Generative AI Company in Dallas

The question that moves a DFW generative AI number is how deep into your existing systems the assistant has to reach. A scoped RAG proof of concept for a DFW enterprise (one corpus, one use case, hosted demo, eval set) is an eight to twelve week engagement. A production-ready internal copilot or customer-facing assistant typically runs sixteen to twenty-eight weeks, including evaluation, guardrails, observability, prompt-injection defences, and integration with existing identity (Okta, Entra ID), telephony (Genesys, NICE, Amazon Connect), and CRM (Salesforce Service Cloud, Microsoft Dynamics 365, ServiceNow). A multi-agent or multi-corpus enterprise generative AI platform sits above that. Inference and model costs are billed at cost-pass-through after a documented monthly cap. We give fixed-fee proposals after a one-week paid discovery, not open T and M. Anthropic, OpenAI, Azure OpenAI, and AWS Bedrock have direct enterprise contracts the client typically owns, not us.

Hallucinations are not solved by switching models. We approach this in four layers. First, retrieval design — most production hallucinations are retrieval failures, not generation failures, so we tune chunking, embeddings, hybrid search (BM25 + vector), reranking (Cohere Rerank, Voyage Rerank, BGE Reranker), and metadata filtering against a measurable retrieval-recall metric. Second, structured output — we use JSON mode, function calling, or constrained decoding (Outlines, Instructor) for any output that downstream code consumes, so the failure surface is schema-shaped, not free text. Third, evaluation — every generation path has a written eval set scored on factual grounding (against retrieved sources), refusal correctness, and downstream KPI delta. Fourth, human-in-the-loop — high-stakes outputs (clinical, financial advice, legal, claims decisioning) gate on human review with documented approval workflow. For regulated DFW clients we add an output classifier that routes high-uncertainty generations to human review automatically.

Yes. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (adopted December 2023) has been adopted by Texas and 20+ other states. It requires insurers to establish a written AI Systems Program covering governance, risk management, third-party AI system oversight, testing and validation, transparency, and consumer protection. Texas Department of Insurance (TDI) has issued aligned guidance for Texas-domiciled carriers. We build production AI features for DFW insurance clients (Liberty Mutual DFW back-office, USAA-adjacent partners, regional carriers) with documented model cards covering training data lineage, performance metrics, fairness testing across protected classes under the Texas Insurance Code Chapter 544 unfair discrimination provisions, bias mitigation steps, human override workflow, and incident response. Third-party AI vendor due diligence packets are prepared in the format TDI examiners and internal audit will accept.

Contact-centre generative AI is one of the most under-engineered enterprise AI categories. AT&T's Ask AT&T deployment (Azure OpenAI, internal use) is the public benchmark. American Airlines Fort Worth, Southwest Love Field, and the DFW BPO ecosystem run similar pilots. We build on top of the client's existing CCaaS — Genesys Cloud, NICE CXone, Five9, Amazon Connect, Cisco Webex Contact Center, or Avaya — using event APIs and real-time transcription (AssemblyAI, Deepgram, Speechmatics, Amazon Transcribe, or the CCaaS-native ASR). Agent-assist surfaces in the desktop within a 1.5 to 2 second latency budget, drives knowledge-base RAG against Salesforce Knowledge or ServiceNow KB, and writes structured after-call wrap-up that flows back into the CRM. We measure handle time, first-call resolution, agent CSAT impact, and escalation rate against a control group. For TCPA and HIPAA-adjacent contact-centre work we add consent capture and PHI redaction (Presidio, custom NER) before any payload reaches a frontier model.

Texas-domiciled clients with TDPSA, HIPAA, GLBA, or contractual data residency obligations have three realistic options. Google Cloud us-south1 (Dallas) is the only major hyperscaler region inside DFW, and Vertex AI hosts Gemini, third-party Anthropic Claude (via partnership), and self-deployed open-weight models there with sub-5 ms RTT to DFW offices. AWS us-east-2 (Ohio) is the closest AWS region — Bedrock hosts Claude, Mistral, Llama, Titan, and others with US data residency. Azure South Central US (San Antonio) hosts Azure OpenAI Service with US data residency and is the natural fit for AT&T-style Azure OpenAI enterprise deployments. For HIPAA we use Bedrock HIPAA eligibility, Azure OpenAI BAA, or self-hosted open-weight on EKS / GKE / AKS with our own BAA. We document residency in writing in the model card and the DPA, including any cross-region replication for DR.

Most DFW enterprise generative AI problems are retrieval problems, not modelling problems — RAG handles them. RAG (retrieval-augmented generation) is right when the answer depends on facts in a corpus that changes (policy documents, product catalogs, claims files, support knowledge bases, regulatory filings). Fine-tuning is right when you need style, format, or domain language the base model does not reliably produce — call summarisation in a specific schema, code generation in a proprietary DSL, document classification with domain-specific labels, low-latency or cost-sensitive routing on a smaller model. Prompt engineering and few-shot is right when behaviour is well-handled by frontier models with a written specification and well-chosen examples. We almost never fine-tune frontier models for factual recall — RAG is more accurate, more updateable, and easier to audit. We do fine-tune smaller open-weight models (Llama 3.1 8B / 70B, Mistral, Qwen) when the routing economics or the latency budget rule out frontier inference.

Prompt injection is the OWASP LLM Top 10 number one risk for a reason — there is no fully reliable defence. We use a defence-in-depth approach. First, input sanitisation: structured input schemas, allowlist patterns where possible, and rejection of obviously adversarial inputs (Lakera Guard, NeMo Guardrails, custom classifiers). Second, separation of trust: untrusted retrieved content is delimited and never grants tool-use permissions, and user input cannot escalate system-message instructions. Third, output guardrails: PII redaction (Microsoft Presidio), toxicity and policy classifiers, and structured output validation. Fourth, capability scoping: agentic tools have least-privilege scopes, write actions require human-in-the-loop confirmation, and any external API call is rate-limited and logged. Fifth, red-team: we run scripted jailbreak suites (Garak, PyRIT, ManySShot baseline) against every production system before launch and on every model upgrade. We document residual risk in writing — pretending the system is unjailbreakable is dishonest.

A scoped internal RAG copilot for a DFW enterprise (single corpus, single use case, integrated with Okta or Entra ID, deployed to a Teams or Slack frontend or a custom React app) takes ten to sixteen weeks from kickoff to limited production rollout, plus four to eight weeks of evaluation tuning and rollout to broader user base. A customer-facing generative AI feature (consumer chat, support assistant, product search) takes fourteen to twenty-six weeks because external-facing UX, brand-voice tuning, abuse detection, and load testing add scope. An agentic workflow with tool use, multi-step planning, and write-actions to downstream systems takes twenty to thirty-six weeks because the failure modes are real and the testing surface is larger. We do not ship generative AI features whose evaluation set is the founder's personal Slack messages — every shipped feature has a written eval that the client signs.