Most AI dies as a pilot. We ship it to production.
Our wedge is simple. We have already put agentic AI into real production in industrial settings, not demos. A retrieval-augmented assistant over roughly a million rows of drilling history, a data hub serving over 190 million rows, conversational analysis tools, and an internal agent factory. We are the partner who gets your AI past the pilot and into the work.
The hard part was never the demo. It was making it survive contact with production.
Most AI initiatives stall the moment a polished proof of concept meets messy real data, real users, and real accountability. We have lived on the other side of that wall. For a major energy operator we built and now operate agentic AI on live industrial data, with retrieval over drilling history, conversational analysis at scale, and consensus extraction running every day. We bring that production discipline to your AI, so the project that impressed everyone in the demo actually ships and stays shipped.
Five engines read it. One answer survives the vote.
Our consensus extraction service is the working proof of multi-model consensus as a production pattern. Five independent engines, Azure Document Intelligence, Claude, Grok, PaddleOCR, and Tesseract, read the same document. The service reconciles every field by weighted vote, assigns confidence tiers, and routes anything uncertain to a human review queue. No single model is the point of failure, and no extraction lands in your data without an audit trail behind it.
Azure Document Intelligence, Claude, Grok, PaddleOCR, and Tesseract each read the document independently, then reconcile field by field.
Every field is settled by a weighted vote across engines, with confidence tiers so you know how much to trust each value.
Low-confidence fields fall through to a review queue, so the system escalates rather than guesses on the answers that matter.
Four verticals, one playbook.
We run agentic AI in real oilfield production today. A retrieval-augmented assistant answers questions over roughly a million rows of drilling history, and a data hub serves over 190 million rows to conversational analysis tools. This is the ground where our patterns were tested.
The discipline we built for subsurface drilling carries directly to geothermal borefield development. We bring the same data modeling, retrieval, and audit practices to the geothermal data problem, applying proven upstream methods to a new domain.
We specialize in taking the AI proof of concept that stalled and getting it into operation. Backed by genuine industrial IoT pedigree, we rescue the demo that never shipped and turn it into a production slice that runs.
Small and mid-size construction firms drown in documents, change orders, and field reports. We bring extraction, retrieval, and agentic workflows down to a footprint a lean operation can actually run and afford.
Assess what is real, ship a production slice, then operate it.
Before we promise anything, we separate the use cases that are ready to ship from the ones that need more data or more clarity. You get an honest map of what AI can do for you now, what it can do soon, and what it cannot do yet.
We pick one slice that delivers value and we put it into production, end to end, with real data, audit built in, and a human review path. A working system in front of real users beats a comprehensive plan every time.
Production is the start, not the finish. We monitor, tune, and extend the system as it runs, and we hand you the patterns and the agent factory so your team can keep building after we are done.
Production credentials, not promises.
Not slideware. A retrieval-augmented assistant over roughly a million rows of drilling history, answering real operational questions for real users.
A data hub serving over 190 million rows feeds conversational analysis tools that people use in their daily work. Scale is a solved problem here, not a research risk.
Our extraction service runs five independent engines over the same document and reconciles every field by weighted vote. Consensus is in production, not on a roadmap.
An internal agent factory lets us stand up new agents quickly and consistently. The second project moves faster because the machinery already exists.
Confidence tiers, source provenance, and a human review queue are built into how our systems answer, so you can trust an output or escalate it.
We connect to the corporate catalog master, the operational reporting system, and the real-time drilling and completions analytics platform already in your environment.
Get your AI past the pilot.
Bring us the proof of concept that stalled, or the use case you have not started yet. We will tell you what is ready to ship and we will ship it.