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LLM Integration · Houston, TX
Houston's operational documents are dense, technical, and consequential: daily drilling reports in rig shorthand, HSE narratives that feed regulatory exposure, joint operating agreements that govern millions in obligations. LLM integration in Houston means putting language models inside those workflows, deployed in your own tenancy, with domain glossaries from your engineers and citations on every claim.
We embed LLM features into your existing systems: field-report summarization, incident classification, contract extraction, and grounded search. Prompt design, model benchmarking on your documents, structured output, and audit logging are part of the standard build.
Tell us which documents your teams retype or re-read.
Energy text is a dialect. Rig shorthand, activity codes, and basin-specific naming defeat generic prompts, so the build starts with a glossary your engineers contribute once and the model consults forever. Extraction goes structure-first: verified numbers, then narrative on top, with anomalies flagged against plan. The eval set gets graded by people who can read a morning report, which is the difference between demo accuracy and field accuracy.
HSE work carries regulatory weight, so the model's role is assembly and consistency: classification against your matrix with quoted justification, backfill re-scoring that exposes hidden trends, and recordability decisions left with your specialists.
Contract intelligence in this market means JOAs, midstream agreements, and service contracts handled as amended families, extracted to schemas your land and commercial teams define, with clause citations on everything. Interpretation stays with counsel; the model just ends the era of keyword-searching scanned PDFs.
Deployment posture is usually the first question here, not the last. In-tenancy inference is the default, self-hosted open-weight models the fallback for the strictest data policies, and the trade-offs get benchmarked on your documents rather than asserted.
Six integration patterns we scope most often for energy, industrial, and healthcare operations.
Structure-first extraction from daily reports with anomaly flags against plan, and a cross-well morning summary where every number clicks back to source.
Consistent scoring against your severity matrix with quoted justification, backfill re-scoring of legacy incidents, and human-owned recordability calls.
Obligations, interests, and trigger clauses extracted across amended document families with clause citations, feeding obligation calendars your land team trusts.
Bedrock or Azure OpenAI inside your account by default, self-hosted open-weight models where policy demands, with the full data path documented.
Abbreviation and terminology sets built with your engineers, eval sets graded by domain readers, and regression runs on every prompt or model change.
Natural-language questions over reports, incidents, and agreements answered with citations, scoped by your existing permissions.
Houston's energy and healthcare sectors run document workflows where errors compound into safety exposure, regulatory findings, or missed contractual obligations. That stakes profile shapes the engineering: citations on every claim, confidence thresholds that route doubt to humans, and deployment inside infrastructure you already govern.
The healthcare side of the market, anchored by the Texas Medical Center's scale, brings HIPAA channels and clinical-text handling; the industrial side brings glossary-heavy extraction and in-tenancy requirements. We build both shapes with the same discipline: measured accuracy on your documents before production traffic.
We work with Houston teams remotely, with scope reviews and weekly demos on video in Central hours. Typical engagements run two to six weeks from kickoff to a feature in production.
Tell us the document flow, the teams that depend on it, and the isolation your data policy requires. We reply within one business day with a rough scope and a fixed price range.