Generative AI Company in Austin

Where the model actually runs is the biggest single lever on an Austin GenAI budget, because air-gapped chip-design work is a different animal from a hosted RAG pilot. The tiers are a scoped Austin generative AI proof of concept over six to ten weeks, covering a data audit, a baseline RAG pipeline on Bedrock or Azure OpenAI, a basic eval harness, and a hosted demo, a custom production GenAI system (RAG assistant for an Atlassian-class SaaS, document extraction for a SailPoint-class identity vendor, generative copy for a Bumble-class consumer app) including LoRA / QLoRA fine-tuning where it pays off, prompt-injection defenses, evals, observability, and a NIST AI RMF model card, or a full production GenAI system with self-hosted Llama 3 / Mistral on dedicated GPU infra (typical for chip-design or Tesla-adjacent IP work) where scope includes air-gapped VPC deployment, fine-tuning pipelines, and multi-model routing. Austin rates sit above San Antonio and Houston because of the Oracle, Tesla, Samsung, and UT Austin talent premium. We give fixed-fee proposals.

TRAIGA (HB 149, signed June 22 2025) has been in force since January 1, 2026. It takes a narrower approach than the EU AI Act: a list of prohibited intent-based uses (social scoring, biometric identification without consent, manipulation causing harm, CSAM-adjacent generation), Texas AG-exclusive enforcement with a 60-day notice-and-cure window, a regulatory sandbox, and a safe harbor for organizations that follow the NIST AI Risk Management Framework. There is no blanket high-risk registration regime. Our discovery phase runs a TRAIGA prohibited-use screening on your use case alongside the NIST AI RMF risk profile — which doubles as the safe-harbor evidence — and every build ships with the documentation the AG would ask for first: intended-use statement, eval results, and human-oversight design.

Yes. Semiconductor IP — netlists, RTL, GDSII, verification test benches, yield data — does not touch a hosted frontier model in our deployments. For Samsung Austin Semiconductor, NXP, AMD, Silicon Labs, or Applied Materials Austin adjacent work, we deploy self-hosted Llama 3 70B, Mistral Large, or Qwen 2.5 72B on a dedicated GPU cluster (typically H100 or H200 on AWS p5 instances, or on-prem Nvidia DGX when the client owns hardware) inside a VPC with no egress to public LLM APIs. LoRA / QLoRA fine-tuning runs on the client's verification or yield data. We coordinate with the client's IP and export-control team upfront — chip-design work often touches EAR 5A002 controlled technology and US-person staffing requirements, which we route through US-based engineering on US soil with appropriate documentation. We will not run chip-design IP through a hosted Claude / GPT-4o / Gemini endpoint regardless of how attractive the latency is.

Consumer-scale generative AI at Bumble or Indeed scale faces continuous prompt-injection and jailbreak attempts as a baseline operational reality, not an edge case. Our defense stack layers (1) input filtering with NeMo Guardrails or Lakera Guard for known jailbreak patterns plus a classifier-based filter trained on the client's adversarial corpus, (2) structured output validation with JSON schema enforcement and refusal-conditioned generation, (3) tool-use sandboxing where any agent that touches a database, sends email, or modifies state runs in a restricted execution environment with allowlisted operations, (4) rate-limiting and anomaly detection on per-user prompt patterns, (5) red-team eval harnesses that run before every production push with both static jailbreak datasets (HarmBench, AdvBench) and continuously updated adversarial probes, and (6) human-in-the-loop review on flagged outputs. Section 5 FTC unfair-and-deceptive guidance and the Texas AG's consumer protection authority both bite if a generative AI product publicly fabricates harmful content.

When a project requires genuine research (novel architectures, rare-event detection, formal verification of LLM outputs, scientific ML for energy or aerospace simulation) we scope collaborations with UT Austin Computer Science faculty, the Oden Institute for Computational Engineering and Sciences, or the UT Austin Machine Learning Lab rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Austin generative AI projects actually stall. For standard work (RAG, fine-tuning, prompt-injection defenses, agent workflows on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits into. We have introduced Austin clients to UT Austin's industry affiliate programs when it fits their roadmap and budget, and we participate in the Austin AI Summit and ATX Hack for AI when those venues are running.

AWS does not yet have a full hyperscaler region in Texas, so our default cloud landing zone is AWS Bedrock in us-east-2 (Ohio, ~35ms from Austin) with us-west-2 (Oregon) failover. Azure OpenAI runs in South Central US (San Antonio, ~80mi south of Austin) at sub-15ms RTT, which is often the fastest commercial LLM endpoint for Austin. GCP Vertex AI is in us-south1 (Dallas, launched 2022, ~25ms RTT). For Oracle Austin enterprise clients we route through OCI Generative AI Service with the Cohere partnership. For TDPSA-sensitive workloads we keep all training data and inference in US regions, document data flows in the Data Protection Assessment, and avoid cross-border replication. For ITAR or export-controlled chip-design adjacent work we route through AWS GovCloud (US-West Oregon) with appropriate US-person engineering controls. Self-hosted Llama 3 / Mistral on a client's dedicated GPU cluster (on-prem or VPC) is the default for any workload where TDPSA data minimization or chip-design IP protection rules out hosted APIs.

A typical Austin RAG assistant (internal knowledge base copilot for an Atlassian-class SaaS, a SailPoint-class identity vendor, or an Oracle Austin enterprise customer) takes ten to sixteen weeks from kickoff to production, assuming clean source documentation and NIST AI RMF documentation in scope. Week 1 to 3 is data audit, source connector build, and chunking + embedding strategy decisions. Week 4 to 8 is retrieval pipeline build, evaluator harness, and baseline LLM selection (Claude 3.5 Sonnet on Bedrock vs GPT-4o on Azure South Central US vs Cohere Command R+ on OCI). Week 9 to 12 is prompt-injection defense, structured output validation, observability instrumentation, and integration into the client's product surface. Week 13 to 16 is staged rollout, eval-driven iteration, and a production-grade model card. If your team is pre-data or needs heavy chunking optimization, add three to four weeks. We have shipped to this cadence inside Oracle Austin, Atlassian Austin, and mid-market SaaS stacks.

Most custom generative AI work qualifies for the federal R&D tax credit under IRC Section 41, which returns a 6 to 10 percent effective benefit (depending on credit method and entity structure) against federal tax liability, with Section 174 capitalization (effective 2022, partially restored for domestic R&D under the 2024 Tax Relief for American Families and Workers Act) adding timing nuance. Texas has no state R&D income tax credit (no state income tax) but the Texas Sales and Use Tax exemption on qualified R&D equipment and software (Tex. Tax Code Sec. 151.3182) reduces the cost of GPU servers, software licenses, and contracted R&D services when properly documented. Eligible generative AI activity includes novel retrieval architecture, fine-tuning under technical uncertainty, prompt-injection defense research, eval-harness development, and multi-model routing experimentation — not routine API integration. We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned with IRS Form 6765. Final eligibility sits with your R&D tax consultant or CPA.