Services
AI agent development for back-office operations
An AI agent is software that does a job, not software that answers a question. It reads the invoice, checks it against the PO, codes it, and puts it in your approval queue. We are an AI agent development company for operations-heavy businesses: we build agents inside the software you already run, with a person approving anything that touches money, and we stay on to keep them running.
What an AI agent is, for an owner
A chatbot waits for you to ask. An automation follows a fixed rule. An AI agent is given a job and the access to do it: it reads what comes in, decides what to do within the limits you set, does it in your systems, and asks a person when it is unsure. The industry word for this is "agentic" AI, which means the software takes actions rather than only producing text.
That is the whole idea. The value is not that the agent is clever. It is that a task a person did every day, in four browser tabs, now happens on its own, and the person only sees the exceptions.
Twelve back-office agents we build
Each one names the systems it touches and the P&L line it moves. That is how we rank them in the assessment and how you should judge them.
1. AP invoice capture and coding. Reads invoices from email and scans, extracts the fields, matches to the PO or vendor history, codes to the GL, and queues for approval. Systems: email, QuickBooks / Sage / NetSuite, Bill.com. P&L line: AP hours, duplicate payments, early-pay discounts captured.
2. AR follow-up and cash application. Watches aging, sends the reminder sequence your collections person would send, escalates at your threshold, and applies payments to invoices when they land. Systems: accounting system, bank feed, email. P&L line: DSO, cash cycle, write-offs.
3. Dispatch check-calls and status updates. Pulls location and ETA from telematics and the TMS or FSM, updates the customer, flags late jobs. Systems: TruckMate, McLeod, ServiceTitan, Motive, Samsara, customer portals. P&L line: dispatcher hours, on-time rate, retained accounts.
4. Container and last-free-day tracking. Registers each inbound container with a tracking provider, reconciles the port's date against the line's, writes back to the TMS, alerts on change. Built for a Houston trucking company. Systems: TMS, Vizion, Teams. P&L line: demurrage and per-diem avoided.
5. Phone receptionist wired into dispatch. Answers, qualifies, books or routes the call, and creates the job in your system rather than leaving a voicemail. Systems: phone system, FSM or CRM, calendar. P&L line: missed calls converted, front-desk hours.
6. Intake and scheduling. Reads requests from web forms and email, creates the job, proposes a slot, confirms with the customer. Systems: FSM or TMS, calendar, email. P&L line: booked jobs per inquiry, response time.
7. RFQ-to-quote. Reads the request, pulls rates and job history, drafts the quote for review. Systems: email, CRM, pricing tables, ERP. P&L line: quote turnaround, win rate, quoted margin.
8. Purchase order matching. Three-way match of PO, receipt, and invoice; flags variances for a person. Systems: ERP or accounting, inventory, email. P&L line: overpayments, AP dispute hours.
9. Payroll and timesheet reconciliation. Compares timesheets, telematics or job clock-ins, and payroll; flags gaps before payroll runs. Systems: payroll, FSM or ELD, HR system. P&L line: payroll errors, overtime leakage.
10. Exception alerts from your systems. Watches for the conditions you define, a job over budget, a load without a driver, a permit about to expire, and tells the right person in Teams or Slack. Systems: any with an API or export. P&L line: the cost of the thing nobody noticed.
11. The weekly owner report. The visibility agent: revenue, margin by lane or job type, AR aging, on-time rate, utilization, from every system into one page on Monday morning. Systems: all of the above. P&L line: decisions made a week earlier.
12. Document assembly and filing. Builds the contract, COI request, compliance packet, or customer report from your templates and data; files it where it belongs. Systems: document storage, CRM, email. P&L line: admin hours, compliance exposure.
We rarely build all twelve. Most companies get three or four that matter, in the order the P&L says.
AI agent integration: how agents fit your stack
An agent is only useful if it can read and write your real systems. That is the part most AI agent development services skip.
We connect agents to your TMS, FSM, accounting system, ERP, and email through their APIs, with least-privilege access: the AP agent can read invoices and create bills, and cannot touch payroll. Every read and write is logged with what the agent saw and why it acted. If a system has no API, we work from its exports and tell you what that limits.
