ulam.ai celebrates 3 years

What's next for ulam.ai
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The following text is a shortened version of a story of ulam.ai by Przemek Chojecki, our CEO.



In the past 3 years, while running ulam.ai without a plan other than ‘do AI consulting’ I came to understand what I like and dislike about it. I love helping others overcome difficult problems be it technical, business or both. I like hard intellectual challenges, especially if they result in tangible results. It attracted me to academia in the first place and I’m constantly searching for that in business as well. 


What I don’t like is being a subcontractor, building products for others, providing raw coding and engineering skills. In other words running a software house or a data science shop is not for me. 


My research background causes me to value free intellectual tinkering over codified approach to innovation. I’ve always been deeply influenced by Grothendieck’s metaphor of solving a problem. Trying to solve a hard problem is like opening a nut. You can try to crack the nut open using a hammer, hitting it over and over. It’s direct, brute force approach. Or you can try to immerse the nut in a liquid, think about a rising sea, and wait for the nut to crack open. This is indirect approach of building proper frameworks, patiently learning, tinkering with ideas until you find the right one. This second approach is dear to me and it is how I want to run the business. It’s also similar to how Paul Graham views finding startup ideas in his essay


Having said that there are two ways to take a company to the the next level: productised services and your own product (to elaborate on leveling up companies, read a great essay on wealth by Paul Graham, Ravi Naval tweet series, Nathan Barry’s blogpost; they all discuss the same topic with slightly different angles). You need a way to supply whatever you do at scale.


Building my own product is what I was trying to do all along with Bring and Bohr, but didn’t managed to carry it out. Neither of them complemented ulam.ai activities. It was basically managing two separate businesses at the same time, which doesn’t work well usually (and it didn’t for me, I had to side track ulam.ai). This time, with Contentyze being in synergy with ulam.ai, I am much better equipped, more experienced and with a good plan I believe. And last but not least, with revenue to reinvest. 


‘Productised services’ means you have a menu of services to choose from with a fixed pricing. Think about barbers, you don’t negotiate price each time you go to have your hair cut. There’s one price and you know what to expect.


This is exactly what I plan for ulam.ai in the next months and years to come. I want ulam.ai to be an AI consulting company with exactly two offers:


  1. individual AI consulting
  2. data science strategy


The first option should be clear, it’s a basic form of consultation, paid per hour for a fixed fee. 


Data science strategy offer is what is really unique about ulam.ai – it’s building a unique, in-depth strategy from technical as well as business perspective related to machine learning, data science or AI problems. Whether you’re building a data science team, implementing AI features in your company or applying for an R&D grant, you need a data science strategy and ulam.ai is here to help. I really like building strategies because each case is different, I need to understand it business wise and then think about technical problems as well. And then I have to put everything on paper, maybe manage the first steps of a client’s team, but don’t build the actual solution. 


In the past years individual consulting and building AI strategies were the most enjoyable projects and I’ve learned a lot from completing them. This is the direction I want to take ulam.ai in. I believe it’ll provide the right framework for stable, growing revenues, ready to be reinvested in more risky projects and unique knowledge.


Furthermore some materials related to artificial intelligence and business will be productised into books and courses. That’s already happened to some extent. In February 2020 I published Data Science Job, a book for people starting their careers in Data Science. I’m currently writing Artificial Intelligence Business, a book with an overview of AI market, machine learning trends and predictions. I’ve recorded two short courses on YouTube related to data science and I plan to create more content in this direction. Books and courses are a good way to create digital products out of services, by utilizing knowledge acquired through consulting. 


Putting it all together, I plan to focus my efforts right now on building a standardised offering for ulam.ai, by creating necessary templates (blueprints for AI innovation and implementation) and integrating tools for smoother work (automation, booking service, outreach, marketing). Having completed that I’ll start hiring more consultants to join me on board while growing the scale of operations. ulam.ai will eventually become a product company in a technology consulting market with a decentralized team of experts.

ai, artificial intelligence, machine learning,