AI Agent Developer: Atlanta

Hire an AI agent developer in Atlanta.

Global Payments' merchant services team answers the same 50 questions thousands of times each week: account status, transaction records, dispute initiation. A live agent handles each ticket in 4 to 8 minutes. An AI agent handles the same sequence in under 30 seconds: query the account system, retrieve the transaction record, generate the resolution, and close the ticket.

Delta Air Lines flight operations faces a parallel challenge. Dispatchers assemble delay notifications by pulling weather data, aircraft maintenance status, and crew scheduling constraints from three separate systems. An agent queries those systems, assembles the data package, and drafts the notification. The dispatcher reviews and approves in two minutes instead of twelve.

Atlanta's fintech and logistics concentration means this class of problem appears constantly. We build the agents that handle it.

How we work

Describe your customer operations workflow.

The Atlanta volume problem.

NCR Voyix processes point-of-sale transactions for tens of thousands of retailers and restaurants. When a merchant calls with a transaction question, a support agent looks up the account, finds the transaction, identifies the issue, and either resolves it or escalates. That sequence involves three systems and takes 5 to 10 minutes. Multiply by hundreds of tickets per hour and the volume is not a staffing problem. It is a workflow problem.

An AI agent wired to the account database, the transaction ledger, and the dispute system handles the lookup sequence without a human in the loop. The live support team handles the 15 to 20 percent of tickets that require judgment. The other 80 to 85 percent resolve automatically.

UPS regional operations in Atlanta coordinates time-sensitive shipments where a delay notification must go out within minutes of a status change. The current workflow requires a coordinator to pull tracking data, identify the impact, draft a customer message, and update the internal ops log. Each step is manual. Each step takes time the customer does not have.

An agent monitors the tracking feed, detects status changes that meet the notification threshold, assembles the customer message from a structured template populated with live data, and queues it for coordinator review. The coordinator sends or edits. Total coordinator time: 45 seconds instead of 8 minutes.

Where agents fit in high-volume operations.

Agents are not a replacement for your support team. They handle the repeatable sequences so your team handles the rest.

Deterministic inquiry types go fully automated

Account status, transaction retrieval, policy lookup: these have a defined answer. The agent retrieves it without human involvement. Resolution happens in under a minute.

Complex tickets get a head start

When a ticket requires judgment, the agent still runs. It retrieves the account record, pulls the transaction history, and packages the context before the human agent sees the ticket. Handle time drops from 12 minutes to 4.

Escalation paths are explicit

The agent knows exactly what it can and cannot resolve. When a ticket hits an escalation condition, it stops, hands off the data package it assembled, and flags the reason. No ticket falls through the cracks.

What we build for Atlanta operations teams.

CRM and account system integration

Read connectors to Salesforce, ServiceNow, Zendesk, or custom account databases. The agent retrieves structured account data at the start of every ticket workflow.

Transaction and ledger lookup tools

Direct API connectors to payment processing systems including Global Payments, Worldpay, and FIS. Transaction records land in a typed schema before the agent acts.

Dispute and case initiation

Write-capable tools scoped to specific case types. The agent initiates a dispute record only when the ticket type explicitly requires it. Every write action is logged.

Human handoff and monitoring

Configurable escalation triggers by ticket type, account value, or resolution confidence. Every agent run logged: tool calls, outcome, latency. Dashboard shows resolution and fallback rates.

How we work.

A one-hour discovery call covers the specific workflow: which systems the agent needs to read and write, what the resolution criteria are, and where the escalation boundaries sit. We come back with a written scope and a fixed price before any code is written.

Build takes 4 to 8 weeks depending on the number of system integrations. We deploy to your infrastructure, not ours. You get the source code, documentation, and a 30-day post-launch support window. No open-ended retainer.

Full process overview →

Questions from Atlanta operations teams.

