Build or buy? We'll find the right solution for you.
No apportioning of blame - deciding whether to develop your own AI solution or buy it in as a ready-made solution is no easy task. How should this fundamental question be answered when, at the same time, every effort is being made to position the company for the future and pursue visionary goals?
The focus is on innovation and growth - yet the make-or-buy question often leads to uncertainty, doubt and sleepless nights. Clear, reliable decision-making aids are required - a tailor-made solution that shows the way and guides investments in a targeted manner.
Our Methodology
Our structured consulting approach ensures your AI strategy aligns seamlessly with your business objectives.
We collaborate closely with your team to:
Understand your business
Your goals, challenges, and strategic direction.
Clarify your requirements
Defining precise AI needs and expected outcomes.
Assess your in-house capabilities
Evaluating existing technical resources, skill levels, and readiness.
Together, we systematically evaluate:
AI Vendors
Researching, shortlisting, and analyzing credible AI solution providers based on strategic alignment, technical capabilities, past performance, and ease of integration.
AI Agencies
Assessing specialized service providers who can support or augment your AI initiatives.
We provide an independent, unbiased review of the selected solutions by:
Compare options
Comparing the feasibility and cost-effectiveness of internal development versus external AI vendor solutions.
Validate technical aspects
Validating technical depth, scalability, and integration ease to ensure comprehensive and future-proof decision-making.
We guide you through critical decision-making by:
Define ROI expectations
Clearly defining ROI expectations, considering immediate value and long-term strategic implications.
Select the optimal path
Ensuring you select the optimal path—whether building in-house capabilities, partnering externally, or combining both—for sustained AI excellence.
We collaborate closely with your team to:
Understand your business
Your goals, challenges, and strategic direction.
Clarify your requirements
Defining precise AI needs and expected outcomes.
Assess your in-house capabilities
Evaluating existing technical resources, skill levels, and readiness.
Together, we systematically evaluate:
AI Vendors
Researching, shortlisting, and analyzing credible AI solution providers based on strategic alignment, technical capabilities, past performance, and ease of integration.
AI Agencies
Assessing specialized service providers who can support or augment your AI initiatives.
We provide an independent, unbiased review of the selected solutions by:
Compare options
Comparing the feasibility and cost-effectiveness of internal development versus external AI vendor solutions.
Validate technical aspects
Validating technical depth, scalability, and integration ease to ensure comprehensive and future-proof decision-making.
We guide you through critical decision-making by:
Define ROI expectations
Clearly defining ROI expectations, considering immediate value and long-term strategic implications.
Select the optimal path
Ensuring you select the optimal path—whether building in-house capabilities, partnering externally, or combining both—for sustained AI excellence.
We've seen numerous technologies, solutions, and vendors rise and fall. We've negotiated partnerships, shared insights, and successfully implemented artificial intelligence solutions. Leverage our extensive experience in artificial intelligence to confidently navigate your decisions and avoid costly mistakes.
We have years of experience across every kind of AI deployment. As early adopters, we've implemented AI across diverse environments—from secure, on-premise infrastructures to scalable, cloud-native solutions. Our deep expertise spans countless deployment scenarios, ensuring we guide you to the right setup for your needs.
We’ve been here since the very beginning of deep learning. Our journey began alongside breakthrough moments like AlexNet and Inception v2. With years of hands-on experience, we’ve grown alongside the evolution of convolutional neural networks and applied them in production before most even started experimenting.
We stay ahead so you can move forward with confidence. Our internal teams are deeply invested in GenAI—experimenting, building, and testing at the edge of what’s possible. We translate that experience into practical guidance, helping our partners make forward-looking, confident decisions in an ever-changing landscape.
We have years of experience across every kind of AI deployment. As early adopters, we've implemented AI across diverse environments—from secure, on-premise infrastructures to scalable, cloud-native solutions. Our deep expertise spans countless deployment scenarios, ensuring we guide you to the right setup for your needs.
AI Deployments – On-Premise & Cloud
Pioneers Since the Early CNN Era
Leading the Charge in Generative AI
Learn more about how our methology will help you get the best results for artificial intelligence projects.
Don't let AI vendor hype guide your investment decisions. Schedule a free consultation to discuss your specific needs and get expert advice on whether to build in-house or partner with vendors.
A wrong AI investment decision can lead to wasted resources and failed implementations. Let us help you make the right choice.
Resources for Your AI Journey
Explore our latest articles on AI vendor selection, build vs. buy considerations, and implementation strategies.
The European Union’s groundbreaking AI Act is ushering in a new era of accountability for businesses using artificial intelligence. One key provision is the requirement for AI competence – ensuring employees (and especially decision-makers) have sufficient AI knowledge and skills.
Explore the strategic implications of AI’s inherent limitations compared to human performance. This article provides executive insights on adapting AI strategies to maintain competitive advantage and drive innovation.
Fine-tuning refines AI models for accuracy and domain expertise, while RAG accesses real-time data for dynamic responses. In this article, we will help you understand the differences between the two terms.
Discover how traditional DevOps limits AI project speed and why transitioning to MLOps boosts development. Learn about data-driven iterations, continuous training, and effective model monitoring.
