AI & Machine Learning Company in Houston

We work from fixed-fee proposals, not open T and M, and scope each engagement rather than publishing bands. A scoped AI proof of concept is a six to ten week engagement covering data audit, a baseline model, and a hosted demo. A custom production ML model (predictive maintenance, seismic interpretation, oncology imaging triage, demand forecasting) adds MLOps, monitoring, and a sector-appropriate model card. Full production AI systems layer on RAG, multiple models, fine-tuning, and enterprise integrations, with energy-sector reservoir or seismic work occasionally pushing higher because of compute. Healthcare engagements that require HITRUST CSF readiness or FDA pre-submission packaging add scope on top. Houston runs roughly in line with Dallas, slightly below Austin, and meaningfully below the Bay Area. Fixed-fee proposals, not open T and M.

TMC engagements are HIPAA-critical by default and most member institutions require HITRUST CSF certified hosting with a signed Business Associate Agreement covering every subprocessor. We deploy in AWS us-east-1 and us-east-2 (HITRUST-inscope), Azure South Central US under the HITRUST shared-responsibility model, or GCP us-south1 with HITRUST controls extended through our deployment templates. PHI never leaves HITRUST-certified regions, model inference logs are scrubbed of identifiers before they reach observability tooling, and de-identification is handled under HIPAA Safe Harbor or Expert Determination depending on use case. For projects targeting eventual FDA 510(k) or De Novo we document training data lineage, version control, and validation aligned with FDA GMLP from week one so the submission is not blocked by reconstructable history.

Yes, this is one of our heaviest verticals. We have built predictive maintenance for rotating equipment using vibration, temperature, and SCADA tag histories; seismic interpretation pipelines on PyTorch and OpenFOAM with GPU acceleration; drill-bit and rate-of-penetration optimisation; production allocation and virtual flow metering; ESG and Scope 1 emissions forecasting under EPA Subpart W reporting requirements; and LLM-based knowledge extraction over decades of well logs, mud reports, daily drilling reports, and completion histories. Models follow API recommended practice (RP 75, RP 17, RP 1173 as relevant), respect FERC jurisdictional boundaries where transmission data is in scope, and are documented to survive the kind of operator audit that follows a Gulf Coast incident. We deliver inside ITAR-aware repos when export-controlled data is involved.

MD Anderson sets the global bar on AI-in-oncology and TMC tumour boards expect models that explain themselves at the same level a senior radiologist or pathologist would. We build DICOM imaging models on Monai and PyTorch with explicit attention maps and class activation visualisations rather than opaque scores, NLP pipelines over oncology EHR notes (Epic, Cerner) that surface staging, biomarker status, and prior therapy lines, and clinical decision-support RAG over NCCN guidelines and institutional protocols. Every model is built under an IRB protocol with documented training cohort demographics for bias evaluation, HIPAA Safe Harbor or Expert Determination de-identification, and FDA GMLP-aligned documentation so the path to 510(k) or De Novo is preserved. Output is delivered through a clinician-facing dashboard with explainability, not a black-box API.

We have done supporting work in the aerospace ecosystem and structure projects to be ITAR and EAR ready from the first sprint. US persons-only repositories, hardened cloud accounts in AWS GovCloud or Azure Government where the contract requires it, technical data segregation, and CUI handling per NIST SP 800-171 are all in scope. For Johnson Space Center work we map telemetry analytics, anomaly detection, and crew-system AI to the relevant NASA software class (typically Class A or B safety-critical) and document verification and validation accordingly. The Starbase pipeline at Boca Chica and the broader Houston aerospace cluster have similar expectations. We will scope ITAR registration coverage with your export-compliance team during discovery so there are no surprises mid-build.

We default to AWS us-east-1 (N. Virginia) for primary training, us-east-2 (Ohio) as the HITRUST disaster recovery pair (TMC member institutions typically require dual-region), GCP us-south1 (Dallas) when sub-15ms latency to Houston endpoints matters, and Azure South Central US (San Antonio) for Texas-resident compute. For PHI and any HITRUST-scoped data we stay inside HITRUST-certified regions with auditable failover. For export-controlled aerospace and defence work we use AWS GovCloud or Azure Government. Cross-border processing is acceptable only when a Privacy Impact Assessment and Business Associate Agreement allow it, typically for non-sensitive internal tooling. Residency is documented in the model card and the data processing agreement so HIPAA, HITRUST, and TDPSA auditors have a clear answer.

A typical Houston energy model (predictive maintenance, virtual flow metering, drilling optimisation, ESG forecasting) takes fourteen to twenty-four weeks from kickoff to production, assuming clean historical data and API recommended practice documentation as part of scope. Week 1 to 4 is data audit and baseline, often involving extraction from Pi historians, OSIsoft, IP21, or proprietary SCADA tag stores. Week 5 to 14 is modelling, iteration, and challenger testing against operator-validated thresholds. Week 15 to 20 is MLOps, monitoring, shadow-mode deployment in a control room or operations centre, and internal HSE and process safety review. Week 21 onward is gradual rollout across assets or basins. Oncology imaging and clinical NLP at TMC follow a similar cadence with additional IRB and FDA documentation overhead. Pre-data or labelling-heavy projects add six to ten weeks.

TDPSA took effect July 1, 2024 and is enforced exclusively by the Texas Attorney General. It applies to any business that conducts activity in Texas, processes personal data, and is not an SBA small business. For Houston AI projects we map controller and processor roles, document sensitive-data processing (which under TDPSA includes precise geolocation, biometric data, and health information not covered by HIPAA), build consent UI for sensitive categories, and produce a data protection assessment for high-risk processing including profiling and targeted advertising. Where HIPAA preempts TDPSA on clinical PHI we document the preemption clearly in the data processing agreement. Our model cards include a TDPSA risk classification so your legal team can answer an AG inquiry without a follow-up engagement.