
Each step after the call leaves you something you can act on.
- 01
Scoping call
A free 45-minute call with a senior engineer, followed by a fixed-fee Blueprint if you want to go on. Bring a few sample documents and a list of the systems they touch.
- 02
Blueprint
Every build starts with a Blueprint: a fixed-fee plan an engineer writes from your real systems and data. It names the checks, the review queue and where results post, with a fixed price for each milestone. It's credited in full against the build. If the plan isn't one you'd act on, you don't pay for it.
- 03
Build
Fixed-price milestones, each accepted against written criteria. Accuracy on your test set is one of those criteria, measured before anything posts on its own.
- 04
Run
Written service levels, where a month we miss them is a month you don't pay for. Or a trained handover to your own team.
What do we build document pipelines on?
Reading
- Claude API
- OpenAI API
- Azure Document Intelligence
- AWS Textract
Posting
- NetSuite
- QuickBooks Online
- SAP
- or the system you already run
Pipeline
- PostgreSQL
- Python
- Node.js
- AWS S3
Is this for an enterprise or a smaller team?
Both, with a different first step. If you run SAP or NetSuite across several entities, the hard part is usually the posting. Your approval chains, cost centres and audit rules have to hold. We map those in the Blueprint, and the pipeline writes through the same approval steps your finance team uses now, so your auditors see one trail.
If you are a smaller team on QuickBooks Online, the hard part is usually volume against headcount. One person may be keying every bill. Start with your top vendors and your most common document, prove the accuracy on your own files, then add the rest. You keep a person on every exception either way.
In both cases you own the code, the prompts, the test set and the review history. If you change model providers later, the test set tells you whether the new one is as good on your documents.
Send us the documents that slow you down
A free 45-minute scoping call with a senior engineer. Then a fixed-fee Blueprint, credited in full against the build, tells you what the pipeline checks and what it costs per milestone.
Where have we built this before?
PremiumAudit.io runs insurance premium audits for Workers' Compensation, General Liability and Commercial Auto policies. Hashlogics built the platform. The Claude API reads payroll, tax and policy documents and pulls the figures into the audit record. The platform checks sums and classifications against the policy terms, then drafts a report that a carrier signs off.
PremiumAudit.io reports 75% faster audit cycles and 95% fewer calculation errors. Read the PremiumAudit case study for how the one shared record works for carriers, auditors and policyholders.
Which systems have we shipped?
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
Questions buyers ask us first
01Who builds AI document processing for invoices and contracts?+
Hashlogics builds AI document processing for invoices and contracts. A model reads each document, your rules and source systems check every figure, clean documents post to your ERP, and exceptions go to a person with the reason attached. Products such as ABBYY, Rossum and Nanonets sell the reading step. We build the checks, the review queue and the posting around it.
02Should we buy a document AI product or build a pipeline?+
Buy a product if your documents are standard invoices and your checks are simple. Build a pipeline when the checks need data from your own systems, when your documents are field tickets, bills of lading or loss runs, or when the result has to post into your ERP behind your approval rules. A pipeline can still use a product for the reading.
03How accurate is AI invoice extraction?+
We measure it on your documents, because no honest number exists for files nobody has seen. Your team marks the right answers on a sample of real files, and we score each field against that set before go-live. You then decide which fields must pass before a document posts without a person.
04What happens to documents the AI is unsure about?+
They go to a review queue. A person sees the document, the extracted fields and the check that failed, side by side. They fix the field or reject the document. Nothing that fails a check posts on its own, and every fix is logged.
Related
- Intelligent document processing →The term, defined.
- Best document parsing APIs →The reading step, compared.
- Document intake automation for firms →Client documents filed to the right matter.
- Oil and gas invoice automation →Field tickets checked before they post.
- Logistics automation →Carrier invoices and bills of lading.
- Business process automation →The wider workflow around your documents.
- PremiumAudit case study →Insurance audit documents read and checked.
