AI Consulting in Houston

Energy services companies typically have data moving across a wide surface — field tablets, SCADA-adjacent systems, ERP platforms, and corporate networks that may include assets in multiple countries. Our builds use scoped service accounts with least-privilege access to the specific systems and data types the workflow touches. For field ticket automation, that means read access to inbound document queues and write access only to the target back-office record, nothing broader. We map every data flow on paper during scoping before any system access is provisioned, and the operations or IT lead at the client signs off on that map. If the client's data residency requirements keep records inside a specific environment — on-prem, a private cloud, or a regional cloud region — we deploy the integration layer there. The audit surfaces any gaps between how data actually moves today and how it should move for the build to work securely.

Any build touching PHI starts with a data flow map — where the information originates, what systems it passes through, who has access at each step, and what the covered entity's existing BAA structure looks like. We route clinical and administrative AI workloads through enterprise model endpoints that offer zero-retention data processing agreements, meaning prompts and outputs are not stored or used for training beyond the immediate request. A signed DPA is part of every TMC-footprint engagement. On the infrastructure side, if the organization's IT governance requires data to stay inside the health system's own cloud tenancy or on-prem environment, we deploy accordingly — the AI layer runs inside the perimeter rather than sending PHI to an external API. Workflow builds in this space typically go through the organization's privacy officer and IT security team before go-live, and we write the technical documentation to support that review, not just hand over a working product and disappear.

Yes, with significant upfront scoping on what data the workflow actually touches. ITAR restricts transmission of controlled technical data to foreign nationals, which means any AI build in the Clear Lake aerospace corridor needs to account for where model inference happens, who administers the system, and whether the data involved qualifies as controlled technical data under the US Munitions List. For most administrative and logistics automation — scheduling, document routing, internal communications workflows — the data involved isn't controlled and the ITAR constraints are manageable. For anything closer to technical design data or program documentation, we scope tightly with the contractor's export control officer before any build starts to confirm the workflow is in bounds. We don't shortcut that conversation. If the compliance path isn't clear, we say so before taking the engagement, not after.

Port logistics is an integration problem before it's an AI problem. Carriers send data in EDI formats, freight forwarders use their own portals, customs documentation arrives as PDFs, and warehouse management systems expect structured inputs that don't match any of the above. The first step is auditing what data actually comes in, in what formats, and where it needs to land — that mapping exercise is part of the $99 AI readiness audit and usually reveals two or three manual steps that are eating the most staff hours. From there, a typical first build handles one high-friction handoff: ingesting carrier documents in whatever format they arrive, extracting the structured fields the WMS needs, and pushing a clean record through without manual re-keying. EDI translation, customs document parsing, and exception routing are all tractable problems — they just need to be scoped against the actual systems in the stack, not a generic logistics template.

The $99 AI readiness audit. It's a structured assessment of where your current workflows break down — where staff hours are going, where data gets re-keyed between systems, where customer or client touchpoints fall through the gaps. For energy services companies, that's usually field ticket processing and exception routing. For medical center organizations, it's often patient intake coordination or prior authorization tracking. For aerospace and logistics operators, it tends to be document handling and system-to-system data transfer. The audit produces a written report that ranks three to five workflow candidates by estimated time savings, implementation complexity, and regulatory considerations specific to your sector. That report is the artifact you bring to an internal decision meeting — it's grounded in your actual systems and data, not a vendor pitch deck. From there, if one workflow stands out, we scope a fixed-price build. If the prioritization question is harder, a $497 Founder Review Call gets you ninety minutes with the founder and a written prioritization memo at the end.