The agent runs in accounts you own: your cloud project, your database, your API keys. We build it; you hold the keys. How integrations work →
Human in the loop, and the paperwork that proves it
Agents earn trust in stages. A shadow period first, where the agent runs and a person keeps doing the job. Then a parallel run with review before anything leaves the building. Then production, with approval steps on the decisions you name: anything that moves money, anything customer-facing with a problem in it, anything the agent is unsure about.
Every action is in an audit trail. We document each agent's purpose, data access, limits, and review steps in a form aligned to the NIST AI Risk Management Framework, which is the standard Texas's AI law (TRAIGA) points to. If a customer, insurer, or regulator asks how your AI makes decisions, you have the answer written down.
Build or buy?
Sometimes the AI feature inside software you already pay for is enough. If your TMS or FSM vendor ships an agent that does exactly the job, inside the one system, take it.
Custom AI agent development pays back when the work crosses systems, when the vendor's feature does not know your accounting system or your customer's portal, when nobody is accountable for it breaking, or when you want to own what you run. Off-the-shelf automation tools sit in between and are the right answer for some workflows. Zapier vs. Make vs. n8n vs. custom: when each one wins →
How we decide which agent first
A two-day AI Readiness Assessment at your operation. Day one, we walk the workflows with the people who do them. Day two, we score every candidate agent on P&L impact and risk and hand you a ranked backlog you keep. Then we build the number-one agent in two weeks against success criteria we agree on. If it misses the mark, you don't pay for the build.
The assessment starts at $10,000 and is credited in full against your first workstream.
What AI agent development costs
- AI Readiness Assessment: from $10,000, two days on site, credited against the first workstream.
- Agent build workstreams: fixed fee, $10,000–$70,000 each, depending on systems touched, data quality, and number of integrations. Quoted before work starts.
- Managed AI: from $600 per month. Monitoring, fixes when a vendor changes an API, engineering hours included. Priority-one response in four business hours on the Managed plan.
Prices verified September 2026. Full detail on the pricing page.
Why Vorsa Logic
Business goals first. Every agent is mapped to a P&L line and ranked by measurable return and risk before we write code.
Built into what you already run. Your systems stay the systems of record. The agent works inside them.
Yours to keep, ours to run. You own the code, data, and accounts. We carry the pager.
Founder-built by Jonathan Klein and Chip Ray, based in Houston. Insured: cyber liability, technology E&O, and general liability.
A live agent
A Houston trucking company moves 600 to 700 import containers a month through Port Houston. Their dispatchers checked port and steamship-line websites by hand for every one. The agent we built registers each container with the tracking provider, reconciles the port's last-free-day against the line's, writes the result to TruckMate, and alerts dispatch in Teams when a date moves. Exceptions go to a person. They own it and keep us on a Managed plan. More on what we build for trucking →
What happens after go-live
Agents depend on vendor APIs and portals that change without notice. Our managed plan monitors every agent, fixes it when a vendor changes something, and includes engineering hours each month for the next improvement. How managed AI works →
Frequently asked questions
A chatbot answers questions when asked. An automation runs a fixed rule. An AI agent is given a job and does it: it reads inputs, decides within limits you set, acts in your systems, and asks a person when unsure. Most real workflows combine rules for the predictable parts and an agent for the parts that need judgment.
The assessment starts at $10,000 and is credited against the first build. Agent workstreams are fixed-fee, $10,000 to $70,000, depending on how many systems it touches and how clean the data is. Managed plans run $600 to $2,200 a month.
The first agent goes into production in two weeks, inside the assessment. Larger agents are scoped in weeks. The shadow and parallel-run periods add time before the manual process is switched off, on purpose.
Yes. Three things limit the damage: least-privilege access, so the agent can only touch what its job needs; approval steps on every decision that moves money or reaches a customer; and an audit log of every action. The shadow period exists to find the mistakes before they matter.
You do. Source code, data, cloud accounts, API keys, and the documentation. If we disappeared tomorrow, it would keep running and any competent engineer could take it over.
It breaks, and we fix it. That is what the managed plan is for: monitoring catches it, the included engineering hours cover the repair, and the priority-one response target is four business hours on the Managed plan.
Which job would you hand to an agent first?
Book a two-day assessment. You get a ranked list of the agents worth building, scored by P&L impact and risk, and we build the top one in two weeks.
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