How do you make sure a customer service agent does not take an action outside its intended scope, like modifying an account when it should only be reading?: The agent is built with explicit permission boundaries at the tool level, not the prompt level. Read operations and write operations are separate tools. The write tool is only included in the agent configuration when the workflow explicitly requires it. An agent scoped to transaction status lookups does not have access to the account modification tool, so there is no path by which it can trigger one, regardless of what the user requests.
What does a fallback path look like when the agent cannot resolve an inquiry, and how does it hand off to a human?: The agent monitors its own confidence at each step. When a required data field is missing, a policy lookup returns no match, or the user request falls outside the defined workflow, the agent stops and queues the ticket for a human agent. The handoff package includes the conversation history, the data the agent already retrieved, and a structured note explaining why it stopped. The human agent does not start from scratch.
How do you handle the Global Payments use case where the same question has different answers for different merchant tiers?: Merchant tier is retrieved as the first step in the workflow, before any policy lookup. The tier value is passed as context to every subsequent tool call. The policy lookup tool takes tier as a required parameter and returns tier-specific answers. This is not prompt engineering, it is a data routing problem. The tier-to-policy mapping lives in a configuration file your team can update without code changes.
What does agent monitoring look like in a high-volume production environment?: Every agent run is logged: the trigger event, each tool call and its result, the final action taken, and the latency of each step. At volume, we build a monitoring dashboard that tracks resolution rate, fallback rate, average handle time, and error rate by inquiry type. When the fallback rate on a specific inquiry type climbs above a threshold, an alert fires so your team can investigate whether a policy changed or the agent needs retraining.

Walk us through your current workflow.

Describe the steps your team executes manually today. We will reply within one business day with a scope and price range.

Hire an AI agent developer in Atlanta.

Atlanta's fintech and logistics concentration means this class of problem appears constantly. We build the agents that handle it.

The Atlanta volume problem.

Where agents fit in high-volume operations.

Agents are not a replacement for your support team. They handle the repeatable sequences so your team handles the rest.

Deterministic inquiry types go fully automated

Account status, transaction retrieval, policy lookup: these have a defined answer. The agent retrieves it without human involvement. Resolution happens in under a minute.

Complex tickets get a head start

Escalation paths are explicit

What we build for Atlanta operations teams.

CRM and account system integration

Read connectors to Salesforce, ServiceNow, Zendesk, or custom account databases. The agent retrieves structured account data at the start of every ticket workflow.

Transaction and ledger lookup tools

Direct API connectors to payment processing systems including Global Payments, Worldpay, and FIS. Transaction records land in a typed schema before the agent acts.

Dispute and case initiation

Write-capable tools scoped to specific case types. The agent initiates a dispute record only when the ticket type explicitly requires it. Every write action is logged.

Human handoff and monitoring

Configurable escalation triggers by ticket type, account value, or resolution confidence. Every agent run logged: tool calls, outcome, latency. Dashboard shows resolution and fallback rates.

How we work.

Questions from Atlanta operations teams.

How do you make sure a customer service agent does not take an action outside its intended scope, like modifying an account when it should only be reading?

The agent is built with explicit permission boundaries at the tool level, not the prompt level. Read operations and write operations are separate tools. The write tool is only included in the agent configuration when the workflow explicitly requires it. An agent scoped to transaction status lookups does not have access to the account modification tool, so there is no path by which it can trigger one, regardless of what the user requests.

What does a fallback path look like when the agent cannot resolve an inquiry, and how does it hand off to a human?

The agent monitors its own confidence at each step. When a required data field is missing, a policy lookup returns no match, or the user request falls outside the defined workflow, the agent stops and queues the ticket for a human agent. The handoff package includes the conversation history, the data the agent already retrieved, and a structured note explaining why it stopped. The human agent does not start from scratch.

How do you handle the Global Payments use case where the same question has different answers for different merchant tiers?

Merchant tier is retrieved as the first step in the workflow, before any policy lookup. The tier value is passed as context to every subsequent tool call. The policy lookup tool takes tier as a required parameter and returns tier-specific answers. This is not prompt engineering, it is a data routing problem. The tier-to-policy mapping lives in a configuration file your team can update without code changes.

What does agent monitoring look like in a high-volume production environment?

Every agent run is logged: the trigger event, each tool call and its result, the final action taken, and the latency of each step. At volume, we build a monitoring dashboard that tracks resolution rate, fallback rate, average handle time, and error rate by inquiry type. When the fallback rate on a specific inquiry type climbs above a threshold, an alert fires so your team can investigate whether a policy changed or the agent needs retraining.

Walk us through your current workflow.

Describe the steps your team executes manually today. We will reply within one business day with a scope and price range.