Railtown AI: A Gem in the Canadian AI Market
As investors and capital market professionals navigate the turbulent junior Canadian markets, few sectors have been immune to the liquidity and low trading volumes affecting most companies. Raising capital has been difficult for many. Marketing has become ineffective in many ways. Investors are now focused more on preserving capital, rather than looking for actual wins. What a time to be alive.
One sector that has shown promise and given opportunity to investors this year has been Artificial Intelligence (AI). On the private side, there have been sizable fund raises for relatively new companies with capital being raised at billion dollar-plus valuations.
Toronto based Cohere, which is developing an AI model ecosystem for the enterprise, raised $270 million as part of its Series C round which valued the company around $2.2 billion.
Only OpenAI ($11.3 billion at a $29B valuation), Inflection AI ($1.3B at a $4B valuation), Anthropic ($450 million at a $4B valuation) and Adept ($415 million at a $1B valuation) have raised more .
All of these deals are private, leaving the average retail guy shit-out-of-luck for playing names like these. Only the big-boys got a taste.
But clearly, we are at the start of a major technological trend.
When delving into the burgeoning world of AI investments, it is important to understand the terminology. With ChatGPT bringing it to the forefront this year, many investors are scrambling to understand how large this opportunity is, and what exactly they are investing in.
So what is Artificial Intelligence?
Artificial Intelligence is defined as the practice of getting machines to perform human intelligence level tasks.
A subset of AI is Machine Learning (ML). ML allows for the development of computer systems to imitate human learning. This is done by using statistical methods to train algorithms to make classifications or predictions and uncover insights that are largely unachievable by humans. Still with me?
Historically, the advancement of AI has been based on significant milestones, followed by increased organizational interest in operationalizing AI to drive business outcomes.
The most recent of these developments has been the rise of Generative AI which was brought to the mainstream via the open availability of OpenAI’s ChatGPT chatbot.
What is Generative AI?
Generative AI sits within Machine Learning and is described as algorithms that can be used to create new content.
This content can come in the form of text, images, computing code, videos, and even music.
Getting more granular, Generative AI is able to find complex relationships within large data sets of training data, summarize what it learns, and then create new data in the form of the aforementioned content.
One of the most popular applications of Generative AI is OpenAI’s ChatGPT chatbot.
ChatGPT is able to take any user query, called a prompt, understand it and then return a well thought out answer, which appear as though a human generated it.
How does Generative AI Work?
The key technology underpinning Generative AI are foundational models.
Foundational models are neural networks that are trained with unsupervised learning on massive datasets, largely from public sources like the internet, but could be from private networks as well. I will explain this more below.
A common example and most popular type of foundational model or “modality” of a foundational model is a Large Language Model (LLMs) like OpenAI’s GPT 1 thru 4 and DALL-E 1&2, Google’s BERT. LLMs can be used for a variety of text-to-text use cases.
Large Language Models (Text-to-text)
One type of foundational model is the Large Language Model, or LLM. LLMs are fed massive amounts of data, which are “scraped” from the internet, or other sources at large.
This data is then fed into an algorithm using unsupervised learning.
LLMs are essentially predictive machines; using the data they are fed and leveraging statistical methods, they are able to predict the next word or strings of words in a sequence based on a prompt.
This process is known as “pre- training” an LLM model and gives the model the ability to recognize, summarize, translate, predict, and generate text-based content such as emails, blog posts, computer code and even more creative endeavors such as creating poems.
So why should investors care?
Simply put, Generative AI is, and will be transformational in many ways. Almost everyone will be able to benefit from the advancements in this area. Let me explain.
We are seeing that Generative AI adoption could outpace previous technology cycles, which bodes well for companies with offerings in development or in market.
The commercialization of Generative AI use cases provides a significant tailwind for enterprise software companies.
Generative AI can have a similar, and potentially an even greater impact on the economy as the internet, stemming from use cases which enhance worker productivity and pointing to a massive opportunity ahead.
The economic impact of Generative AI could be $2.6-$4.4tn or between 4-7% of the GDP of advanced economies (as per the IMF, the total GDP of advanced economies is currently $60.5tn). For comparison, according to a 2011 report by McKinsey, the economic impact of the Internet was found to be $1.7tn or approximately ~3.9% of the GDP of advanced economies (according to the IMF, the total GDP of advanced economies was $46.7tn in 2011). Simply put, Generative AI will change and affect everything, like how the internet did 25 years ago.
So what should investors do?
Well, unless you have deep contacts in Silicon Valley, you are just not going to have access to the bigger names with monster valuations. Luckily, here in Canada, we have been able to play the space with a few small, and large names.
When doing your due diligence on AI deals, I would ask these questions to decide if the company is a fugazi, or the real deal.
- Is this just an old tech-deal slapping “AI” onto the name?
- Or do they have actual tech they developed in-house?
- Do they have patents? Or have applied for them at the very least?
- Are they making revenues?
- Does their tech solve a real world problem?
- Are they doing business or partnered with a Fortune 500 company?
If you are lucky enough to find a company that checks most of these boxes, then you are probably in a good position, depending on your entry point.
One such company I would like to bring to your attention that meets this criteria is Railtown AI Technologies Inc. (CSE: RAIL) (Frankfurt: A3DE8E)
What is Railtown AI?:
What Railtown AI creates is Generative AI for Software Development.
Their Generative AI Copilot Assistants have been purposely built to bring the power of Artificial Intelligence to Software Developers to help improve productivity, drive down inefficiency, improve product quality, lower costs and accelerate engineering and team velocity.
Railtown has built not one, but three products that fix definable problems, and create efficiencies for any company that creates software, and that market is large and growing.
