The AI Stack for Emerging Startups

December 11, 2023

Paul Graham once famously said that startups are companies designed to grow fast. Historically, they achieved this growth through a combination of technologies, known as 'The Startup Stack.' This stack, built around basic yet scalable digital solutions, focused on key aspects like web development, customer relations, and sales and marketing automation.

Now, as we embark on the AI decade, this stack is poised for a complete transformation. Emerging AI technologies are forcing us to rethink our traditional approaches to sales, marketing, and development, marking a significant shift in the startup ecosystem.

Core Generative AI (GAI) technologies such as Llama2, Falcon, Whisper, Mistral, StarCoder and Stability are becoming essential elements of startup infrastructure. These advanced tools enable startups to tackle previously unattainable tasks due to complexity or cost constraints, such as sophisticated code automation, producing high-quality digital content, and extracting deep insights from extensive data sets.

I contend that this new 'AI-Powered Startup Stack' allows startups to circumvent traditional growth stages, enabling them to scale at unprecedented speeds. From our experience of working with sophisticated early stage startups, it is becoming increasingly evident that the AI-driven startup stack is more than a good-to-have; it's a vital component for startups born in the AI era, and will fundamentally reshape the nature of startup growth and innovation in the coming future.

In this article, I will outline the foundational elements of this new startup stack and discuss how startups can leverage them for rapid growth. Additionally, I'll explore how incubators can facilitate this adoption, helping their cohort of startups to scale faster and achieve greater scalability.

Let’s dive in.

The Generative AI Stack for Startups

In 2023, the generative AI landscape is booming, driven by the advancement of powerful open-source models like Llama2, Whisper, Stability, Mistral, and StarCoder. These models are democratizing AI access, allowing startups to leverage cutting-edge technology without exorbitant costs.

Here's a breakdown of some key open-source models and their potential applications for startups:

Falcon 40B: The Falcon 40B is a powerful open-source large language model (LLM) developed by the Technology Innovation Institute (TII). It offers a range of capabilities, including text generation, translation, question-answering, and summarization. 

Llama2: This powerful large language model (LLM) excels in text generation and can be used for diverse tasks like writing marketing copy, generating product descriptions, and even creating realistic dialogue for chatbots.

Mistral: This text-to-code translation model can convert natural language descriptions into executable code, significantly simplifying software development and automating repetitive coding tasks.

Stability: This open-source diffusion model is a game-changer for image generation, allowing startups to create realistic high-quality images from scratch, even without artistic expertise. It can be used to generate product mock-ups, design marketing materials, and create personalized artwork for customers.

Whisper: This state-of-the-art automatic speech recognition (ASR) model offers high accuracy and real-time processing, making it ideal for transcribing audio interviews, meetings, and online content. Transcripts can be further analyzed for insights or used to create text-based content.

StarCoder: This AI code generation platform utilizes a large code corpus to generate code snippets and algorithms, speeding up development processes and allowing programmers to focus on complex problems.

In the sections below, we will list out the models, and then explain the suitable ones for startups to get leverage from.

Falcon 40B and 180B: Powerful Open-Source Large Language Models

The Technology Innovation Institute (TII) has made significant contributions to the advancement of artificial intelligence with the development of Falcon 40B, a powerful open-source large language model (LLM). 

Trained on 40 billion parameters and one trillion tokens of text data, Falcon 40B offers a range of capabilities that are valuable for both research and commercial applications.

Furthermore, building upon the success of Falcon 40B, the Technology Innovation Institute (TII) next released Falcon 180B. This behemoth boasts a staggering 180 billion parameters, trained on an immense dataset of 3.5 trillion tokens. This significantly expands its capabilities and opens the doors to even more powerful applications.

Falcon models come with some key features:

  • High Accuracy and Performance: They demonstrate impressive accuracy in various tasks, including text generation, translation, question-answering, and summarization.
  • Open-Source Availability: Unlike many other LLMs, they are available under an open-source license, making them accessible to a wider community of developers and researchers.
  • Royalty-Free Usage: As of 31 May 2023, Falcon 40B is free of royalty for both research and commercial use, further encouraging its adoption and accelerating AI innovation. Falcon 180B has a similar license. 
  • Large Model Flexibility: Both Falcon 40B and Falcon 180B provide a flexible foundation for building custom applications and fine-tuning the model for specific tasks and domains.
  • Multilingual Capabilities: These models can process and generate text in multiple languages, making them suitable for global applications.

Llama2 for Versatile LLM Tasks

Among the open-source generative AI models available today, Llama2 is an extremely powerful LLM capable of a number of tasks. Developed by Meta AI, Llama2 offers a wide range of capabilities that empower developers and researchers to explore the potential of AI in various fields. 

Key Features

  • Sizeable Parameters: Llama2 comes in three variants - 7B, 13B, and 70B parameters. This allows users to choose the model size that best suits their needs and computational resources.
  • Open-Source Availability: Unlike many other LLMs, Llama2 is fully open-source and available under a permissive license. This makes it accessible to a broader community and encourages collaboration and innovation.
  • Diverse Capabilities: Llama2 excels in text generation, translation, question-answering, summarization, and other NLP tasks. This versatility makes it a valuable tool for a wide range of applications.
  • Safety-Focused Design: Meta AI prioritized safety during the development of Llama2, resulting in a model that generates text with significantly lower levels of toxicity compared to other LLMs.
  • Continuous Improvement: Meta AI actively maintains and updates Llama2, ensuring its performance and capabilities remain at the forefront of the field.

