AI & Machine Learning Company in Dallas

Two things set a Dallas AI budget: whether the workload touches export-controlled data, and how much production hardening the model needs. A scoped AI proof of concept for a non-regulated workload (Sabre-pattern travel-AI prototype, Toyota-pattern telematics prototype, AT&T-pattern fraud-detection baseline) is a six to ten week engagement. A production ML model adds full TDPSA documentation, SHAP-and-LIME explainability, and MLOps wiring. Defence-AI builds for Lockheed Martin Aeronautics, Bell, or Tier 1 supplier engagements carry the largest premium, because US-person-only staffing, an ITAR-controlled environment, DFARS 252.204-7012 incident-response wiring, CMMC 2.0 Level 2 evidence collection, and AWS GovCloud (US) or Azure Government deployment all land inside scope. Dallas sits below San Francisco and New York on labour but above Austin on defence specialisation, because the Fort Worth cluster commands an export-control premium.

Defence-adjacent AI engagements out of Fort Worth (Lockheed Martin Aeronautics F-35 and F-22 sustainment, Skunk Works programmes, Bell V-280, Tier 1 suppliers) demand the strictest export-control posture in US industry. Every defence-AI engagement begins with an export-control classification (USML category under ITAR or ECCN under EAR), confirms US-person-only access for any team touching controlled technical data, and operates inside a controlled-unclassified-information (CUI) enclave with DFARS 252.204-7012 incident-response wiring and CMMC 2.0 Level 2 minimum evidence collection (most Lockheed Martin Aeronautics flowdown requires Level 2 by 2027 phased rollout). Deployment runs on AWS GovCloud (US) or Azure Government, model weights and training data stay inside the enclave, and any cross-border data flow requires a documented export-licence pathway. AUKUS Pillar 2 advanced-capabilities sharing applies for select F-35 sustainment work with UK and Australian primes. We do not staff defence engagements with non-US-person personnel. Final ITAR or EAR licence determinations sit with the client's empowered official and the State Department's Directorate of Defense Trade Controls.

The Texas Data Privacy and Security Act (TDPSA, Texas Business and Commerce Code Chapter 541, effective July 1, 2024) is Texas's comprehensive consumer-privacy law. It applies to businesses that conduct business in Texas or produce products or services consumed by Texas residents, that process or engage in the sale of personal data, and that meet revenue or volume thresholds (with a small-business exemption tied to SBA criteria). For AI projects, TDPSA requires controller-versus-processor classification, a documented data-processing agreement with every sub-processor (including us, the AI vendor), consumer-rights-request handling for access, correction, deletion, portability, and opt-out of targeted advertising, sale, and profiling that produces legal or similarly significant effects, and a documented data-protection assessment (DPA) for high-risk processing including AI profiling. We build TDPSA conformance into the model card so the same documentation pack serves TDPSA, the Texas Attorney General's enforcement posture, and broader state-privacy-law obligations (CCPA, CPRA, Colorado, Connecticut, Virginia).

The Capture or Use of Biometric Identifier Act (CUBI, Texas Business and Commerce Code § 503.001) governs the capture of biometric identifiers — retina or iris scans, fingerprints, voiceprints, or records of hand or face geometry — for commercial purposes in Texas. CUBI requires notice and consent before capture, prohibits sale of biometric identifiers except in narrow circumstances, and imposes reasonable-care storage obligations with destruction within a reasonable time (and within one year of the purpose for collection being satisfied). CUBI is enforced by the Texas Attorney General with civil penalties up to USD $25,000 per violation, materially less stringent than Illinois's BIPA private right of action but still serious for any voice-AI, face-recognition, or biometric-fraud-detection workload. We build CUBI conformance into discovery for any AI project capturing face, voice, fingerprint, or hand-geometry data and document destruction timelines in the model card. Pindrop-pattern voice-security AI engagements get particular attention here.

