Talking about AI and everything under its umbrella (like LLMs) is starting to sound like alphabet soup. And for a lot of land teams, it might as well be. So, what’s the difference between LLM, AI, ML, and NLP? And why does it matter?
LLMs have some seriously exciting implications for contract analysis in oil and gas, especially for how operators manage land contracts, prepare data rooms, and surface obligations buried deep down in decades of manual document management. Especially in A&D scenarios, we’ve seen how automation can reduce post-close risks and accelerate decision-making. In an industry where a day of delayed operations can cost millions, those inefficiencies and risks add up fast.
To understand what’s changing, let’s walk through the basics, then show you why generic AI won’t cut it for land, and what it takes to make LLMs useful for contract analysis in oil and gas.
Start with the stack: AI > ML > NLP > LLM
What’s the AI equation, and what does it all mean?
Artificial Intelligence (AI) is the umbrella. Think of it as the systems designed to perform tasks that typically require human abilities like reasoning, learning, perception, and decision-making.
Machine Learning (ML) is a subset of AI that learns patterns from data to make predictions or decisions without being explicitly programmed for each specific task.
Natural Language Processing (NLP) is a branch of ML that has to do with helping AI understand, interpret, and generate human language in text or even speech.
Large Language Models (LLMs) are an advanced type of NLP model that is trained on massive amounts of text. They can predict and generate language, surface patterns, and answer questions based on context.
If you’re visual, think of this as a funnel. At the top, AI includes everything. At the bottom, LLMs are where language-focused intelligence lives.
For oil and gas, it may help to think of AI as the system that organizes and prepares your data. LLMs are the part that lets you ask questions of that data once it’s cleaned up.
So when people say they’re “using AI,” they often mean they’re using an LLM.
But most LLMs weren’t built for land
By now, most people in the workforce have seen or experimented with tools like ChatGPT, Microsoft Copilot, or Google’s Bard/Gemini. They’re impressive, yes. But they’re generalists.
Using a general-purpose LLM for land contract analysis is like hiring someone who:
- Knows the theory but has never set foot in the field
- Struggles to read between the lines of your data
- Makes things up when they don’t know the answer
- Can’t be trusted with sensitive information
These models have read a lot (terabytes of books, websites, Wikipedia, etc.), but that doesn’t necessarily mean they’ve read the right data. And when they don’t know something, they tend to make things up, also known as “hallucinating.”
A recent Stanford study found that general-purpose LLM chatbots hallucinated answers between 58% and 82% of the time on legal questions. (In one notorious case, a New York lawyer faced sanctions for citing cases ChatGPT invented in a brief.)
And that’s the problem.
Land contracts aren’t like HR docs or marketing emails. They’re deeply specific, often messy, and absolutely critical to operational continuity and compliance. The format is inconsistent. The language shifts by province, company, or even decade. Some are handwritten. Others are digitized from microfiche. You can’t just throw these into a general-purpose model and expect clean answers.
Consider a seemingly straightforward question like: “For all agreements in Area X, is there a Cessation Clause?”
With a generic AI, you might miss relevant clauses if the language is phrased differently, for example, describing a stop in operations without using the exact term “Cessation Clause.” That means you risk overlooking critical obligations and spending extra time digging through contracts manually.
To extract real value for contract analysis from an LLM, you need an LLM model that’s been trained on the real world, instead of just language. One that, as we like to say, has earned its hard hat.
Why land contract analysis is uniquely difficult
People outside the industry assume land contracts are standardized. Anyone who’s actually worked with them knows better.
A single lease may include a head agreement, multiple amendments, farm-outs, royalty agreements, and a chain of title documents, all interrelated but rarely packaged neatly together.
Some contracts are structured and well-typed. But many are not. Some include copies of all their amendments and exhibits; others reference external documents you have to hunt down elsewhere. Some leases span a few pages, while others fill a large filing cabinet. Many don’t follow a consistent naming convention. And older agreements often use dated or region-specific language that can be harder to interpret, especially when terminology has shifted over decades.
Yet, land and legal teams like yours are expected to perform tasks like:
- Extract key provisions: Expiration dates, rental rates, held by production limit, zonal rights, royalty rates, and development obligations
- Identify obligations, risks, and missing documents: For example, spotting when a required consent or surface agreement isn’t in the file.
- Link related documents: Connecting Exhibit A or later amendments back to the original lease contract, even if they weren’t attached.
