Generative AI Company in Atlanta

How many systems the model has to read from is the question that settles an Atlanta GenAI budget. Work is delivered as a scoped GenAI proof of concept (single use case, single channel, basic RAG) over six to ten weeks at Atlanta rates, including discovery, an eval set of 200+ examples, a working prototype on Bedrock or Azure OpenAI, and an ROI memo, a production customer-service or internal copilot with multi-tool orchestration, PII redaction, prompt-injection defences, and observability over fourteen to twenty-two weeks, or an enterprise-scale deployment (Delta-style IRROPS copilot, Home Depot-style retail assistant, Cox Automotive-style listing generation) scaled by integration count, fine-tuning requirements, and ongoing eval and red-team cadence. Atlanta rates sit below Manhattan and SF but above Charlotte and Nashville because Georgia Tech and Emory CS produce strong applied LLM talent. We give fixed-fee proposals broken into discovery, build, and operate phases.

Yes, and this is one of our deepest patterns given Atlanta's payments concentration. The architectural rule is simple: cardholder data (PAN, full track data, CVV) must never leave PCI-DSS scope, and therefore must never reach a foundation model log, prompt, or fine-tuning dataset. We implement tokenisation at the edge (your existing PCI tokeniser or a new one through Stripe/Spreedly), route only tokenised references through the LLM, and reconstruct PAN data only inside your existing PCI environment for actions like refund issuance. Foundation models run on Bedrock or Azure OpenAI under enterprise contracts where prompts are not used for training and 30-day retention can be disabled. SOC 2 Type II evidence flows from our infrastructure-as-code, logging through CloudTrail or Azure Monitor, and quarterly access reviews. We have shipped this pattern for Atlanta payments and banking clients and the QSA reviews have been clean. The same architecture extends to Georgia HB 156 (data breach disclosure) obligations.

Every production engagement goes through OWASP LLM Top 10 red-teaming before launch. For prompt injection (LLM01) we use defence-in-depth: input sanitisation with Lakera Guard or Prompt Armor, system-prompt isolation that treats user input as data not instructions, output validation through Guardrails AI or NeMo Guardrails, and least-privilege tool calling where the LLM cannot reach destructive APIs without human approval. For insecure output handling (LLM02) we never render LLM output directly to a browser without sanitisation, never execute LLM-generated SQL without parameterisation, and never let an LLM call shell commands. For training data poisoning (LLM03) we hash and version every fine-tuning dataset with a documented provenance trail. For sensitive information disclosure (LLM06) we run Presidio or Nightfall scrubbing on every input and output. Quarterly red-team exercises use Promptfoo, Garak, and HackAPrompt-style adversarial sets. Findings feed back into the eval suite.

Depends on the use case and compliance scope. For customer-facing copilots on regulated data (PCI, HIPAA), we default to Claude 3.5 Sonnet on AWS Bedrock because the enterprise contract, HIPAA eligibility, and AWS BAA make compliance straightforward, and Claude's instruction-following beats GPT-4 on long-context retrieval-heavy tasks. For Microsoft-aligned shops (Cox, NCR Voyix) we use Azure OpenAI GPT-4o under the Microsoft enterprise data agreement. For Google-aligned clients Vertex AI Gemini 1.5 Pro is competitive, especially for multimodal use cases. We fine-tune open-weight Llama 3.1 70B or Mistral Large only when (a) the use case has narrow domain language where fine-tuning beats RAG on cost and latency, (b) regulatory scope (some federal or healthcare workloads) prohibits sending data to a hosted frontier model even under BAA, or (c) inference economics demand sub-cent-per-call costs at scale. We will give you a benchmark comparison on your real eval set, not a vendor preference.

We refuse to ship without an eval set, and we will help you build one in discovery. Minimum 200 examples covering the realistic distribution of your data, with ground-truth answers labelled by your subject-matter experts (not by us, because we are not Delta gate agents or Home Depot Pro desk reps). Evals run on every prompt or model change through Ragas, DeepEval, or Promptfoo with metrics that match the use case: faithfulness and context recall for RAG, exact-match or F1 for extraction, BLEU/ROUGE for summarisation only with caveats, and pairwise LLM-judge for open-ended generation with human spot-checks at 5 percent sample rate. We set acceptance thresholds before build starts (typically 85 percent faithfulness, 90 percent groundedness for RAG) and we will not deploy below the threshold. For hallucination reduction the levers are retrieval quality first (chunking, embedding model, reranker), prompt engineering second, and only then fine-tuning. We do not chase fine-tuning to fix bad retrieval, which is the most common Atlanta GenAI failure mode we are asked to remediate.

We have integration patterns for SAP S/4HANA (Delta operations backbone), Oracle Retail (Home Depot inventory), Salesforce Service Cloud (used heavily across Atlanta enterprises), Manheim's MMR API (Cox Automotive wholesale pricing), and the major contact-centre platforms (Genesys, NICE inContract, Five9, Amazon Connect). For real-time tool calling we use LangGraph or custom function-calling orchestration with strict input/output schemas validated through Pydantic. For batch use cases (overnight description generation, daily summarisation) we run on AWS Batch or Step Functions with idempotency keys. Authentication into enterprise systems uses OAuth 2.0 client-credentials or mutual TLS, with secrets in AWS Secrets Manager or HashiCorp Vault. We coordinate with your enterprise architecture team early to avoid the classic GenAI integration failure where a working prototype cannot get past security review because nobody scoped IAM roles, network paths, or data classification until week 12.

A focused internal copilot (one workflow, one data source, one channel) takes ten to fourteen weeks from kickoff to production for an Atlanta enterprise, assuming clean source data and a real eval set built in discovery. Customer-facing copilots take fourteen to twenty-two weeks because the red-team, legal review, and human-in-the-loop rollout add four to six weeks even after the model is technically ready. Multi-agent or multi-tool systems (Cox Automotive listing pipeline, Home Depot Pro desk assistant) take twenty to thirty weeks because each tool integration carries its own auth, error handling, and observability work. Fine-tuned open-weight deployments add four to eight weeks for data curation, training runs, and eval. We ship to these cadences with EST-hour weekly demos and a monthly Atlanta on-site if the client is Buckhead, Midtown, Sandy Springs, or Alpharetta-based and prefers in-person reviews.

Yes, with appropriate caveats. HIPAA-eligible GenAI in Atlanta runs on AWS Bedrock under a signed BAA (Claude 3.5 Sonnet, Claude 3 Haiku, Titan, and Llama via Bedrock are BAA-covered), Azure OpenAI under the Microsoft BAA, or self-hosted Llama 3.1 / Mistral on HIPAA-eligible infrastructure when scope demands it. Protected Health Information never leaves the BAA-covered environment, prompts and completions are not retained beyond what the BAA allows (Bedrock defaults to no retention, Azure OpenAI requires opting out of content monitoring), and Presidio or Nightfall PII scrubbing runs on all logs. For CDC-style public-health research workloads we evaluate Snowflake Cortex or on-prem open-weight deployments depending on data classification (PII vs de-identified vs aggregate). Emory and Piedmont clinical documentation pilots require IRB review and integration with Epic or Cerner, which we scope with their informatics teams. We will not take a clinical GenAI engagement without a documented HIPAA risk analysis and a clinician-facing override mechanism for every model-suggested decision.