We believe that machine learning problems should be classified not by the industry they serve, but by the type of business problem they solve. Many machine learning projects address one of the following high-level business problems:

• 1. Helping to find more customers (and/or lower customer acquisition costs)
• 2. Optimizing existing systems in terms of budget, performance, and scale
• 3. Automating to reduce team work hours, human errors, etc.
• 4. Keeping customers for longer (anti-churn)
• 5. Predicting and building systems and solutions for the future demanding market
• 6. Selling more to existing customers (upsell / recommendation engines)

Certainly, we customize our projects to suit specific industries such as healthcare, finance, manufacturing, media, retail, etc., but more importantly, we tailor our projects to meet the specific needs of your particular business and your specific data.

First off, let's start with a simple chat to see if we're a good match. We both want to work on projects that have a good chance of success, right? So, let's chat about your goals, your data, and what you've been up to. We'll only jump in if we can genuinely improve on what you're already doing.

Next, we usually kick things off with a short paid engagement, usually lasting 1-3 weeks. We'll take a look at a sample of your data and figure out what you want to learn from it and your desired business metrics. Then, we'll come up with a detailed plan for building a functional prototype and an initial production system. At this point, we'll both decide if we can still deliver meaningful results for you, given the likely investment to proceed.

Assuming we decide to move forward, our next step is to build a prototype, or a series of functional prototypes, to reduce risk and show you how the model and system perform, including APIs. We'll take as many reasonable shortcuts as we can (e.g., use static data) to efficiently demonstrate whether the system can perform.

If everything goes smoothly, we'll seamlessly transition the prototype into a production system for you. Once the system is live, we can keep optimizing and maintaining it for you, or we can hand it off to your team, whichever suits you best.

Let me know if you need further adjustments!

Not at all, Our smallest customer has only one employee: the CEO and owner. Our service is results- and scope-oriented. We can support anything within our competencies, from small-scale projects to large-scale AI transformations for corporations.

Our choice of tools and platforms is tailored to each customer's specific needs, so our team remains flexible. Generally, we prefer to utilize GCP, Azure, or AWS cloud services alongside Virtual Machines, incorporating various best practices.

Data Privacy: We guarantee that our AI solutions comply with data protection laws and uphold user privacy.

Bias Mitigation: We proactively deal with potential biases in AI models to prevent unfair treatment or discrimination.

Transparency: We ensure that AI systems are comprehensible and explainable to users and stakeholders.

Accountability: We set clear guidelines and responsibilities for AI decision-making processes.

The price and time it takes to set up an AI solution depend on a bunch of things like how complicated the project is, the size of your business, and how much customization you need. We'll work closely with you to figure out a realistic timeline and keep you in the loop every step of the way.