Yes, for the unclassified and CUI portions of the workload and within the export-control posture described above. Lockheed Martin Aeronautics Fort Worth ships F-35 Lightning II and F-22 Raptor sustainment plus advanced Skunk Works programmes, and AI demand inside that ecosystem concentrates on predictive maintenance for F-35 sustainment (the Autonomic Logistics Information System and successor ODIN data), targeting and sensor-fusion ML under ITAR-controlled environments, and unmanned-autonomy ML for the Loyal Wingman and CCA programmes. Bell's V-280 Valor (FLRAA winner) and military rotorcraft AI work concentrates on flight-control ML and predictive-maintenance on rotor systems. We staff defence engagements with US persons only, operate inside Lockheed Martin's or Bell's controlled enclave or in AWS GovCloud (US) or Azure Government, and produce DFARS 252.204-7012 and CMMC 2.0 Level 2 evidence packs. Final classified work sits with cleared primes only; we are not a TS-cleared shop.

ExxonMobil Irving and the broader Permian Basin operator cluster (Pioneer Natural Resources, now part of ExxonMobil after the 2024 close, plus the Midland-Odessa upstream cluster) ship reservoir-modelling AI, predictive-maintenance ML on upstream equipment, and SCADA-stream anomaly detection under Texas Railroad Commission (RRC) oversight for oil and gas operations and FERC oversight for interstate pipeline and electricity-market workloads. Our energy-AI engagements bake RRC reporting requirements into the model-card audit trail (production reporting, well-integrity records under Statewide Rule 13, and methane-emission baselines under RRC and EPA OOOOb), SHAP-and-LIME explainability into the reservoir model so reservoir engineers can defend predictions to RRC field inspectors, and physical-security controls into SCADA-integration patterns aligned with API Standard 1164 and NIST SP 800-82. FERC Order 881 ambient-adjusted-ratings ML for ERCOT and broader electricity-market workloads adds a separate compliance lane.

We default to GCP us-south1 (Dallas, opened 2022) for the latency win — sub-5 ms intra-metro RTT to Plano, Irving, Frisco, Southlake, and downtown Dallas — with AWS us-east-1 (Northern Virginia, roughly 1900 km, 30 to 38 ms RTT) and AWS us-east-2 (Ohio, roughly 1500 km, 25 to 32 ms RTT) as multi-cloud fallbacks. For defence-AI workloads under ITAR, EAR, DFARS, and CMMC 2.0 we operate inside AWS GovCloud (US-East and US-West) or Azure Government with US-person-only access. For Toyota Connected and Lexus telematics workloads we sometimes pair with AWS Bedrock in us-east-1 where the model selection is broader. Sabre travel-AI workloads typically pin to AWS us-east-1 and us-east-2 for proximity to legacy Sabre GDS hosting. Energy-AI workloads under RRC oversight stay inside US regions by policy. Every residency choice is documented in the model card and the data-processing agreement so TDPSA and ITAR auditors have a clear answer.

An automotive AI build on the Toyota Connected or Lexus telematics pattern typically takes sixteen to twenty-four weeks from kickoff to production, assuming clean telematics historical data and a defined integration list (the Toyota Connected platform, AWS IoT Core, and downstream analytics surfaces). Week 1 to 4 is discovery, TDPSA classification, and a CUBI review if any biometric-driver-state data is in scope. Week 5 to 14 is modelling, iteration, and SHAP-and-LIME explainability work. Week 15 to 20 is MLOps wiring, shadow-mode deployment, and pre-production review. Week 21 onward is staged rollout. A defence-AI build on the Lockheed Martin Aeronautics or Bell pattern runs twenty-eight to forty-eight weeks because the export-control onboarding, DFARS incident-response wiring, CMMC 2.0 Level 2 evidence collection, and AWS GovCloud (US) deployment surface add roughly twelve weeks before the modelling team can start. Sabre travel-AI builds and ExxonMobil-pattern energy AI builds typically run twenty to thirty weeks.