AI Consulting in Dallas

Energy data stays inside the perimeter. For field-ticket and invoice workflows, we deploy the processing layer inside the operator's existing cloud tenant — typically Azure or AWS — using VPC-isolated compute with no egress to external LLM endpoints unless the operator explicitly approves it. When we do route to an external model, it's through enterprise zero-retention endpoints where prompts and outputs are not logged or used for training. Vendor contract data and HSE compliance documents get the same treatment: read-only scoped access to the document repository, a service account with least-privilege permissions, and a written data-flow map the ops or IT lead signs off on before any credential changes hands. For oilfield services companies with field teams on spotty connectivity, we also build async queue architecture so the automation doesn't break when a field tablet goes offline — it queues and reconciles when connectivity returns.

Texas doesn't have a comprehensive consumer privacy law equivalent to California's CCPA as of mid-2026, but that doesn't mean the regulatory picture is blank. Energy operators working on federal leases are subject to federal data and records requirements. Healthcare organizations anywhere in Texas fall under HIPAA regardless of state law. Financial services firms with Dallas operations — banks, insurance companies, investment advisors — are subject to federal frameworks: Gramm-Leach-Bliley, SOC 2 expectations from enterprise clients, and SEC recordkeeping rules if they're registered. The practical effect for automation builds is that we scope data handling to the most restrictive applicable framework, not the least. Texas also has active enforcement around deceptive trade practices under the DTPA, which is relevant for any client-facing AI communication — automated intake bots and voice responders need accurate disclosure that the respondent is interacting with an automated system. We build that disclosure into every client-facing workflow.

HIPAA compliance for an AI build isn't a checkbox — it's architecture decisions made before the first line of code. We sign a Business Associate Agreement before touching any PHI-adjacent workflow. For scheduling and intake automation, patient-identifiable data is handled through HIPAA-eligible infrastructure: Azure Health Data Services or AWS HealthLake, both of which carry BAA coverage, rather than general-purpose AI endpoints. The automation layer — the scheduling bot, the intake triage, the referral routing — processes structured data fields rather than raw clinical notes wherever possible, which limits PHI exposure surface. When unstructured clinical text is in scope, it routes to a HIPAA-covered endpoint with zero-retention terms. Audit logging is on by default: every automated access to PHI-adjacent records is logged with timestamp, action, and the workflow trigger, so the practice has the documentation trail a compliance review or breach-notification assessment would require. We don't cut these corners for speed — a build that fails a compliance audit costs more than the build itself.

No replacement needed — these platforms expose APIs or structured data exports that automation can work with. CoStar's data is primarily accessed through their API for commercial data subscribers, and deal intelligence workflows can pull market comps and property data programmatically. Yardi has a REST API suite (Yardi Voyager, Yardi Commercial) that covers lease data, tenant records, and property financials. MRI Software similarly exposes integration endpoints for lease abstraction, GL, and tenant communication data. The practical build pattern for a brokerage or asset management group is: lease documents get abstracted by the AI layer and the structured output (key dates, rent steps, tenant obligations, options) gets written back into Yardi or MRI through the API rather than by a coordinator manually entering fields. Deal-stage updates in the CRM get triggered automatically when document milestones hit — executed LOI uploads, executed lease uploads — instead of relying on brokers to remember to update the pipeline. The integrations require API credentials and a scoped service account in each platform, which most IT contacts at mid-size firms can provision in under an hour.

Most mid-market professional services firms — consulting, staffing, IT services, accounting — start with the $99 AI readiness audit. It takes about fifteen minutes to complete and produces a written report that maps where time is actually leaking: how many proposals get built from scratch each week, how much time the ops or admin team spends on client onboarding paperwork, whether after-hours inquiries are getting captured or going to voicemail. The audit output is specific to the business, not a generic template. From there, two paths. If one high-leverage workflow is obvious — say, a professional services firm where every new engagement requires a custom proposal that takes four hours to assemble — we scope a fixed-price build around that workflow, typically two to four weeks, delivered and running. If the picture is more complex or the leadership team wants an outside prioritization perspective, a $497 Founder Review Call gets you ninety minutes with the founder and a written memo ranking three to five automation candidates by time-to-value and implementation risk. Either path starts with the audit.