Speed Up Your Accounts Receivable with Custom AI
AI speeds up accounts receivable by automating invoice matching, payment reminders, and cash flow forecasting. This reduces manual data entry and shortens the average days sales outstanding.
Key Takeaways
- Using AI to speed up accounts receivable automates invoice matching, payment reminders, and cash flow forecasting for small businesses.
- This automation reduces manual data entry and shortens the average time it takes to collect payments from customers.
- A custom system can integrate directly with existing accounting software and bank accounts, providing real-time visibility into cash flow.
- Automated payment reconciliation can process bank syncs in under 3 seconds, a task that often takes minutes per transaction manually.
Syntora built a financial automation system connecting Plaid and Stripe to a PostgreSQL ledger. The system automates transaction categorization and processes bank syncs in under 3 seconds. For SMBs, Syntora applies this experience to build custom accounts receivable systems that accelerate cash collection.
Syntora built the financial backend for its own operations, connecting Plaid and Stripe to a PostgreSQL ledger for real-time transaction categorization. This same pattern applies to accounts receivable, using AI to match incoming payments to open invoices and identify collection risks before they become problems.
Why Do Finance Teams Still Chase Invoices Manually?
Many small businesses run their finances on QuickBooks Online or Xero. These platforms are excellent for recording transactions but their automation is limited to simple, rule-based tasks. Their bank reconciliation features often fail when a single deposit covers multiple invoices, a payment is partial, or the deposit amount is net of wire fees. This forces a bookkeeper to manually find and match transactions, a process prone to error.
Consider a 15-person service business that sends 50 invoices a month. Their bookkeeper spends a full day each month reconciling bank statements. A client pays three invoices at once via ACH, but the total deposit is $50 less than the invoice total because of a bank fee. QuickBooks cannot match the payment automatically. The bookkeeper must find the three invoices, calculate the discrepancy, and manually apply payments, creating a separate journal entry for the fee. This single transaction takes 15 minutes to resolve.
The structural problem is that off-the-shelf accounting software is built as a system of record, not a system of intelligence. The architecture prioritizes data integrity for tax purposes over the complex logic required for operational efficiency. These platforms are not designed to learn from your payment history or parse ambiguous text in a bank transaction description. Their APIs allow you to pull data, but they lack the event-driven architecture needed to act on that data in real time.
How Syntora Builds a Custom AI-Powered AR System
The first step is a read-only connection to your bank data via Plaid and your accounting system, like QuickBooks or Xero, via its API. Syntora audits the last 12 months of invoices and payments to understand your specific matching challenges. This audit identifies common partial payment scenarios, wire fees, and customer-specific payment patterns. The output is a clear map of the logic the AI system needs to learn.
Syntora built its own financial ledger on PostgreSQL with an Express.js API, a system that processes bank syncs in under 3 seconds. For your AR system, the approach is similar. A FastAPI service listens for new bank transactions from Plaid. When a deposit arrives, a lightweight AI model using the Claude API analyzes the transaction description and amount against open invoices. This model handles ambiguous cases that stump rule-based software, like matching a single $9,850 deposit to three invoices totaling $10,000, correctly identifying a $150 processing fee.
The delivered system is a service running on AWS Lambda that typically costs less than $50 per month to operate. It writes matched payments directly into your accounting software, closing the correct invoices and flagging any true exceptions for human review. You receive the full Python source code, a runbook for maintenance, and a simple dashboard to monitor match rates. The system enhances your current workflow, it does not force you to adopt a new one.
| Manual AR Process in QuickBooks | Automated with a Custom AI System |
|---|---|
| 10-15 minutes per complex payment reconciliation | Under 3 seconds for automated matching |
| 5-8 hours per month spent on manual reconciliation | Less than 1 hour per month spent reviewing exceptions |
| 3-5% error rate from manual data entry | Under 0.1% error rate on matched transactions |
Key Benefits
One Engineer, From Discovery to Deployment
The founder who scopes your project is the same engineer who writes every line of code. No project managers, no communication gaps, no offshore handoffs.
You Own All the Code and Infrastructure
You receive the full source code in your private GitHub repository and the system runs on your cloud account. There is no vendor lock-in, ever.
A Realistic 4-Week Timeline
A typical AR automation project moves from discovery to a deployed system in 4 weeks. The initial data audit provides a firm timeline before the build begins.
Fixed-Cost Monthly Support
After deployment, you can opt into a flat monthly support plan that covers monitoring, maintenance, and small feature updates. No unpredictable hourly billing.
Deep Financial Tech Experience
Syntora has built production financial systems connecting Plaid, Stripe, and custom ledgers. This is not theoretical knowledge; it's real-world engineering experience applied to your AR challenges.
The Process
Discovery & Data Audit
A 45-minute call to understand your current AR process and pain points. You provide read-only access to your bank and accounting data, and receive a scope document outlining the approach, timeline, and fixed cost within 3 business days.
Architecture & Approval
Syntora presents a brief technical design showing how the system will connect to your existing tools. You approve the final architecture and integration points before any code is written.
Build & Weekly Demos
The system is built with weekly check-ins to demonstrate progress. You see the AI matching your actual transaction data, allowing for feedback and adjustments before the final deployment.
Handoff & Training
You receive the complete source code, a deployment runbook, and a live training session on how the system works. Syntora monitors the system for 4 weeks post-launch to ensure stability and accuracy.
Related Solutions
The Syntora Advantage
Not all AI partners are built the same.
SyntoraSyntora
We assess your business before we build anything
Industry Standard
Assessment phase is often skipped or abbreviated
Syntora
Fully private systems. Your data never leaves your environment
Industry Standard
Typically built on shared, third-party platforms
Syntora
Zero disruption to your existing tools and workflows
Industry Standard
May require new software purchases or migrations
Syntora
Full training included. Your team hits the ground running from day one
Industry Standard
Training and ongoing support are usually extra
Syntora
You own everything we build. The systems, the data, all of it. No lock-in
Industry Standard
Code and data often stay on the vendor's platform
Ready to Automate Your Financial Advising Operations?
Book a call to discuss how we can implement ai automation for your financial advising business.