AI Agent Development · Atlanta

AI agent development in Atlanta.

Atlanta runs on logistics, healthcare administration, fintech, and a thick layer of B2B SaaS. The agent workloads here have a shape that matches the city: high-volume operational queues at Delta, UPS, and Home Depot; high-velocity claims and dispute workflows at WellStar, Equifax, and Independence Blue Cross vendors; and mid-market customer-ops queues at Mailchimp, Calendly, NCR Voyix, and the late-stage SaaS companies along Tech Square.

The Atlanta enterprise stack is convergent in a useful way. SAP at Coca-Cola, UPS, and Home Depot. Salesforce Service Cloud across most of the ops shops. Snowflake widely deployed. EDI between trading partners. That convergence means the integration surface for an agent is well-known, the data plumbing exists, and the project timeline is dominated by the agent design and the eval harness rather than the connectors.

Projects run fixed-scope. Discovery first, fixed quote after, four to eight weeks of build with weekly demos on Eastern time.

How we work

Tell us about the workflow.

The shape of agent work in Atlanta.

The first pattern is logistics exception handling at hub scale. Delta runs the operational control center at Hartsfield with IRROPS, crew-legality, and downline mis-connection workflows on top of Sabre and the internal crew-scheduling system. UPS runs package-exception handling out of Sandy Springs across Worldship, EDI 214 status messaging, and customer claims. Home Depot runs vendor-portal exception management for the long tail of suppliers feeding 2,300+ stores. Each of these workflows is a tier-1 deflection candidate where the agent does the assembly and the human makes the call.

The second pattern is healthcare claims and admin ops. WellStar Health, Emory Healthcare, and the Independence Blue Cross processors in the metro run prior-authorization, claims adjudication, and provider-network management on a mix of Epic, Cerner, and payer-side systems. The Atlanta-Fed economic data feeds into the local insurance and finance back office in ways that make the integration patterns familiar.

The third pattern is fintech and credit dispute handling. Equifax sits at the center, but the dispute and identity workflows extend to NCR Voyix payments, the FIS and Fiserv offices in the metro, and a layer of bank-tech vendors serving the regional banks. FCRA, ECOA, BSA, and AML record-keeping rules drive the audit-trail design.

The fourth pattern is mid-market B2B SaaS customer ops. Mailchimp, Calendly, SalesLoft, Pendo, and a long tail of Atlanta SaaS companies run customer-support and customer-success teams that handle a few thousand to a few tens of thousands of tickets a month. The volume justifies an agent, the stack is modern (Salesforce, HubSpot, Snowflake, Zendesk, Intercom), and the deflection economics work as soon as the eval set is labeled.

The fifth pattern is industrial supply-chain ops. Coca-Cola runs SAP and the partner-trading network out of midtown. Georgia-Pacific runs paper and packaging supply chains across the Southeast. The shape of agent work in those companies is exception handling on PO matching, freight booking under EDI 204/210 traffic, and SKU-master data hygiene across the SAP MM module.

Every one of these is an agent candidate where the integration surface is well-understood and the unit economics work at Atlanta enterprise margins. The hard problem is not the model. The hard problem is the eval set and the human-gate design.

What we build for Atlanta teams.

Six engineering components show up in nearly every agent we ship for logistics, healthcare, fintech, and SaaS teams in the city.

Agent loop inside your enterprise stack

LangGraph or OpenAI Assistants depending on the workflow shape. The agent reads and writes through your existing SAP BAPIs, Salesforce objects, and Snowflake models so the audit trail lives in the systems your team already operates.

Integrations to the Atlanta stack

SAP ECC and S/4HANA, Salesforce Service and Sales Cloud, Snowflake, EDI 204/210/214/856, Sabre, Worldship, Epic, Cerner, e-OSCAR for FCRA disputes, Zendesk, Intercom, HubSpot. Typed schemas with retry policies and idempotency keys on every write.

Classifier and routing layer

Logistics-exception classification, support-intent routing, claims-routing rubric, FCRA dispute taxonomy. Built against a labeled set you provide, versioned, evaluated, and gated by CI before any change reaches production traffic.

Human-in-the-loop gates

Any irreversible action (reissue a ticket, deny a claim, write back to SAP, decide a dispute outcome) routes to a human in Slack, Teams, or your existing queue with the full reasoning trace and assembled case file attached.

Observability with cost telemetry

OpenTelemetry spans for every LLM and tool call. Model version, token counts, latency, cost, trajectory length, override flag. Searchable by account, PNR, claim ID, or run ID. Cost dashboards by feature and channel.

