Replace your in-house wiki with AI

Better than any other LLM

The same great technology that powers AppIntel AI can also power your in-house search. Same speed, same ease of use, same trust, same confidentiality.

Your in-house wiki is incomplete, out of date, and not being used. Use AppIntel AI for a quick, tried and tested in-house index of all your corporate data.

What if you had an AI search of all your corporate data?  How would that improve your business?  How would it improve your decisions?  How would it reduce your costs?

The word wiki shows up only once in all the industry submissions.  Last month one operator started an enhanced recovery scheme in a high H2S pool that has been on production since the 1950s.  They quoted a well-known wiki in their submission documents.

Replacing in-house wiki

The second most common question we get in AppIntel AI demos: Can you add another dataset to AppIntel AI?  I want it for my company records, designs, field work, etc.

Like you, other companies have approached us about creating an in-house version of AppIntel AI that turns your past project data into a searchable AI.

We can use AppIntel AI systems to create a personalized system with just your data for just your consumption. Your own AI contains all the advantages of AppIntel’s years of refinement and functionality. 

You can get the first results from your new AppIntel AI wiki within sixty days with project completion in under twelve months.

Better than in-house wikis

AppIntel AI beats in-house wikis. Wikis rely on employees taking time to write articles for the wiki. Posting wiki articles takes up precious time that could be used to further your business causes. Since no one vets wiki articles in-house wikis have varying currency, completeness, and quality.

AppIntel AI is trained to watch for new information and update itself continuously.

The same great technology that powers AppIntel AI can also power your in-house search. Same speed, same ease of use, same trust, same confidentiality.

Better than other LLMs

AppIntel AI is better than other LLMs for in-house use because AppIntel has been trained to recognize and retrieve numbers. ChatGPT and other LLMs haven’t figured out how to do this important function yet.

Currently AppIntel finds and tags numerical information such as permeabilities, UWI locations, and other quantitative information. AppIntel AI can also be trained to retrieve your special tagged data.

We know what it takes to keep an LLM updated daily and hourly automatically.  You don’t want a solution that updates only once every few years.

You don’t want to have to start all over again with important token lists like formation equivalents and other oil and gas terms.  Those are already well established, tested, and optimized in AppIntel.

AppIntel AI is streamlined to run quickly.  In-house solutions we have seen take several days for even the easiest answers to search queries.

We have been at this since 2009 – a decade before Sam Altman formed OpenAI.

Better than other electronic well file solutions

The answer for quick retrieval over the last 25 years has been to tag important information and put every digital document in an electronic database. Most of the boxes allow for tagged data: depths, pressures, etc.

AppIntel AI automatically accumulates your structured and non-structured information and immediately makes it available for quick search.  It is easily searchable for proximity to your field operations, just like AppIntel AI does for industry submissions.

Unlike AppIntel AI, adding information to structured databases takes a huge amount of organizational time for current documents.  The project of organizing past documents in the database is so daunting that it rarely gets completed.  Most companies default to searching unstructured documents on shared drives hoping people will store them in the right place. 

Forget about searching by UWI or by documents within a few miles of a UWI in an in-house database.  With AppIntel AI, searching by proximity to UWI is easy. Even in the next section to the north or south or even across meridians or correction lines.

When the cost gets high enough for in-house solutions, or the proponent of the project leaves, the company discontinues the expensive project and the system is slowly forgotten and falls out of date.

Faster, cheaper, more reliable than DIY projects

Recent surveys show 80% of in-house AI projects fail. Unfortunately, when they fail, everyone associated with the project gets fired. AI knowledge is a different skill set than oil and gas exploitation.

In-house solutions are not free.  Indeed, DIY software is always the most expensive answer.

Frustrated with lack of progress, some oil and gas companies are spinning off their AI experiments to IT companies for free. All the infatuation, effort and heartbreak were finally enough: these operators divorced themselves from their AI attempts.

Tags: Flood, AI in oil and gas

Granger Low  23 Mar 2026

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