HOW WE WORK

Hire FAANG engineers who have extensive experience in building AI/ML solutions that iterates fast, understands customer expectations, and offer a working solution that is both practical and cost-effective.

CUSTOMER OBSESSION

Our clients are at the center of everything we do. We prioritize understanding their needs and goals to deliver personalized, high-quality services. We believe in overcommunicating to keep our team aligned and informed. This customer obsession helps us exceed expectations, fostering long-term partnerships and mutual success.

BIAS FOR SIMPLICITY

We believe in the power of simplicity. Instead of over-engineering or reinventing the wheel, we strive to find the most straightforward and effective solutions. This does not mean cutting corners. By focusing on simplicity, we create solutions that are easier to understand, implement, and maintain, saving our clients time and resources.

BREATH AND DEPTH

Our approach to technology combines broad knowledge with deep expertise. This balance allows us to identify and implement the best possible AI solutions for our clients' unique challenges. We leverage a mix of technologies to address needs comprehensively and effectively.

BUDGET IN MIND

We understand that every project has budget constraints. Our team is dedicated to delivering top-notch AI solutions that are both effective and economical. By keeping the budget in mind from the outset, we ensure clients receive maximum value without compromising quality or innovation.

Development Process

We believe in starting small and iterating fast. We understand the power of ML/deep learning, but it is not a silver bullet. The state of AI is constantly changing. What was not possible a few months ago may be possible now, but we need to also be careful to not to reach for what does not exist.

We first run through the end-to-end development process quickly, from research, development, to deployment, in order to identify the largest bottleneck and risks of the project. We then repeat and iterate this process, focusing one problem areas at a time. This ensures the highest success rate of any of our projects.

Here is generally the steps we take per iteration:

Identify business objectives, and translate that to ML objectives
Research on existing solutions, get a sense of the current state of the technology for the problem. Re-use if possible
Select a few potential AI models for the problem, and collect a small set of data for experimentation.
Train and test model performance
Deploy to production
Setup MLops. Monitor system performance metrics and ML model evaluations.
Verify if project meets our initial objectives
Repeat