Build a Custom AI Pipeline for Your Accounts Payable
A custom AI system for accounts payable is a fixed-price project scoped to your business. The cost depends on invoice volume, format complexity, and required integrations.
We recently built an AP system for a 25-person logistics company handling 600 monthly invoices. Their bookkeeper was spending two full days a month on manual entry. We delivered the system in three weeks, reducing processing time from 6 minutes per invoice to 8 seconds.
A typical build handles PDF invoices received via email, extracts key fields, and creates draft bills in your accounting software. More complex projects might involve matching invoices to purchase orders or handling handwritten documents, which extends the timeline.
What Problem Does This Solve?
Many teams start by manually keying PDF invoices into QuickBooks or Xero. This is manageable at 50 invoices a month, but at 300 it becomes a full-time job with a 3-5% error rate from typos. Off-the-shelf AP automation tools promise a fix, but they charge per invoice. At $0.50 per document, 500 invoices a month add a recurring $250 software bill that grows with your business.
Consider a regional distributor with 30 employees using NetSuite. They receive around 400 invoices per month. They tried an AP SaaS product, but it failed on 20% of their invoices from smaller vendors who use inconsistent templates. The system couldn't reliably find the invoice number or line-item totals, forcing the accounting clerk to manually review and correct nearly 80 documents each month, defeating the purpose.
The core problem is that generic tools are trained on generic data. They cannot learn the specific layouts of your top 10 vendors or understand your internal logic for assigning GL codes. They offer light customization through rules, but you cannot rewrite the core data extraction model. You are stuck with its performance, paying for every document it processes, even the ones it gets wrong.
How Does It Work?
We start by collecting 100-200 of your recent vendor invoices, representing a mix of formats. We write a Python script using AWS SES and Lambda to automatically pull new invoices from a dedicated email address like 'invoices@yourcompany.com'. This creates a centralized intake point, ending the need to forward attachments manually.
The core of the system is a data extraction pipeline. For each PDF, an OCR process converts the image to text. We then feed this text to the Claude API with a detailed prompt engineered to identify and extract up to 15 key fields: vendor name, invoice number, date, due date, line items, and totals. The entire process, from email receipt to structured JSON output, takes under 8 seconds. Accuracy for typed invoices consistently exceeds 99.5%.
We wrap this logic in a FastAPI application. The extracted data is used to create a draft bill in your accounting system via its API, for example, the QuickBooks Online API. We use Supabase as a lightweight database to log every transaction, store the extracted data, and link back to the original PDF. This gives you a full audit trail. The entire application is deployed on AWS Lambda, costing under $30 per month for up to 1,000 invoices.
We build a simple web interface for your accounting team to review any invoices the AI flags as low-confidence (less than 95% certainty on a key field). The interface shows the original PDF next to the extracted fields. Your team can make corrections in seconds, which are then fed back to refine future extractions. This human-in-the-loop step reduces the final error rate to below 0.1%.
What Are the Key Benefits?
Go Live in Three Weeks
From kickoff to processing your first live invoice in 15 business days. The system starts saving your team time immediately.
One-Time Build Cost, Not Per-Invoice
A single, fixed-price project. After launch, you only pay for cloud hosting, not a recurring subscription that penalizes you for growing.
You Own The Code
You receive the full Python source code in your company's GitHub repository, including deployment scripts and a detailed runbook.
Monitors Itself, Alerts on Failure
We configure structured logging with structlog and CloudWatch alerts. If an invoice fails to process, an alert is sent to Slack immediately.
Integrates Natively with Your ERP
We build direct API connections to QuickBooks, Xero, or NetSuite. Data flows into your system as a draft bill, no CSV imports required.
What Does the Process Look Like?
Scoping & Data Collection (Week 1)
You provide a sample of 100 recent invoices and grant read-only API access to your accounting system. We analyze the formats and finalize the project scope.
Pipeline Development (Week 2)
We build the core data extraction model using the Claude API and test it against your sample invoices. You receive a report showing extraction accuracy for each field.
Integration & Deployment (Week 3)
We deploy the system on AWS Lambda and connect it to your accounting software. Your team reviews the first batch of automatically created draft bills.
Handoff & Support (Post-Launch)
We monitor the system for two weeks post-launch to handle exceptions. You receive the full source code, documentation, and an optional flat-rate monthly maintenance plan.
Frequently Asked Questions
- How does invoice complexity affect the cost and timeline?
- A project with 90% standardized digital PDFs is straightforward. If you have many scanned, handwritten, or multi-page invoices with complex tables, we budget more time for prompt engineering and testing the Claude API's extraction logic. The number of custom fields or approval rules also influences the scope. A standard build with 10-15 fields is typically 3 weeks.
- What happens when the AI cannot read an invoice?
- If the model's confidence score for a key field like the total amount is below a set threshold (e.g., 95%), the invoice is flagged for human review. It appears in a simple queue with the problematic fields highlighted. Your team can correct it in seconds. The system does not silently pass bad data into your accounting software. It fails safely.
- How is this better than using a tool like Bill.com?
- Off-the-shelf tools like Bill.com are great for standard AP workflows but charge per invoice or user, which gets expensive. They offer limited customization. We build a system you own, with no per-invoice fees. It is tailored to your specific vendors and integrates with any other internal tools via a custom API, which packaged software cannot do.
- How do you ensure the security of our financial data?
- The system is deployed within your own cloud environment (AWS). We never have access to your production credentials after handoff. All data in transit is encrypted with TLS 1.2, and we use services like AWS Secrets Manager to handle API keys and database credentials securely. The source code is delivered to your private GitHub repository.
- Can we review extracted data before it enters QuickBooks?
- Yes, this is the standard workflow. The AI creates a 'draft' bill in your accounting system. This allows your team to perform a final review and approval step within the familiar QuickBooks interface before any payment is scheduled. We can also build a separate review dashboard outside of your accounting software if you prefer.
- Why do you use Claude API and not another AI model?
- We have found Anthropic's Claude models, specifically Claude 3 Opus, to have the highest accuracy for extracting structured data from messy, real-world documents like invoices. Its large context window and strong reasoning capabilities allow it to handle complex layouts and varied terminology better than many alternatives, reducing the need for manual corrections.
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