What are the components of the AI tech stack?

Artificial Intelligence (AI) technology is advancing at a breakneck pace, fundamentally reshaping the modern technical stack. As it plays a crucial role in supporting AI-native products and solutions, businesses need to better understand the components of the AI tech stack. Companies like Cursor and Legora are great examples of how to implement this new infrastructure, showcasing the transformative potential of these technologies.

Legora, for instance, is a Swedish AI startup that's been making waves in the legal technology sector. Recently, they've been in talks to secure over $100 million in funding, which would raise their valuation to about $1.7 billion (source). Earlier in the year, they received $80 million to expand their footprint in the U.S. legal market (source). These investments underscore the rapid growth and increasing importance of AI in specialized sectors such as legal services.

Cursor, another player in the coding space, has been scaling its operations within the software industry. This company reached 200M ARR in 2 years by leveraging opensource technologies (vscode) and upgrading them with AI native features (source); this growth highlights the ongoing trend of integrating AI to enhance application experiences. Recently, Cursor's parent company Anysphere raised a massive $900 million funding round at a $9.9 billion valuation (source). Cursor's approach reflects a broader industry shift towards creating more unified and effective AI solutions.

The modern AI tech stack, comprising data handling, GPU infrastructure, user interfaces, and orchestration layers, is essential for developing robust AI applications. In the coming years, businesses will need to adapt to leverage these technologies effectively. Understanding the components of the AI tech stack is crucial for companies aiming to implement AI solutions that drive growth and competitive advantage.

Decoding the AI tech stack: Essential components

This AI tech stack is not just a random collection of technologies and isolated tools that you glue together, but a carefully orchestrated set of components that ensure seamless AI operations, getting rid of dependencies and making sure they are all available based on your infrastructure needs. Some components could be provided by external vendors, but once put in production, businesses might need to comply with several constraints in the long run. Companies need to leverage these components but have the choice to switch from proprietary to self-hosted solutions, easily avoiding vendor lock-in to preserve their soveregnity. All these components need to be carefully chosen in order to maximize the output that you get from the system.

From traditional to vector databases, bringing documents to AI

Traditional databases have long served as the backbone of data management systems, excelling at storing and retrieving structured data efficiently. However, when it comes to handling AI workloads, especially those involving text, images, or even video data, traditional databases often fall short. especially when the things you are looking for are explained in plain language and not in a structured database request. This is primarily due to their inability to effectively manage unstructured data and perform the complex, high-dimensional similarity searches needed for AI applications.

Vector databases have emerged as a solution to these limitations. Unlike traditional databases, vector databases are designed to handle high-dimensional data efficiently. They encode information into vectors, which are essentially numerical representations that capture the meaning of data; it could be about text semantics, patterns in an image, or other types of representation. This encoding allows for efficient similarity searches, a crucial capability for AI systems that need to process and understand large volumes of data.

The transition to vector databases is not just about storage; it's about transforming how data is accessed and utilized by AI systems. By converting text and other unstructured data into vectors, these databases enable AI models to perform tasks like natural language processing, recommendation systems, and image recognition with greater accuracy and speed. This capability significantly enhances the contextual awareness of AI systems, allowing them to provide more accurate insights and predictions by focusing on the relevant information for a particular task.

Document handling: Making data AI-ready

The success of AI deployment hinges entirely on transforming raw, unstructured data into formats that AI models can effectively process. This transformation isn't just a technical requirement—it's the foundation upon which all meaningful AI insights are built.

Today's advanced AI systems must handle an extraordinarily diverse range of document formats. Beyond basic text, modern systems process PDFs, spreadsheets (XLSX/CSV), audio files (MP3/WAV), documents (DOCX), presentations (PPTX), web content (HTML), and virtually every image format (PNG/JPEG/TIFF). This format flexibility is no longer optional—it's essential for comprehensive data processing across industries where information exists in countless forms.

The technological backbone supporting these capabilities combines several sophisticated components working in concert. Automated document processing pipelines extract structured information from unstructured sources. Optical Character Recognition (OCR) technology converts images of text into machine-readable data, while specialized parsers handle tabular data, audio transcription, and image analysis. Large Language Models (LLMs) then provide the contextual understanding needed to interpret the extracted information meaningfully.

However, simply extracting data isn't enough. The information must be unified and properly encoded to be truly valuable. This is where parsing becomes absolutely critical. The parsing process analyzes information sequences across all these diverse formats to extract useful, consistent representations. These representations are then encoded into vector embeddings—numerical representations that capture semantic meaning—and stored in vector databases for efficient retrieval.