Production eval harness

Braintrust or Promptfoo against a labeled set, gating CI. Task completion rate, classifier accuracy, tool-call accuracy. Regression suite runs on every prompt or model change so a quiet drift does not reach customers or auditors.

Why this work fits Atlanta.

Atlanta concentrates a useful mix: 17 Fortune 500 headquarters, the world's busiest airport by passenger volume, a logistics cluster around UPS and the Port of Savannah supply chain, a fintech corridor anchored by Equifax and NCR Voyix, and a mid-market SaaS scene around Tech Square. The labor pools in operations, claims processing, and customer support across that mix run in the hundreds of thousands.

We have scoped agent work for ops teams at logistics-adjacent shippers, mid-market SaaS support orgs, and credit-bureau process-improvement groups in the metro. The patterns repeat. SAP or Salesforce as the system of record, a high-volume queue with a deterministic share above 50%, a regulatory or customer-experience requirement that forbids fully autonomous action, and a senior operator who has to sign the call.

We work remotely with Atlanta clients on Eastern time. Discovery calls fit between 9am and 6pm ET, demos happen weekly, and the codebase ships to a repository you own. No agency-style retainer, no managed cloud you have to migrate to later.

Industries where we see strongest fit: logistics and supply-chain exception management, healthcare claims and prior-authorization, fintech and credit-bureau dispute handling, B2B SaaS customer support, and industrial supply-chain SKU and PO hygiene.

Questions from Atlanta clients.

Can an agent handle IRROPS passenger rebooking at Delta hub scale during a storm event?: Yes, for the deterministic share of the queue. Irregular operations at a hub like Atlanta combine weather-driven cancellations, crew-legality issues, and downline mis-connections at a volume that overwhelms the OCC during a real event. An agent reads from the operational control center feed, the Sabre PNR, and the crew-scheduling system, then proposes a rebooking option per passenger ranked by elite status, cabin class, downstream mis-connect risk, and revenue protection. The OCC agent or the gate agent approves or overrides with one click. We do not let the agent auto-reissue a ticket during the first months of production because a wrong reissue against a high-status passenger has a customer-relations cost. Once the agent has handled a couple of major IRROPS events in shadow mode and the eval set shows clean decisions, low-risk rebookings (single-leg, no elite status, plenty of inventory) can move to auto-execution with sampling audit. The cargo side has the same pattern through the cargo management system.
How do you integrate an agent into the SAP-and-Salesforce stack that UPS, Coca-Cola, and Home Depot all run?: The Atlanta enterprise stack is unusually convergent. SAP ECC or S/4HANA as the system of record for finance and supply, Salesforce Service Cloud or Sales Cloud as the customer-facing system, Snowflake as the warehouse, and a thick layer of EDI between trading partners. The agent reads from SAP through the documented BAPI or OData layer when those exist, falls back to RPA against the SAPGUI when they do not, and writes back through the same path. On the Salesforce side, we use the standard REST API with idempotency keys on every write so a retried call does not double-post a case. Cross-system reconciliation (an order in SAP that has no matching case in Salesforce, or vice versa) is one of the most common workflows we ship. The agent assembles the discrepancy package; the operations analyst signs the resolution.
What does an Equifax FCRA dispute-handling agent look like under the regulatory record-keeping requirements?: FCRA disputes have to be investigated within 30 days under Section 611, and the credit reporting agency has to maintain records of the dispute, the data furnisher response, and the reinvestigation outcome. The agent reads the inbound dispute (uploaded letter, online portal submission, or CFPB complaint relay), classifies the disputed item (tradeline accuracy, identity, mixed file, fraud), pulls the consumer file and the relevant furnisher data, drafts the ACDV to the furnisher through the e-OSCAR pipeline, and assembles the reinvestigation package for the analyst. The credit analyst signs the outcome because the FCRA liability sits with the human decision. We log every model version, every data source, and every override decision to an append-only record that satisfies the CFPB and FTC examination expectations. The agent also flags patterns that suggest identity theft or mixed-file errors so the team can route those to the appropriate workflow.
How does an agent fit alongside the customer-support team at a Mailchimp or Calendly?: The Atlanta SaaS pattern is mid-market support volume with a heterogeneous query mix. Account questions, billing disputes, deliverability problems at Mailchimp, calendar-sync edge cases at Calendly. The agent owns tier-1 deflection on the deterministic share (password resets, plan changes, common deliverability fixes, OAuth reconnect flows), classifies the rest against your taxonomy, and drafts a response that the support engineer reviews and sends. For account-specific debugging (a specific deliverability issue tied to a customer's SPF and DKIM records, or a calendar-sync failure tied to a particular Google Workspace tenant), the agent pulls the relevant logs and configuration, runs the standard diagnostic playbook, and either resolves or assembles a one-screen handoff for the human. The support engineer reads the assembled context instead of starting from a fresh ticket. Average handle time drops measurably even when the deflection rate is modest.

