Hire an LLM Engineer, Houston

Hire an LLM engineer in Houston.

Houston energy engineers at ExxonMobil, Shell, Halliburton, and Baker Hughes spend 8 to 10 hours per week writing reports from raw operational data: well performance summaries, equipment inspection reports, maintenance records. These reports follow a known structure with defined sections and required fields. An LLM pipeline that reads the raw data and drafts the structured summary reduces the writing portion to 2 to 3 hours of review and correction.

Texas Medical Center: extracting structured data from pathology reports, radiology reads, and operative notes for research databases and quality programs. Each document type has a different schema, but the engineering problem is the same: pull specific fields from unstructured text into a typed, queryable database record.

Pricing is scoped to the report and its data sources, quoted as a fixed price before work begins.

How we work

Describe the technical report your engineers spend hours writing each week.

What report writing actually costs in Houston energy.

A senior engineer at an ExxonMobil or Shell upstream operation earns $150,000 to $200,000 per year. At $80 to $100/hour fully loaded, 8 to 10 hours per week of report writing is $33,000 to $52,000 per year in engineering time per person. For a team of 15 engineers each writing weekly reports, that is $500,000 to $780,000 per year spent on a task that is fundamentally structured data formatting.

The value of a senior Houston energy engineer is in the analysis and the decisions, not in the time spent copying operational data into a report format. An LLM pipeline that produces the first draft from structured inputs redirects engineering time toward the judgment-intensive parts of the review.

For Texas Medical Center research programs at Houston Methodist, Baylor St. Luke's, or MD Anderson, the equivalent problem is research associate time spent abstracting fields from clinical documents. A research associate at $60,000 to $75,000 per year abstracts 20 to 30 records per day. An LLM extraction pipeline processes 200 to 300 records per hour.

The economics are clearest in the medical context because the cost-per-record calculation is straightforward: $0.06 to $0.15 per record for the LLM extraction versus $3.00 to $5.00 per record for manual abstraction, including overhead.

Two pipelines we build for Houston.

Operations report summarization for energy

Reads structured input data (sensor values, maintenance logs, production records) and generates a structured summary with four defined sections: metrics, flagged issues, recommended actions, and next service dates.

Numeric values come from structured input, not from model inference. No invented numbers. Engineer reviews the draft and edits the qualitative sections.

Integration: accepts input from your operational data system via API or file export, delivers the draft to your document system or directly to email.

Typical time savings: 8-10 hrs/week to 2-3 hrs/week per engineer

Clinical data extraction for Texas Medical Center

Extracts structured fields from pathology reports, radiology reads, and operative notes into a typed database record. Schema matches your research database field definitions.

Section-aware extraction handles multi-part clinical documents. Low-confidence extractions go to a human review queue.

Output writes to your REDCap database, Epic data warehouse, or custom research database via API or direct insert.

Typical throughput: 200-300 records per hour vs. 20-30 per day manually

How the build process works.

We start with your sample documents and your target output format. For operations report summarization, that means seeing 5 to 10 raw operational data exports alongside the corresponding finished reports your engineers currently write. We identify the structural pattern and design the pipeline around it.

For clinical data extraction, we start with your extraction schema (the fields your research associates currently key into the database) and 20 to 30 sample clinical documents with known correct extractions verified by your team.

Accuracy is measured against a labeled test set before go-live. Build time: 4 to 8 weeks from scope approval depending on document complexity. Post-launch support window is included.

Questions from Houston energy and medical teams.