Effective parsing must handle the multifaceted nature of real-world data. A single document might contain textual information, tabular data, images with embedded text, and structural elements—all providing different but complementary signals about the content. Modern AI systems need to capture these multiple perspectives to build a complete understanding.

This multi-signal approach is precisely why UBIK implemented our advanced multi-vector search technology. Rather than relying on a single representation of a document, we capture and index multiple signals from each piece of content. When you search, the system evaluates relevance across all these dimensions, providing dramatically more accurate and contextual results than traditional single-vector approaches.

The importance of making documents truly AI-ready is crucial. AI is becoming increasingly integrated into critical business workflows, and the ability to accurately parse, process, and represent diverse document types directly impacts everything from data extraction efficiency to the quality of insights generated. Organizations that master this fundamental component of the AI tech stack gain a significant competitive advantage—they can leverage their entire information ecosystem rather than just the small fraction that happens to be in easily digestible formats.

Harnessing computational power: GPUs and beyond

Once you have these first components to bring your data to the system, you need to have a way to power the different engines to power your application. This is the role of the GPU, which plays a critical role in training and executing AI models. GPUs, or Graphics Processing Units, are designed to handle parallel processing tasks, making them ideal for the heavy computational demands of AI, such as training deep neural networks. Unlike traditional CPUs, which are optimized for sequential processing tasks, GPUs can perform many operations simultaneously, significantly accelerating AI workloads and allowing for faster model training and inference.

The benefits of GPUs extend beyond just speed. They enable AI systems to manage large-scale data and complex models, providing the computational backbone required for cutting-edge AI applications. As AI models grow in complexity and size, the need for robust GPU infrastructure becomes even more pronounced, driving advancements in hardware that continue to push the boundaries of what is possible. Also, as the technology spread, the demand for computing increased, making it even harder to have access to the GPUs.

In addition to GPUs, many organizations are turning to external LLM providers like OpenAI, Anthropic for compute offloading. By using API keys, businesses can leverage the computational power of external AI providers without needing to invest in their own infrastructure. This approach offers several benefits, including cost savings and scalability. Companies can access powerful AI models and computing resources on demand, paying only for what they use, which can be more economical than maintaining their own GPU clusters.

However, there are also limitations to consider when offloading compute tasks to external vendors. Security is a primary concern, as sensitive data must be shared with third-party providers. Ensuring that data is protected and complies with privacy regulations is crucial. Additionally, while offloading can reduce initial infrastructure costs, the ongoing expense of API usage can add up, particularly for large-scale or frequent tasks.

Scalability is another factor to weigh. While external providers can offer significant computational resources, the reliance on third-party infrastructure means that any changes in service availability or pricing can impact operations. For some businesses, maintaining their own GPU infrastructure may provide greater control and predictability, allowing them to tailor their resources precisely to their needs.

GPUs are indispensable for the modern AI tech stack, providing the parallel processing power necessary for advanced AI applications. Whether leveraging internal GPU resources or offloading to external vendors, businesses must carefully consider the trade-offs in terms of cost, security, and scalability to determine the best approach for their AI initiatives.

Interface and consumption of AI

If you are building an AI native product, the interface layer is crucial for bridging the gap between complex AI capabilities and user interaction. A well-designed user interface (UI) is not merely a visual component but a vital part of how AI technology is adopted and utilized effectively by end-users.

The primary goal of an AI user interface is to simplify complex technologies, making them accessible for everyday use. By doing so, interfaces empower users to interact with AI-driven features effortlessly. This simplification is essential because it allows users to leverage AI's full potential without needing deep technical expertise. For business professionals and technical decision-makers, this means they can focus on extracting value from AI systems rather than getting bogged down in technical details.

Moreover, a thoughtfully designed interface plays a pivotal role in building trust. Users need to understand and trust the technology they are engaging with, especially when it involves AI-driven insights and decision-making. Transparent interfaces that clearly explain how AI systems make decisions help demystify AI and address user concerns about reliability and accuracy. This transparency is critical for the broader adoption and acceptance of AI solutions across industries.

Beyond trust, AI-powered user interfaces are transforming how digital experiences are delivered. These interfaces are increasingly personalized, adapting to individual user preferences and behaviors, which enhances the overall user experience. By tailoring interactions to the needs of each user, AI interfaces create more relevant and engaging experiences, thereby driving user satisfaction and loyalty.