- Tag metadata: Lease ID, well/UWI, parties, effective dates, depths, commodities, and other critical fields.
- Summarize complex agreements: Distill a 50-page contract to answer a due diligence question like “What conditions could terminate this lease?” or “What surface rights are granted?”
When contract analysis is done manually, across PDFs, scanned files, and shared drives, it takes weeks, and there is a lot of room for human error.
When contract analysis is done using generic AI, it might be faster, but it’s often wrong. Summaries are vague, metadata is inconsistent, and clauses can get missed or fabricated entirely. Because of the amount of context you need for land contracts, they pose a unique challenge for AI.
What makes domain-specific LLMs different?
As we stated before, generic models like GPT-5 and Gemini are trained on massive amounts of internet text. That gives them range, but not precision.
Domain-specific LLMs flip that equation. Instead of being trained on everything, they’re trained on the right things. They sacrifice some breadth for depth in the areas that matter.
StackDX’s model, for example, is trained on over 50+ million energy-specific documents. That includes:
- Typed and handwritten freehold/private lease agreements
- Crown/state leases, title chains, and title opinions
- Assignments (notices of assignment/NOAs) and AFEs
- Correspondence and email chains between parties
- Documents from every basin and operator type
From the moment documents enter the system, they’re handled by AI that knows the difference between a surface lease and a title opinion (US) or title chain (CA). If the answer isn’t there, it tells you instead of guessing to keep the conversation going.
When we ask the model to pull specific details, we can enforce structure in the data it returns. If we request a date, it’s extracted in a format ready to store in a database, with basic QA to flag results that do not align with what we know about the lease or well. If we ask for the number of days held by production, the output is an integer, and in most cases, it will be under ~180 days. By forcing structure in our prompts, we get a level of quality control you do not typically see with a generic model.
And when you ask it a question, the model doesn’t just guess to keep the conversation going. If the answer isn’t there, it tells you.
That blend of speed and accuracy matters, especially in compliance-heavy industries and workflows. We’re not the only ones doing this. Domain-specific LLMs are showing up everywhere precision counts:
- In finance, firms are using models trained on investor filings and regulatory disclosures to spot risk and draft reports
- In healthcare, clinical language models help surface conditions from patient records without misinterpreting medical shorthand
- In legal, LLMs trained on case law and statutes are already being used to write motions, summarize precedent, and flag conflicts
- In insurance, underwriters are using AI to pull obligations from decades of policy documents, with traceability built in
That doesn’t mean domain-specific models never make mistakes. Human oversight still matters, especially in high-stakes work like contract analysis. It’s another tool in your toolbox to help you start from a clean, structured clause-linked base.
It starts with document intelligence
One hard-earned lesson in applying an LLM is that garbage in = garbage out.
If you feed an LLM model disorganized or contextless information, you get unreliable results. Our platform addresses this by using a document management system to clean, classify, and enrich all the inputs before they ever reach the language model. Think of it as building a rock-solid foundation underneath the LLM.
Stack’s structured approach means filtering first, then asking. By using structured data to target the LLM, we can also validate the output against known facts.
Key steps include:
- Entity linking: The system links documents to the assets (wells, facilities) or land agreements they relate to, using system data. For example, a lease agreement can be linked to land system data, related wells, and its geospatial location. An assignment can be linked along a title chain. A map or plat can be linked to the relevant tract or well.
- Document classification: Every file is tagged by type. Is this a surface lease? A mineral lease? A right-of-way agreement? A drilling report? Knowing the document type gives context to the LLM.
- Metadata extraction: Important fields are pulled out using traditional NLP or regex parties, dates, legal descriptions, or UWI, lease numbers, related well IDs, etc. If a document references another (e.g., “see Exhibit B”), that reference is noted.
By the time our LLM looks at your data, it’s not just a random pile of text. It’s ready to be pulled from, understood, and traced back to the source.
StackDX vs. generic tools: a side-by-side look
Unlike point solutions that lock you into a single provider’s ecosystem, StackDX operates as a complete platform designed specifically for energy document workflows. Our data control means your sensitive lease information stays within your secure cloud environment.
Our model flexibility ensures you’re not tied to today’s technology as the field rapidly evolves. We integrate with multiple LLM providers to deliver the best performance and value.
And our partnership approach means that, in addition to an LLM, real humans are available to work directly with your team to configure the right solution for your specific oil and gas needs.
