Build Custom AI Payroll Automation for Your SMB

Most SMBs start with platforms like Gusto or Rippling. These tools are excellent for standard W-2 employees but falter with variable compensation. If you pay commissions based on Stripe revenue or job-cost labor from TSheets, you must export a CSV, perform calculations in a spreadsheet, and manually re-enter the data. The so-called automation breaks at the most critical step.

QuickBooks Payroll keeps everything inside the Intuit ecosystem but offers rigid reporting. Calculating payroll liability by department or project for accurate job costing requires manual exports and pivot tables. There is no mechanism to feed your unique business logic back into the payroll calculation engine. The system cannot answer questions like, "What was our true labor cost for Project X last month?"

A detailed scenario: a 25-person construction company pays variable rates based on the job site and offers tool reimbursements. Every two weeks, an office manager spends a full day consolidating TSheets data, matching rates to job codes in a spreadsheet, calculating overtime, cross-referencing bank statements for reimbursements, and finally entering totals into Gusto. A single formula error in the spreadsheet can result in an incorrect paycheck and a compliance headache.

The structural problem is that off-the-shelf payroll products are built around a fixed data model. They assume standardized inputs. They are not designed to integrate external business logic, like per-project profitability, directly into a payroll run. This forces the most complex calculations offline and re-introduces the manual error they were supposed to prevent.

The engagement would start with a complete audit of your current payroll process. Syntora would map every data source: your bank connection via Plaid, your time-tracking software's API, your HRIS, and your accounting ledger. The goal is to identify the exact point where manual work begins and codify the business rules you use. You receive a technical specification that details the data flow and integration points before any code is written.

The core of the system would be a FastAPI service running on AWS Lambda. We built a similar financial system that syncs bank transactions in under 3 seconds using an Express.js and PostgreSQL stack. For your payroll system, this Python-based approach allows for flexible, auditable business logic. The system would pull employee records from your HRIS API, apply your commission or job-costing rules, and log every calculation in a Supabase (PostgreSQL) database for a clear audit trail. The projected build time is 3 weeks.

The delivered system is a 'headless' payroll engine that runs on a schedule. It processes up to 50 employee records, generates a file formatted for your payroll provider, and sends a confirmation. The entire automated process has a projected hosting cost under $50 per month. You receive the full source code, a runbook for maintenance, and a system built on open standards that you completely own.