Rebuild Your Procure-to-Pay Process with AI Automation
Rebuilding procure-to-pay with AI automation reduces manual data entry time by over 90%.
A typical build scope depends on the number of vendor invoice formats and the complexity of your ERP integration. A manufacturer with 50 vendors sending standard PDF invoices is a straightforward project. A business with 200 vendors using EDI feeds, scanned documents, and email attachments requires more complex data handling and validation logic.
We built an accounts payable system for a 25-person specialty parts manufacturer processing 800 vendor invoices per month. Their existing manual process was slow and error-prone. Our AI pipeline went live in 4 weeks, reducing their accounts payable clerk's workload by 20 hours per week and eliminating late payment penalties.
This cuts invoice processing costs and eliminates late payment fees, yielding positive ROI within 6-9 months.
What Problem Does This Solve?
Most manufacturing SMBs start by using their accounting software's built-in tools, like QuickBooks Bill Pay. These tools manage payments but do not automate the tedious data entry from vendor invoices. An accounts payable clerk still spends hours manually keying in invoice numbers, line items, and due dates, a process where a single typo can lead to a costly overpayment.
To solve the data entry problem, some teams try dedicated OCR software. An OCR tool can extract text from a PDF, but it does not understand context. It might pull '12/25/2024' but cannot reliably identify it as the 'due_date' field across a dozen different invoice layouts. This forces developers to write brittle, template-based rules for each vendor, which break the moment a vendor changes their invoice design.
AP automation platforms seem like the next logical step, but they introduce new problems. They charge per invoice or have high monthly minimums, penalizing volume. A manufacturer processing 1,000 invoices per month can face a $500 monthly bill before user fees. These platforms also lack the ability to run custom validation logic, like checking a PO line item against a custom field in a proprietary inventory management system before approving payment.
How Does It Work?
We begin by collecting 100 sample invoices from your top 20 vendors to understand format variations. An ingestion pipeline, built on AWS Lambda, pulls new invoices from a dedicated email inbox every 5 minutes. It uses Amazon Textract for high-fidelity OCR, correctly processing both digital PDFs and scanned paper documents, including those with complex tables.
We then feed the raw text from Textract into the Claude 3 Sonnet API for intelligent entity extraction. We define a JSON schema with up to 15 key fields you need (Invoice ID, PO Number, Vendor Name, Line Items, Total). The model extracts this structured data in a single API call. The entire extraction and validation process completes in under 8 seconds per document, down from 6-8 minutes of manual entry.
Next, a FastAPI service validates the extracted data. This Python application checks the PO number against your ERP, verifies line-item prices, and flags duplicates. Validated invoices are pushed directly to your accounting system's API, whether it's NetSuite, QuickBooks, or a custom-built platform. We use Supabase as a Postgres database to log every transaction and its status, creating a permanent audit trail.
The entire system is deployed as a serverless application, keeping hosting costs under $50 per month for processing up to 2,000 invoices. We implement structured logging using `structlog` and configure CloudWatch alerts. If the API error rate exceeds 1% in any given hour, we receive an immediate notification to investigate the issue, often before your team is even aware of it.
What Are the Key Benefits?
Process Invoices in 8 Seconds, Not 8 Minutes
Our AI pipeline extracts, validates, and enters an invoice into your accounting system in under 10 seconds. Eliminate manual data entry and approve payments the same day.
Pay for the Build, Not Per Invoice
A one-time fixed price for the system, with hosting costs under $50/month. Avoid SaaS fees that punish you for growing your transaction volume.
You Own the Code and the Process
We deliver the full Python source code to your company's GitHub. You have complete control and are never locked into a proprietary platform.
Proactive Monitoring Catches Errors
We configure CloudWatch alarms to monitor API health and data validation rates. You get alerts for failed invoices, not a backlog at month-end.
Connects Directly to Your ERP
The system writes validated data directly to QuickBooks, NetSuite, or your custom manufacturing ERP via API. No more CSV imports or manual reconciliation.
What Does the Process Look Like?
Discovery and Scoping (Week 1)
You provide 50-100 sample invoices and read-only access to your ERP. We deliver a detailed technical proposal mapping every field and validation rule.
Core Pipeline Build (Week 2)
We build the data extraction and validation engine using the Claude API and FastAPI. You receive access to a staging environment to test with new invoices.
ERP Integration (Week 3)
We connect the pipeline to your live accounting system and configure production monitoring. You receive a runbook detailing the system architecture and error handling.
Launch and Support (Week 4+)
The system goes live. We monitor performance for 30 days post-launch. After this period, we transition to an optional monthly maintenance plan.
Frequently Asked Questions
- How much does a custom procure-to-pay system cost?
- Pricing is scoped based on the number of unique vendor invoice formats and the complexity of your ERP integration. A system for a company with 20 primary vendors integrating with QuickBooks Online is a standard 4-week build. A project with 100+ vendors and a legacy on-premise ERP requires more custom development. Book a discovery call for a detailed quote.
- What happens when the AI misreads an invoice?
- Invoices that fail validation or have a low confidence score are flagged for human review. They are sent to a dedicated email address with a link to a simple UI where an admin can correct the data. The system learns from these corrections over time. This 'human-in-the-loop' design ensures high accuracy without creating a processing bottleneck.
- How is this different from using an AP automation platform like Tipalti?
- Tipalti is a comprehensive SaaS platform with features like global payments and tax compliance, but it charges per invoice and has limited customization. Our build focuses solely on the data extraction and entry process. We provide the core automation engine which you own completely, without recurring per-transaction fees, and tailor it to your exact validation rules.
- What if our invoices are low-quality scans or handwritten?
- Our process uses Amazon Textract, which is robust for low-quality scans. However, handwritten invoices are challenging for any OCR system. During discovery, we will assess your invoice samples. If a significant portion is handwritten, we will build a workflow to route those specific documents for manual entry while automating the rest, ensuring the project still delivers value.
- Who supports the system after the build?
- You receive the full source code and a runbook for your team. For businesses without an engineering team, we offer a flat monthly maintenance plan. This covers hosting management, dependency updates, and up to 5 hours of developer time for bug fixes or minor enhancements. The person who built the system is the person who supports it.
- How does the system handle a new vendor?
- The AI model is trained to understand the general structure of invoices, not the specific template of one vendor. When you add a new vendor, the system typically processes their invoices correctly without any changes. For formats that are highly unusual, we can fine-tune the extraction prompts in a few hours as part of the maintenance plan.
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