Hire an AI Consultant · Houston, TX

Hire an AI consultant in Houston.

Houston energy companies -- Shell, ExxonMobil, ConocoPhillips -- have made substantial AI investments. Many of those investments are not delivering expected ROI. The gap is almost always in one of three places: the use case wasn't defined clearly before the project started, the data was worse than expected, or the people who were supposed to use the AI tool were never trained on it.

An independent consultant does the post-mortem. Which failure mode applies? Is the project recoverable, or should the investment be redirected? What would a correctly scoped version of this project actually cost and deliver?

Houston Medical Center hospitals face a different problem: AI tools that require FDA clearance as Software as a Medical Device, and HIPAA data handling requirements that most AI vendors are not equipped to meet. Getting that analysis right before procurement saves months of remediation.

Fixed scope, fixed price agreed before work starts, based on the scope of the engagement.

How we work

Tell us about your AI investment that isn't performing.

Post-mortem analysis: finding the real bottleneck.

The three failure modes look similar from the outside. The fixes are completely different. Correct diagnosis is the entire value of the analysis.

Data quality failure

The model was trained on data with missing values, inconsistent labels, or a feature set that doesn't actually predict the outcome. The fix is a data remediation project, not more AI investment. We pull a sample and check before recommending anything.

Use case definition failure

The project launched without a measurable success metric agreed on by stakeholders. "Improve efficiency" is not a use case. A document classification model that was built for the wrong job requires a rescope, not a retrain. We look at the original brief and the actual business need.

Change management failure

The tool works. Nobody uses it. End users were not involved in specifying it and were not trained on it. This is recoverable without rebuilding. We interview end users to distinguish this from a technical failure before recommending a technical fix.

Healthcare AI: FDA clearance and HIPAA in the same decision.

Houston Medical Center is the largest medical complex in the world. The hospitals here face AI procurement decisions that require regulatory expertise most AI consultants don't have.

When does an AI tool require 510(k) clearance?

FDA's 2023 guidance on AI and machine learning-based Software as a Medical Device is the governing document. The key question: is the tool intended to influence clinical decision making for diagnosis or treatment? If a clinician would act on the output without independent verification, it is likely SaMD. We run the FDA classification analysis for each tool before Houston Methodist or Memorial Hermann procurement teams sign anything.

HIPAA compliance at the data processing level

Most AI vendors have a standard Business Associate Agreement template. For healthcare AI, the BAA needs to cover the specific data flows: what PHI enters the model, where it is processed, whether it is retained for model improvement, and what happens to it at contract termination. Vendors who resist those questions are disclosing a compliance risk before you have signed.

Administrative vs. clinical AI

Not all healthcare AI is SaMD. Prior authorization automation, coding accuracy tools, and scheduling optimization operate on administrative workflows. These have HIPAA implications but not FDA regulatory implications. The classification determines the procurement timeline and compliance requirements.

Vendor due diligence before the demo

We build a compliance questionnaire for each vendor before the demo is scheduled. FDA clearance status, HIPAA audit history, data retention practices, and subprocessor list. Vendors who can't answer those questions in writing before a demo are not viable for a Houston Medical Center deployment.

How we work with Houston clients.

Diagnosis before prescription

For post-mortem engagements, we don't recommend a fix until we have completed the diagnostic. The first deliverable is a written failure mode analysis. You see what we found before we recommend what to do about it.

Remote-first, async by default

Shared Slack channel, written updates every Friday, recorded walkthroughs for every milestone. Energy and healthcare companies have complex stakeholder environments. We produce written documentation that moves between teams without requiring everyone on the same call.

Fixed scope before work starts

Discovery call, written scope document, fixed price. You approve the scope before we invoice. Scope changes require a written change order. No surprise costs at engagement end.

Questions we get from Houston clients.

