Landman Does Not Live on RAG Alone

Before working on Rowland, I built one of the most advanced Retrieval-Augmented Generation (RAG) frameworks. Through that, I was fortunate enough to help tens of thousands of independent developers and companies gain better retrieval over millions of documents each month.

It became clear, however, that RAG only gets you so far.

Those who worked in nuanced fields—where document understanding required deep contextual awareness and multi-step reasoning—found RAG hitting a ceiling.

Real-World Complexity in Energy

In energy, you're not just looking for facts. You're connecting division order requirements with JOA provisions, cross-referencing AFE approvals with operational authorities, and understanding how changes in ownership cascade through multiple agreements and regulatory filings.

While RAG excels at finding the right clause, it struggles when the answer requires:

Synthesizing information across interconnected documents
Understanding how an amendment to one agreement affects downstream obligations
Recognizing when critical stakeholder approvals are missing from the chain of title

Beyond Search

The reality is that AI intelligence exists on a spectrum. Most tools in our industry operate at the most basic level—keyword-based document search.

Rowland goes beyond that.

By combining retrieval, orchestration, and domain-specific reasoning, it’s able to:

Draft redlines and documents based on internal templates
Suggest next steps in a divestiture based on assignment terms
Track obligations across multi-party agreements

Why This Matters

Tools that rely solely on RAG frameworks are stuck at "find." But energy professionals need tools that can "understand" and "act."

That’s what we’re building with Rowland. And every day, we get a little closer.

Try Rowland today at Rowland.ai.