Finally, a strong interface empowers users by providing intuitive access to powerful AI capabilities. This empowerment is crucial for enabling users to utilize AI to accomplish tasks more effectively and efficiently, ultimately leading to better decision-making and enhanced productivity.

The design of user interfaces and experiences remains a cornerstone for its successful implementation and adoption. By focusing on user experience, trust, personalization, and empowerment, businesses can ensure that AI technologies deliver maximum value to end-users. This is one of the reasons why we provide at UBIK agent the tools to customize the tools interface and your experience as deeply as possible.

The orchestration layer: Managing complexity

Now that you have all the raw systems to upgrade your product, you need a layer to glue them together and orchestrate the flows. This is the orchestration layer, the one responsible for managing model configurations, integrating tools, and coordinating different AI processes, which are crucial for maintaining the efficiency and reliability of AI applications.

This orchestration layer should allow users to perform several configurations of the technology; this is the part that allows verticalization of the technology by combining the different elements together. For example, the orchestration layer will manage the behaviour of the AI interactions by defining what should happen when a user inputs particular information, uploads a document, and also makes sure that during the process of generating the result, errors are handled properly. This orchestration layer will route the different requests to the set of technical elements composing the product. By bringing them to the same place, you avoid bottlenecks and issues that could happen in one of the elements of the system.

In essence, the orchestration layer handles tasks such as resource allocation, workflow management, and system monitoring. It ensures that resources are efficiently distributed among different AI models and applications, enabling them to run smoothly and respond to changing demands. By managing these processes, the orchestration layer allows AI systems to scale effectively and adapt to dynamic environments, thus enhancing their flexibility and adaptability.

Platforms like UBIK exemplify the power of a robust orchestration layer. UBIK enables customization and integration of AI agents with external services, providing users with the flexibility to tailor AI solutions to their specific needs. This capability is particularly important for business professionals and technical decision-makers who require AI systems that can easily integrate with existing workflows and technologies.

UBIK's Innovative Approach to AI Integration

UBIK delivers a complete AI tech stack solution that directly addresses the fragmentation challenges plaguing most AI implementations. Our modular, context-aware system unifies conversations, agents, tools, and documents into a cohesive ecosystem, enabling truly seamless workflows and integration.

We provide the entire technical stack in one consolidated platform, allowing you to scale your AI use cases from concept to production in minutes rather than months. Our approach is fundamentally different - we don't just offer pieces of the solution, we deliver the full value chain.

For data handling, our robust APIs and direct platform capabilities simplify document parsing and ingestion. Our intelligent organizational system lets you categorize information logically through directories and folders. Most importantly, every document processed in the platform becomes instantly searchable through our multi-signal search system, regardless of file format - PDFs, spreadsheets, audio, video, or images.

When it comes to computational power, UBIK remains deliberately agnostic across all components. This flexibility allows you to deploy services on demand for hosting your own models, embeddings, or LLMs, as well as other critical services like parsing and code execution. Need additional compute? Simply add it to your account as required, maintaining full control over your infrastructure choices.

For interface needs, we provide in our platform the chat interface where you can insert your own custom UI for the tools, but we also provide embeddable iframes and external integration capabilities that seamlessly upgrade your legacy products with AI capabilities. This approach ensures you maintain control over the entire value chain of your AI stack while delivering modern experiences to your users.

The agent builder sits at the heart of UBIK's orchestration layer, empowering users to create and customize AI agents as easily as building apps. Our visual interface for designing AI workflows enables non-technical users to develop sophisticated, scalable solutions. Configure logic, connect tools, and integrate with existing applications while customizing user interfaces for instant deployment. Our modular design ensures all tools can be composed and reused across projects, maximizing your development efficiency.

UBIK's comprehensive tools Library enhances these capabilities with ready-to-use components, including RAG search, web search, code interpreters, and information analysis tools. These components allow for targeted document searches, Python code execution, and multi-document information summarization, all while supporting external service integration and one-click deployment.

The entire system remains deeply context-sensitive, leveraging your documents, applications, and work environment to perform complex tasks directly within your existing workflows. With persistent state and memory, users can seamlessly resume tasks without losing context, while our extensive connectivity to external services ensures UBIK integrates naturally into your existing digital ecosystem.

In essence, UBIK is a truly complete, modular AI solution that puts you in control of your entire AI value chain, from data ingestion to user experience, all while eliminating the integration headaches that typically slow AI adoption.