How do you diagnose whether an AI project failed because of data quality, use case definition, or change management -- they often look the same from the outside?: The diagnostic starts with three questions in parallel. First, data quality: pull a sample of the data the model was trained on or is operating on, and check it for completeness, consistency, and labeling accuracy. If the training data has 30% missing values in the key feature columns, the model was never going to work regardless of architecture. Second, use case definition: look at the original project brief and ask whether the success metric was measurable and agreed on before the project started. Projects that launched with vague goals like 'improve efficiency' almost always fail on use case definition. Third, change management: interview the end users. If the people who were supposed to use the AI tool were not involved in specifying it, and no one trained them, the tool may work technically but have zero adoption. These three failure modes have different fixes. Data quality failures require a data remediation project before any AI investment makes sense. Use case failures require resetting the scope and getting stakeholder alignment. Change management failures sometimes mean the tool is fine and the problem is organizational.
When should a company cut its losses on an AI project vs. trying to fix it?: Cut your losses when two conditions are both true: the core use case assumption was wrong, and fixing it would require rebuilding from scratch. If a document classification model was built to classify contract types but the actual business need was to extract specific clauses from those contracts, you have a use case mismatch. Retraining the model doesn't fix it because it was built for the wrong job. The second condition is cost: if the fix requires more than 60% of the original project cost, that money is usually better redirected to a correctly scoped new project. Try to fix it when the use case is right but execution was flawed -- bad data labeling, wrong model architecture for the problem, or inadequate test coverage. Those are recoverable. A $400k AI project at Shell that classified maintenance tickets with 62% accuracy because the training labels were inconsistent is fixable. A $400k AI project that tried to predict drilling outcomes from formation data that doesn't actually predict drilling outcomes is not.
What FDA guidance applies to AI tools used in clinical decision support -- when does a hospital need 510(k) clearance for its AI?: The FDA's 2023 guidance on AI and machine learning-based software as a medical device (SaMD) is the relevant document. The key question is whether the AI tool is intended to diagnose, treat, cure, mitigate, or prevent a disease or condition. Clinical decision support tools that analyze patient data and provide clinicians with recommendations about diagnosis or treatment are likely SaMD and require FDA clearance or approval. Tools that provide general reference information, calculate simple formulas, or support administrative functions are generally not SaMD. The practical test for Houston Methodist or Memorial Hermann: if a clinician would treat the AI output as a diagnosis or treatment recommendation without independent verification, it is likely SaMD. If the clinician treats it as one input among many and applies their own clinical judgment, it may fall into the non-device CDS category. The FDA has a decision flowchart in the guidance that most hospital AI procurement teams have not read. We walk through that analysis for each tool before you deploy.
What does a realistic AI ROI expectation look like for an oil and gas company -- what use cases have delivered positive ROI in the sector?: The use cases with the most documented positive ROI in oil and gas are: (1) predictive maintenance for rotating equipment -- compressors, pumps, and turbines -- where failure prediction 2-4 weeks out has reduced unplanned downtime by 15-25% in documented ConocoPhillips and ExxonMobil deployments; (2) drilling optimization models that reduce non-productive time by flagging lithology changes and bit wear in real time, with documented NPT reductions of 8-15%; (3) production anomaly detection that catches rate declines early enough to intervene before significant production loss. The use cases that have not delivered consistent ROI: AI-generated reports that summarize data engineers already have, chatbots for internal knowledge management that nobody uses after month three, and general-purpose LLM deployments with no specific use case definition. The difference between the successful and unsuccessful deployments is almost always use case specificity and data quality, not the AI technology itself.

Find out what went wrong -- and whether it's fixable.

Describe the AI project that isn't delivering. We reply within one business day with a rough scope and price range for the post-mortem analysis. No commitment required.

Hire an AI consultant in Houston.

Fixed scope, fixed price agreed before work starts, based on the scope of the engagement.

Post-mortem analysis: finding the real bottleneck.

The three failure modes look similar from the outside. The fixes are completely different. Correct diagnosis is the entire value of the analysis.

Data quality failure

Use case definition failure

Change management failure

Healthcare AI: FDA clearance and HIPAA in the same decision.

Houston Medical Center is the largest medical complex in the world. The hospitals here face AI procurement decisions that require regulatory expertise most AI consultants don't have.

When does an AI tool require 510(k) clearance?

