Building Enterprise-Scale LLM Applications: A Practical Guide
Learn how to design, implement, and deploy large language models at enterprise scale with considerations for security, cost, and performance.
Delivered in weeks, not months.
Built for oilfield services, power generation, and manufacturing teams.
We are a Houston-based analytics practice helping energy and industrial companies turn fragmented data into numbers leadership can trust.
With 20+ years of enterprise data and AI experience, our work is always senior-led. No junior handoffs. No learning on your time. You work directly with practitioners who understand how data supports real operations.
Our goal is simple: help teams stop debating numbers and start making decisions β by building clarity, ownership, and trust into reporting.
Sound familiar? You're not alone.
KPIs scattered across Excel, ERP, field systems, and finance reports. No single source of truth.
Different teams reporting different figures in the same meeting. Trust erodes before the meeting ends.
Reporting takes days or weeks to prepare, not seconds. By the time it's ready, it's already outdated.
Expensive dashboards exist but abandoned because they don't answer the operator's real questions.
"This is what growth looks like when systems evolve faster than foundations."
"This isn't a tooling problemβit's a foundation and clarity problem."
A proven framework to deliver operational excellence in weeks, not months.

We move teams from data chaos to operational clarity through these three specialized outcome buckets:
We replace manual, spreadsheet-driven reporting with a single, trusted view leadership actually uses β across operations, finance, and management.
We design pragmatic data platforms that support growth without disrupting operations β using cloud-native, lakehouse, and governance-first patterns.
(When the Foundation Is Ready)
Once reporting is trusted, we help teams apply AI and automation where it actually adds value β forecasting, anomaly detection, and intelligent workflows.
The same approach scales cleanly from global enterprises to Houston energy and industrial teams.
Designed and delivered a unified data platform that replaced fragmented reporting with a single, trusted source of truth across operations, finance, and leadership teams.
Defined and implemented a standardized data architecture that made operational and subsurface data consistently accessible across teams β without custom one-off solutions.
Applied AI-assisted validation to improve data accuracy and reduce manual review effort β ensuring teams trusted the numbers before acting on them.
Simplified deployment and change management across environments so reporting and data updates could move faster without breaking production systems.
Different scale. Same problems. Same disciplined approach.
Strategic guidance on data architecture, AI governance, and digital transformation for energy and industrial leaders.
Learn how to design, implement, and deploy large language models at enterprise scale with considerations for security, cost, and performance.
Explore real-world implementation strategies for data mesh architecture, including organizational changes, technical requirements, and success metrics.
A comprehensive guide to implementing MLOps in your organization, covering the entire machine learning lifecycle from development to deployment and monitoring.
Let's discuss your challenges and see if a Health Check is the right next step.
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