Currently, over 40% of a software developer’s time is spent researching, debugging, testing problems with code, and sometimes doing this repeatedly. These are redundant, repetitive, mundane costly daily tasks that ultimately cost companies billions of dollars of lost productivity.
It’s estimated that GDP lost from developer time spent on bad code is as high as $85 billion, with a 42% productivity loss dealing with bad code and technical debt. 17.3 average hours are spent on bad code and debugging, while technical debt of poor software costs as much as $1.3 trillion overall. These are huge problems that bring huge opportunities.
If companies only get about 60% of a developer’s time to build software that works, Railtown has the opportunity to change that 40% of loss into productivity. Today software runs everything, and the world depends on it to function. In the end, this AI will make a transformative improvement in software development and therefore result in better software, happier engineers and ultimately happier users.
Diving into Railtown AI’s Technology:
The first solution built was Railtown’s Root Cause Analysis Co-Pilot, which is designed to detect bugs within applications and then quickly pinpoint the root cause of the errors and the impact on the application.
Root Cause Analysis eliminates the need to comb through every line of code. The Root Cause Analysis Co-Pilot hooks into the developer’s machine, where it spots errors before they reach production and immediately determines the root cause and ticket where the problem originated. This saves time and drives up efficiency.
Release Notes Co-Pilot is their second solution which documents product updates and enhancements which takes an enormous amount of time for a team lead to produce, as they have to sift through dozens, if not hundreds, of completed work items to summarize and then write digestible release notes that can be consumed company wide. Release Notes continually analyzes all new software deployments, as well as any changes made to software which will trigger the co-pilot to automatically generate comprehensive and accurate release notes. The net result is saving hours of developer downtime.
The Root Cause Analysis Co-Pilot and the Release Notes Co-Pilot are now available to over 400,000 Microsoft partners on the Microsoft Azure Marketplace and at Railtown’s website.
The third solution may be Railtown’s most important yet. Now in beta stage of development, Ai Assistant is a co-pilot that can provide reason-based answers related to a wide range of topics including work items, deployment issues, build errors, bug fixes, performance issues and more. Developers can ask the co-pilot specific questions or describe the problem that they are facing, and the co-pilot will provide relevant and actionable information to help solve the problem. This is huge, and unlike anything else in the industry.
Investment Highlights:
- Seasoned technology leaders
- Seasoned capital markets experience
- 100% owned and developed IP
- Multiple provisional patents
- Multiple years of trained AI model
- Very large and expanding vertical market
- Large beta texting community
- No barrier to entry
- First mover advantage
- Certified Microsoft Partner
- Microsoft Co-sell Status
- Established enterprise partner
- Battle-tested solutions
- Commercially launched solutions
- High-margin SAAS solutions
Capital Structure
CSE listed (RAIL) / OTC application filed
- $12M Market cap
- 91,535,640 Issued and Outstanding
- 54,742,2203 Founders and Directors
- 2,951,113 Convertible Debenture
- 9,610,347 Warrants
- 3,275,000 Employee Options
- 107,372,000 Fully Diluted
- Cory Brandolini – Founder/CEO/Director
- Marwan Haddad – Founder/CTO/Director
- Elliot Holtham – Founder/AI Data Scientist
- Paul Wooodward – CFO/Director
- Anna Maria Parente – Director
- Rob Goehring – Director
- Dr. Eldad Haber – Advisor
- Kevin O’Neill – Advisor
- Rebecca Kerswell – Vice President, Marketing & Communications
- Ali Sinawi – Capital Markets Advisor
Conclusion:
Railtown has spent years developing their technology. With AI, the barrier to entry is not small. Typically it takes many years to develop this technology, giving companies like Railtown a huge advantage over other start-ups that are playing catch up.
With multiple commercially-ready products in hand, the company is poised to benefit from the monumental shift in how people interact with each other, and how business is done.
On the stock side, the company has traded close to 20 million shares since May, signaling strong market support and increasing confidence in the opportunity.
The capital markets team has done a great job bringing this story to our network, and we feel this will only continue with expected developments, future financings and a growing market for generative AI solutions.
If you would like to learn more about Railtown AI, send us a message and we will be happy to connect with you.
We will be sending out updates so please keep the company on your watchlist, add yourself to our email list, and we will be sure to keep you updated.
Happy Investing,
Bello Capital Partners & SmallCap Communications Inc.
Disclosures: The content of this post is for information only. Readers fully understand and agree that nothing contained herein, written by Bello Capital Partners [Bello] and SmallCap Communications (SmallCap) about Railtown AI, including but not limited to, commentary, opinions, views, assumptions, reported facts, calculations, etc. is not to be considered implicit or explicit investment advice. Nothing contained herein is a recommendation or solicitation to buy or sell any security. Bello and SmallCap are not responsible under any circumstances for investment actions taken by the reader. Bello and SmallCap have never been, and are not currently, a registered or licensed financial advisor or broker/dealer, investment advisor, stockbroker, trader, money manager, compliance or legal officer, and does not perform market making activities. The shares of Railtown AI are highly speculative, not suitable for all investors. Readers understand and agree that investments in small cap stocks can result in a 100% loss of invested funds. It is assumed and agreed upon by readers that they will consult with their own licensed or registered financial advisors before making any investment decisions. Readers understand and agree that they must conduct their own due diligence above and beyond reading this article. Bello and SmallCap are not responsible for any perceived, or actual, errors including, but not limited to, commentary, opinions, views, assumptions, reported facts & financial calculations, or for the completeness of this article or future content.
At the time this information was posted, Bello and SmallCap owned shares of Railtown Ai, and may buy or sell shares at any time.