HIPAA compliance at the data processing level

Administrative vs. clinical AI

Vendor due diligence before the demo

How we work with Houston clients.

Diagnosis before prescription

Remote-first, async by default

Fixed scope before work starts

Discovery call, written scope document, fixed price. You approve the scope before we invoice. Scope changes require a written change order. No surprise costs at engagement end.

Questions we get from Houston clients.

How do you diagnose whether an AI project failed because of data quality, use case definition, or change management -- they often look the same from the outside?

The diagnostic starts with three questions in parallel. First, data quality: pull a sample of the data the model was trained on or is operating on, and check it for completeness, consistency, and labeling accuracy. If the training data has 30% missing values in the key feature columns, the model was never going to work regardless of architecture. Second, use case definition: look at the original project brief and ask whether the success metric was measurable and agreed on before the project started. Projects that launched with vague goals like 'improve efficiency' almost always fail on use case definition. Third, change management: interview the end users. If the people who were supposed to use the AI tool were not involved in specifying it, and no one trained them, the tool may work technically but have zero adoption. These three failure modes have different fixes. Data quality failures require a data remediation project before any AI investment makes sense. Use case failures require resetting the scope and getting stakeholder alignment. Change management failures sometimes mean the tool is fine and the problem is organizational.

When should a company cut its losses on an AI project vs. trying to fix it?

Cut your losses when two conditions are both true: the core use case assumption was wrong, and fixing it would require rebuilding from scratch. If a document classification model was built to classify contract types but the actual business need was to extract specific clauses from those contracts, you have a use case mismatch. Retraining the model doesn't fix it because it was built for the wrong job. The second condition is cost: if the fix requires more than 60% of the original project cost, that money is usually better redirected to a correctly scoped new project. Try to fix it when the use case is right but execution was flawed -- bad data labeling, wrong model architecture for the problem, or inadequate test coverage. Those are recoverable. A $400k AI project at Shell that classified maintenance tickets with 62% accuracy because the training labels were inconsistent is fixable. A $400k AI project that tried to predict drilling outcomes from formation data that doesn't actually predict drilling outcomes is not.

What FDA guidance applies to AI tools used in clinical decision support -- when does a hospital need 510(k) clearance for its AI?

The FDA's 2023 guidance on AI and machine learning-based software as a medical device (SaMD) is the relevant document. The key question is whether the AI tool is intended to diagnose, treat, cure, mitigate, or prevent a disease or condition. Clinical decision support tools that analyze patient data and provide clinicians with recommendations about diagnosis or treatment are likely SaMD and require FDA clearance or approval. Tools that provide general reference information, calculate simple formulas, or support administrative functions are generally not SaMD. The practical test for Houston Methodist or Memorial Hermann: if a clinician would treat the AI output as a diagnosis or treatment recommendation without independent verification, it is likely SaMD. If the clinician treats it as one input among many and applies their own clinical judgment, it may fall into the non-device CDS category. The FDA has a decision flowchart in the guidance that most hospital AI procurement teams have not read. We walk through that analysis for each tool before you deploy.

What does a realistic AI ROI expectation look like for an oil and gas company -- what use cases have delivered positive ROI in the sector?

The use cases with the most documented positive ROI in oil and gas are: (1) predictive maintenance for rotating equipment -- compressors, pumps, and turbines -- where failure prediction 2-4 weeks out has reduced unplanned downtime by 15-25% in documented ConocoPhillips and ExxonMobil deployments; (2) drilling optimization models that reduce non-productive time by flagging lithology changes and bit wear in real time, with documented NPT reductions of 8-15%; (3) production anomaly detection that catches rate declines early enough to intervene before significant production loss. The use cases that have not delivered consistent ROI: AI-generated reports that summarize data engineers already have, chatbots for internal knowledge management that nobody uses after month three, and general-purpose LLM deployments with no specific use case definition. The difference between the successful and unsuccessful deployments is almost always use case specificity and data quality, not the AI technology itself.

Find out what went wrong -- and whether it's fixable.

Describe the AI project that isn't delivering. We reply within one business day with a rough scope and price range for the post-mortem analysis. No commitment required.