You're here because AI is creating opportunities and threats which will fundamentally change your business.You need to become an AI company … and yet you’re hesitant to take the first step.
Doing nothing isn’t an option. You know that companies—and careers—are made in moments like these by those who take smart bets on big changes. You’re here because you want to be the leader who drives the change instead of the one who reacts to it.
But can we help you? Maybe. Let’s start by dispelling a few AI myths.
5 myths you’ve heard about becoming an AI company
Myth 1: There is a chronic talent shortage in AI.
There is a talent shortage for every technical skill, and AI is no different. There are fewer people with AI skills but also fewer good AI opportunities. Many smart people are learning about this technology and want to work on your team—you just need to identify your gaps and begin recruiting or developing the people to fill them.
You won’t need to compete with Apple or Google for rock-star researchers if you sell the job opportunity to the right people. We know because we’ve done it for people like you.
Myth 2: AI strategies are esoteric.
The work is complex but your plan will be relatively straightforward. You need to do a few things to create your AI strategy:
- Select an initial AI use case.
- Review your data.
- Identify infrastructure changes, talent gaps, and organizational changes.
- Build prototype models.
You can have a budget, business case, staffing plan, and timeline for your first AI project in about 2 months. We know because we’ve done it for people like you.
Myth 3: Data is your biggest problem.
Yes, machine learning models rely on data. But you can systematically improve your data assets deploying your initial models. Trying to “fix your data first” is almost always a bad idea. You don’t know what data you need and you’ll always have data problems anyway.
Instead of obsessing about data problems, identify your most valuable data assets and build your first AI project with them. We know it’s possible because we’ve done it for people like you.
Myth 4: AI takes a big initial investment.
Spending months on a cutting-edge research effort, buying an AI company, or building out massive infrastructure first is almost always a bad idea. We suggest starting with simple models … and a few data sources … with open source tools … running on AWS/Google/Azure … a small team … and a short timeline.
Make a bigger bet later after you get a quick win. We know because we’ve helped people like you begin scaling after a quick win.
Myth 5: The technology is your biggest challenge.
The business press is obsessed with AI research breakthroughs. Conferences, vendors, and LinkedIn articles are bombarding you with confusing technobabble. Building machine learning models—even with deep learning or cutting-edge research—isn’t your biggest challenge.
Your biggest AI challenge is execution
Your biggest AI challenge isn’t any one thing—it’s everything.
Good execution requires acting on the thousands of little details which pop up in complex engineering projects. Sometimes it is just good ol’ fashioned hard work, collaboration, and determination. Details like:
- Building your prototype models.
- Interpreting the results against your business needs.
- Putting together a presentation for you CEO.
- Getting your data pipeline built.
- Creating your feature store policies.
- Setting a deployment cycle between data science and data engineering.
… and so on.
How about bad execution?
Bad execution results from ignoring the details and investing time in the wrong activities:
- Building complex models when simple models will solve your problem.
- Creating a brittle data pipeline which is expensive to support.
- Doing machine learning without a process to measure business impact.
So… Why should you hire us?
Why are we uniquely qualified to help you execute more effectively?
We are operators. We’re not “consultants”. We ship software. We build prototypes. We attend standups. We work as part of your team.
We value execution over strategy—and perspiration over inspiration.
We have decades of AI experience. Prolego is only a few years old but our team has decades of experience. We built our first neural network in 1994. We were building machine learning data pipelines 15 years ago. We have the battle scars from driving innovation at the world’s largest organizations such as NASDAQ, the CIA, and Pfizer.
We will navigate your non-technical challenges. Making you successful requires much more than bringing our technical expertise. We need to align with you business goals, integrate with you team, be aware of political challenges and allow you to demonstrate progress.
We share the risk with you. Prolego is 100% funded by customers and we are small by choice. Because we have no outside investors we have to prove our value every day.
Every. Single. Day.
Making you “happy” isn’t good enough. We only win if you succeed. We only win if we’re accelerating your progress by years. Most importantly, we only win if we get your team to a point where you don’t need us anymore.
Why should you NOT hire us
Don’t hire us if you want a big team. We believe that success in AI requires a small team of senior, experienced, specialists. If you’re looking for an army to solve your infrastructure challenges we’re the wrong company.
Don’t hire us if you want to bet on a big, proprietary platform. We believe your should start small and stick with open-sourced tools and open infrastructure like Amazon AWS. Don’t hire us if you want to make a big bet on a proprietary platform.
Don’t hire us if time doesn’t matter. We approach our engagements with a sense of urgency. We will push ourselves and your team to make tradeoffs which allow us to execute faster. We believe real progress only happens when your ML models go into production—so we try to make that happen as fast as possible.
Do we have to be responsible with AI? Ensure your infrastructure will scale? Minimize disruption? Of course. But we do it with a sense of urgency.
It’s all About You
You started reading this page out of curiosity. You kept reading because you found yourself nodding and thinking, “yes”.
But you have questions, concerns, and a list of reasons why your situation is unique. Let’s talk more about you—just contact us and we’ll be happy to schedule a quick call and offer you some advice for taking your first steps towards becoming an AI company.Get In Touch