What does an LLM report summarization pipeline look like for a Halliburton operations team?: The pipeline reads raw operational data: well performance logs, equipment sensor readings, and maintenance event records. It produces a structured summary with four sections: key performance metrics (with values extracted from the raw data), issues flagged during the period, recommended actions with the reasoning, and next inspection or maintenance dates. Each section is generated from specific input fields, so the engineer reviewing the summary can trace every statement back to its source data. Review time drops from 2 to 3 hours of report writing to 20 to 30 minutes of reviewing and correcting a draft.
How do you prevent the LLM from generating plausible-sounding but wrong technical values in energy reports?: For quantitative fields (pressures, flow rates, temperatures, production volumes), we extract the values directly from the structured input data rather than asking the model to read and transcribe them from text. The model receives the numeric values as typed inputs and uses them in the generated text. It cannot invent a pressure reading it was not given. For qualitative assessments (condition ratings, urgency classifications), we use a defined vocabulary with explicit options the model must choose from, and every qualitative statement is required to cite the specific input field it is based on.
Can the pipeline handle the scope of pathology reports for a Texas Medical Center research database?: Yes, with a nested extraction schema. A pathology report contains specimen information, gross description, microscopic description, diagnosis, and ICD codes. Each section has different field types and lengths. We build a section-aware extraction pipeline that identifies each section boundary, extracts the required fields from that section, and assembles the structured record. For Baylor St. Luke's or Houston Methodist research database population, the output schema matches your database's required fields exactly, including nullability and allowed value sets.
How does the LLM handle Baker Hughes technical reports that mix structured data with free-text observations?: Mixed-format reports require a two-pass extraction. Pass one: extract all structured fields (numeric values, dates, IDs, checkbox-style status fields) directly from the structured parts of the report. Pass two: pass the free-text observation sections to the LLM for qualitative extraction (fault descriptions, technician recommendations, anomaly narratives). The two passes are assembled into a single structured output. We define the schema for both passes before building, and accuracy is measured separately for structured and free-text fields.

Ready to reclaim engineering hours from report writing?

Send us a sample report and the source data it was written from. We will identify the automation path and send a scope within one business day.

Hire an LLM engineer in Houston.

Pricing is scoped to the report and its data sources, quoted as a fixed price before work begins.

What report writing actually costs in Houston energy.

Two pipelines we build for Houston.

Operations report summarization for energy

Numeric values come from structured input, not from model inference. No invented numbers. Engineer reviews the draft and edits the qualitative sections.

Integration: accepts input from your operational data system via API or file export, delivers the draft to your document system or directly to email.

Typical time savings: 8-10 hrs/week to 2-3 hrs/week per engineer

Clinical data extraction for Texas Medical Center

Extracts structured fields from pathology reports, radiology reads, and operative notes into a typed database record. Schema matches your research database field definitions.

Section-aware extraction handles multi-part clinical documents. Low-confidence extractions go to a human review queue.

Output writes to your REDCap database, Epic data warehouse, or custom research database via API or direct insert.

Typical throughput: 200-300 records per hour vs. 20-30 per day manually

How the build process works.

Accuracy is measured against a labeled test set before go-live. Build time: 4 to 8 weeks from scope approval depending on document complexity. Post-launch support window is included.

Questions from Houston energy and medical teams.

What does an LLM report summarization pipeline look like for a Halliburton operations team?

The pipeline reads raw operational data: well performance logs, equipment sensor readings, and maintenance event records. It produces a structured summary with four sections: key performance metrics (with values extracted from the raw data), issues flagged during the period, recommended actions with the reasoning, and next inspection or maintenance dates. Each section is generated from specific input fields, so the engineer reviewing the summary can trace every statement back to its source data. Review time drops from 2 to 3 hours of report writing to 20 to 30 minutes of reviewing and correcting a draft.

How do you prevent the LLM from generating plausible-sounding but wrong technical values in energy reports?

For quantitative fields (pressures, flow rates, temperatures, production volumes), we extract the values directly from the structured input data rather than asking the model to read and transcribe them from text. The model receives the numeric values as typed inputs and uses them in the generated text. It cannot invent a pressure reading it was not given. For qualitative assessments (condition ratings, urgency classifications), we use a defined vocabulary with explicit options the model must choose from, and every qualitative statement is required to cite the specific input field it is based on.

Can the pipeline handle the scope of pathology reports for a Texas Medical Center research database?

Yes, with a nested extraction schema. A pathology report contains specimen information, gross description, microscopic description, diagnosis, and ICD codes. Each section has different field types and lengths. We build a section-aware extraction pipeline that identifies each section boundary, extracts the required fields from that section, and assembles the structured record. For Baylor St. Luke's or Houston Methodist research database population, the output schema matches your database's required fields exactly, including nullability and allowed value sets.

How does the LLM handle Baker Hughes technical reports that mix structured data with free-text observations?

Mixed-format reports require a two-pass extraction. Pass one: extract all structured fields (numeric values, dates, IDs, checkbox-style status fields) directly from the structured parts of the report. Pass two: pass the free-text observation sections to the LLM for qualitative extraction (fault descriptions, technician recommendations, anomaly narratives). The two passes are assembled into a single structured output. We define the schema for both passes before building, and accuracy is measured separately for structured and free-text fields.

Ready to reclaim engineering hours from report writing?

Send us a sample report and the source data it was written from. We will identify the automation path and send a scope within one business day.