Here’s a better look at StackDX vs two popular AI tools (ChatGPT and Gemini):
| Capability | ChatGPT (GPT-4) | Gemini (Google) | StackDX |
|---|---|---|---|
| Provision extraction | Output isn’t consistently structured/easy to validate | Output isn’t consistently structured/easy to validate | Handles fragmented, real-world leases with precision |
| Entity linking | Limited to document/context window | Limited to document/context window | Linked to system and government data |
| Interpretation accuracy | General legal knowledge | Slightly improved prompting | Fine-tuned in energy data like O&G contracts and well reports |
| Data control | Hosted by OpenAI | Hosted by Google | Secure Azure hosting with client data isolation |
| Model flexibility | Locked to OpenAI | Locked to Google | Model agnostic by integrating with multiple LLM providers |
| Partnership approach | Generic support | Generic support | Works directly with clients on use cases and value delivery |
Real-world contract workflows StackDX supports
All of this might sound abstract, so it’s worth underscoring: this isn’t a hypothetical toolset. Land teams today are already using StackDX’s AI (and similar domain-tuned solutions) for high-stakes, real-world workflows such as:
A&D (Acquisition & Divestiture)
Quickly evaluate what’s in and missing from the land file. If you want early insight on operator or asset moves in your region, then solutions like Stack’s market and asset intelligence give you tracking and alerts on competitor/neighbouring well activity and the intel helps you build your chain of title.
Lease Compliance Monitoring
Track continuous development requirements, held-by-production status, and region-specific variations with automated compliance workflows. Our LLM analyzes the production cessation provision to identify which leases should be reviewed and when, and you get alerts before conditions are missed.
Data Room Prep
Query all documents related to the land and assets being sold and assemble them in seconds. StackAI identifies gaps in the data, performs bulk extraction, and maps relationships to system data that need to be transferred in the deal. The result is a package where documents have meaningful names and attributes for the purchaser, without requiring extensive administrative review.
Expiry Reviews
Filter and prioritize upcoming lease expiries. Assign review to the right team with visibility into relevant history and obligations.
Each workflow is about augmenting the land team, not replacing them. The LLM isn’t going to negotiate a lease for you or decide whether to farm out a well, but it will ensure you have all the relevant info at hand and reduce the time spent clicking through folders or perusing cabinets.
What contract analysis looks like in practice
Let’s bring it down to ground level with a hypothetical (but totally plausible) scenario:
Let’s say your land team is reviewing a lease flagged for possible divestiture. The file includes a base agreement, four amendments, a few scanned exhibits, and an internal memo summarizing production history.
You need to confirm whether the lease is currently held by production and if any terms could put that status at risk.
With StackDX, here’s what happens next:
- The system pulls up all related documents already linked to that tract and lease ID.
- It extracts and groups any clause related to cessation of production, development obligations, or expiry timing.
- It calculates the number of days the well can be shut-in, pulling that figure directly from the contract (data extraction) and connecting it with related production data from the database to provide operational context.
- And it links every insight back to the original page and file, so you’re not relying on interpretation; you’re instantly validating the answer.
If something’s unclear, that contract is flagged for review, while everything else moves forward.
This is how domain-specific AI shows its value: not in a flashy interface, but in real work that used to take half a day and now takes a few minutes. And it gives you a lot more confidence that you’ve done the work correctly and to the best of your ability.
So what does all this mean?
It means land teams don’t have to choose between doing everything manually (slow and tedious) and blindly trusting a one-size-fits-all AI (fast but risky). As we’ve explored in our broader look at AI’s role in land workflows, using domain-specific LLMs for contract analysis in oil and gas gives you the best of both worlds: speed and accuracy.
The output is trustworthy because the model actually understands the input. It’s not a black box guessing at contract language.
Instead, it’s a very specific, purpose-built tool that’s seen thousands of leases before and knows what to look for. You get clause-level precision, source-linked summaries, and workflow integrations that help your team stay ahead of obligations instead of reacting after something falls through the cracks.
The technology isn’t replacing land expertise, even though you may be concerned about this. It’s amplifying it, and we still need humans for their nuance, years of expertise, and critical thinking. All those years of experience your land team has, all the intuition about how a deal comes together or where to be suspicious in a contract, that’s still essential.
In an oil and gas context, that means a land professional armed with a land-trained LLM can accomplish in a day what might have previously taken a month, and do it with greater certainty.
The AI surfaces the insights, but you make the judgments and decisions equipped with those insights.
