AI & Machine Learning Company in Austin

A scoped AI proof of concept at Austin rates runs USD $35,000 to $80,000 over six to ten weeks, covering data audit, a baseline model, and a hosted demo. A custom production ML model (fraud scoring, churn prediction, document extraction, demand forecasting) typically lands at USD $110,000 to $260,000 including MLOps, monitoring, and a TDPSA-aligned model card. Full production AI systems with RAG, multiple models, fine-tuning, and enterprise integrations range from USD $220,000 to $1.1M. Austin rates sit roughly 10 to 15 percent below Bay Area benchmarks because Texas has no state income tax and the talent pool spans both Tesla and Apple alumni plus UT Austin graduates. We give fixed-fee proposals, not open T and M estimates.

TDPSA took effect July 1, 2024 and is the first comprehensive Texas privacy statute. It applies to any business that conducts activity in Texas, processes personal data, and is not a small business under the SBA definition. The Texas Attorney General has exclusive enforcement and has already issued public investigations into AI vendors. For AI projects we map controller and processor roles, document sensitive-data processing (which under TDPSA includes precise geolocation and biometric data), build consent UI for sensitive categories, and produce a data protection assessment for any high-risk processing including profiling and targeted advertising. Our model cards include a TDPSA risk classification so your legal team can answer an AG inquiry without a follow-up engagement.

The Texas Capture or Use of Biometric Identifier Act predates TDPSA and remains active. It defines biometric identifiers narrowly (retina or iris scan, fingerprint, voiceprint, record of hand or face geometry) and requires informed consent before capture, prohibits sale, mandates protection equivalent to other confidential information, and forces destruction within a reasonable time, no later than one year after the purpose ends. Statutory damages are USD $25,000 per violation, which scales catastrophically. For face recognition, voice cloning, and on-device biometric features we run a CUBI review during discovery, design consent flows that meet the statute, and build retention timers into the data layer so deletion happens automatically rather than depending on a future ticket no one prioritises.

Yes. Texas-resident inference is increasingly practical because Microsoft Azure South Central US (San Antonio) sits one hour from Austin, GCP us-south1 (Dallas) launched in 2022, and the Stargate AI Campus in Abilene is bringing OpenAI and Oracle compute fully inside the state. For clients with state-agency contracts under HB 2060, energy-sector buyers concerned about export control, or IP-sensitive R&D groups we deploy self-hosted Llama 3, Mistral, or Qwen on H100 clusters in Texas regions, route retrieval through Azure South Central US or GCP us-south1, and document every cross-region call. Cerebras CS-3 wafer-scale systems are also available for training workloads where H100 economics break.

When a project requires actual research (novel architectures, rare-event modelling, fairness work tied to Good Systems, or reinforcement learning beyond shelf algorithms) we scope collaborations with UT Austin Computer Science faculty, the AI Lab, or the Good Systems Bridging Barriers initiative rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Austin AI projects stall regardless of model quality. For standard work (RAG, fine-tuning, classical ML, computer vision on known architectures, document extraction) no academic partner is needed. We will tell you up front which bucket your problem fits, and we have introduced clients to UT Austin sponsored-research arrangements when the roadmap and budget justify it.

Texas HB 2060 (2023) created the Texas Artificial Intelligence Advisory Council and required an inventory of automated decision systems used by state agencies. For state-agency engagements we deliver an HB 2060-ready inventory entry covering system purpose, data sources, vendor identity, bias evaluation, and human oversight design. We pair this with a NIST AI RMF 1.0 risk profile, plain-language disclosure copy suitable for public records requests, and an impact assessment that anticipates the eventual successor statute Texas is widely expected to pass. The package is reusable across agencies and is structured so internal audit and the Department of Information Resources can sign off without redoing the work.

Texas has no state corporate income tax, so AI R&D credit positioning runs through the federal Research and Development Tax Credit (IRC Section 41) and, for qualifying entities, the Texas Franchise Tax R&D Credit which can apply to a portion of Texas-incurred qualified research expenses. Eligible AI activity generally includes novel modelling, architecture experimentation, algorithmic uncertainty, and systematic investigation, not routine integration. We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned to the IRS Section 174 capitalisation regime and Section 41 four-part test. Texas-domiciled startups can also access Texas Enterprise Fund and historically the Texas Emerging Technology Fund pathways. Final eligibility sits with your R&D tax advisor.

A typical Austin enterprise model (fraud scoring, demand forecasting, document extraction, defect classification for semiconductor lines) takes twelve to twenty weeks from kickoff to production, assuming clean historical data and TDPSA documentation as part of scope. Week 1 to 4 is data audit and baseline. Week 5 to 12 is modelling, iteration, and challenger testing. Week 13 to 16 is MLOps, monitoring, shadow-mode deployment, and internal review. Week 17 onward is gradual rollout. If you are pre-data or need labelling we add six to eight weeks. For Tesla supply-chain or Apple supply-chain work add two weeks for export-control review. We have shipped to this cadence inside Texas enterprise stacks repeatedly.