Scope an agent for your Atlanta team.

Describe the workflow, the system of record, and the volume. We'll reply within one business day with a rough scope, a recommended architecture, and a price range. No commitment, no sales call required.

AI agent development in Atlanta.

Projects run fixed-scope. Discovery first, fixed quote after, four to eight weeks of build with weekly demos on Eastern time.

The shape of agent work in Atlanta.

What we build for Atlanta teams.

Six engineering components show up in nearly every agent we ship for logistics, healthcare, fintech, and SaaS teams in the city.

Agent loop inside your enterprise stack

Integrations to the Atlanta stack

Classifier and routing layer

Human-in-the-loop gates

Observability with cost telemetry

Production eval harness

Why this work fits Atlanta.

Questions from Atlanta clients.

Can an agent handle IRROPS passenger rebooking at Delta hub scale during a storm event?

Yes, for the deterministic share of the queue. Irregular operations at a hub like Atlanta combine weather-driven cancellations, crew-legality issues, and downline mis-connections at a volume that overwhelms the OCC during a real event. An agent reads from the operational control center feed, the Sabre PNR, and the crew-scheduling system, then proposes a rebooking option per passenger ranked by elite status, cabin class, downstream mis-connect risk, and revenue protection. The OCC agent or the gate agent approves or overrides with one click. We do not let the agent auto-reissue a ticket during the first months of production because a wrong reissue against a high-status passenger has a customer-relations cost. Once the agent has handled a couple of major IRROPS events in shadow mode and the eval set shows clean decisions, low-risk rebookings (single-leg, no elite status, plenty of inventory) can move to auto-execution with sampling audit. The cargo side has the same pattern through the cargo management system.

How do you integrate an agent into the SAP-and-Salesforce stack that UPS, Coca-Cola, and Home Depot all run?

The Atlanta enterprise stack is unusually convergent. SAP ECC or S/4HANA as the system of record for finance and supply, Salesforce Service Cloud or Sales Cloud as the customer-facing system, Snowflake as the warehouse, and a thick layer of EDI between trading partners. The agent reads from SAP through the documented BAPI or OData layer when those exist, falls back to RPA against the SAPGUI when they do not, and writes back through the same path. On the Salesforce side, we use the standard REST API with idempotency keys on every write so a retried call does not double-post a case. Cross-system reconciliation (an order in SAP that has no matching case in Salesforce, or vice versa) is one of the most common workflows we ship. The agent assembles the discrepancy package; the operations analyst signs the resolution.

What does an Equifax FCRA dispute-handling agent look like under the regulatory record-keeping requirements?

FCRA disputes have to be investigated within 30 days under Section 611, and the credit reporting agency has to maintain records of the dispute, the data furnisher response, and the reinvestigation outcome. The agent reads the inbound dispute (uploaded letter, online portal submission, or CFPB complaint relay), classifies the disputed item (tradeline accuracy, identity, mixed file, fraud), pulls the consumer file and the relevant furnisher data, drafts the ACDV to the furnisher through the e-OSCAR pipeline, and assembles the reinvestigation package for the analyst. The credit analyst signs the outcome because the FCRA liability sits with the human decision. We log every model version, every data source, and every override decision to an append-only record that satisfies the CFPB and FTC examination expectations. The agent also flags patterns that suggest identity theft or mixed-file errors so the team can route those to the appropriate workflow.

How does an agent fit alongside the customer-support team at a Mailchimp or Calendly?

The Atlanta SaaS pattern is mid-market support volume with a heterogeneous query mix. Account questions, billing disputes, deliverability problems at Mailchimp, calendar-sync edge cases at Calendly. The agent owns tier-1 deflection on the deterministic share (password resets, plan changes, common deliverability fixes, OAuth reconnect flows), classifies the rest against your taxonomy, and drafts a response that the support engineer reviews and sends. For account-specific debugging (a specific deliverability issue tied to a customer's SPF and DKIM records, or a calendar-sync failure tied to a particular Google Workspace tenant), the agent pulls the relevant logs and configuration, runs the standard diagnostic playbook, and either resolves or assembles a one-screen handoff for the human. The support engineer reads the assembled context instead of starting from a fresh ticket. Average handle time drops measurably even when the deflection rate is modest.

Scope an agent for your Atlanta team.