Mistral: Bridging the Gap between Natural Language and Code

Mistral is an innovative open-source language model specifically designed for translating natural language descriptions into executable code. This remarkable tool empowers individuals with limited coding experience to create software and automate tasks, democratizing access to the world of programming.

Key Features

  • Natural Language Processing: Mistral understands natural language prompts and instructions, allowing users to express their desired functionality without needing deep coding knowledge.
  • Code Generation: Mistral translates natural language descriptions into various programming languages, including Python, Java, and JavaScript, enabling users to build software applications.
  • Modular Design: Mistral operates on a modular architecture, allowing for customization and integration with other tools and workflows.
  • Open-Source Availability: Mistral is available under an open-source license, fostering collaboration and encouraging community contributions to the model's development.

Stable Diffusion for Image and Video Generation

Stable Diffusion has quickly captured the imagination of the AI community with its ability to generate stunningly realistic images and videos. This open-source model is democratizing AI, making it accessible to a broader audience and unlocking new avenues for creative expression and innovation.

Image Generation

Stable Diffusion excels at creating high-quality images from textual descriptions. Whether it's a breathtaking landscape scene, a whimsical creature, or a detailed architectural design, Stable Diffusion can bring your imagination to life.

The model offers several advantages over other image generation methods:

  • High Fidelity: Outputs are remarkably detailed and realistic, often indistinguishable from real photographs.
  • Versatility: Generates a diverse range of styles and artistic expressions based on user prompts and settings.
  • Controllability: Users can fine-tune the generated image through detailed prompts and adjustments to diffusion settings.
  • Accessibility: The open-source model is readily available for download and use, making it accessible to individuals and organizations of all sizes.

Video Generation

Stable Diffusion recently expanded its capabilities to include video generation, opening up a new world of possibilities. This feature allows users to create short video clips based on textual descriptions or reference images.

Early implementations have shown promising results, demonstrating:

  • Fluid Motion: Generated videos are smooth and realistic, capturing the natural flow of movement.
  • Temporal Coherence: Each frame seamlessly transitions into the next, creating a cohesive and believable video experience.
  • Customization: Users can specify the desired frame rate, length, and style of the generated video.
  • Creative Potential: This technology opens doors for new forms of storytelling, animation, and video game design.

Whisper for Audio Transcription with Open-Source AI

Whisper is a groundbreaking open-source automatic speech recognition (ASR) model developed by OpenAI. It utilizes a convolutional neural network architecture to transcribe spoken language into text with accuracy and efficiency. 

Whisper offers a range of benefits that are transforming the way we interact with audio content:

  • High Accuracy: Whisper achieves state-of-the-art performance, surpassing traditional ASR models in terms of accuracy and word error rate (WER). This ensures high-quality transcriptions even in challenging environments with background noise or diverse accents.
  • Real-Time Processing: Unlike many other ASR models, Whisper boasts real-time processing capabilities, enabling transcription of live conversations, interviews, and lectures with minimal latency. This makes it ideal for real-world applications like live captioning and video conferencing.
  • Multilingual Support: Whisper supports a wide range of languages, making it a versatile tool for transcribing content from various sources. This opens doors for global communication and accessibility, breaking down language barriers and facilitating cross-cultural understanding.
  • Open-Source Availability: As an open-source model, Whisper is freely available for download and use, empowering individuals and organizations to leverage its capabilities without licensing fees. This democratizes access to advanced ASR technology and fosters innovation within the AI community.
  • Customizability and Adaptability: Whisper offers a high degree of customization, allowing users to fine-tune the model for specific domains and tasks. This adaptability makes it suitable for various applications, from transcribing academic lectures to processing legal recordings.

StarCoder and Code-Llama: AI-Powered Allies for Developers

StarCoder and Code-Llama are two innovative open-source AI models that empower developers by automating repetitive coding tasks and generating human-quality code snippets. Both tools hold immense potential for enhancing developer productivity and accelerating software development.


  • Focus on Code Generation: Trained on a massive dataset of code, StarCoder generates code snippets based on natural language descriptions or existing code fragments.
  • Code Completion and Suggestion: StarCoder assists developers by autocompleting code and suggesting relevant functionalities, reducing the time spent on tedious typing and syntax errors.
  • Code Optimization and Refactoring: StarCoder can analyze and optimize existing code, identifying inefficiencies and suggesting improvements for better performance and maintainability.
  • Multi-Lingual Support: StarCoder supports a variety of programming languages, making it a versatile tool for full-stack developers and projects with diverse codebases.


  • Chat-Based Code Development: Code-Llama interacts with developers like a conversational AI, discussing code concepts and generating code based on natural language prompts and instructions.
  • Code Fix-up and Debugging: Code-Llama analyzes code snippets and identifies errors, suggesting fixes and improvements to resolve bugs and enhance code quality.
  • Unit Test Generation: Code-Llama automatically generates unit tests for newly written code, ensuring code functionality and facilitating testing and debugging processes.
  • Recipe Generation: Code-Llama generates various code recipes, including documentation templates and boilerplate code, saving developers time and effort.

Choosing between StarCoder and Code-Llama

StarCoder: Ideally suited for developers seeking a tool primarily for code generation and completion, especially for simple tasks like repetitive function writing.

Code-Llama: Better suited for developers seeking a chat-based AI assistant for in-depth code discussions, debugging support, and automated recipe generation.

Specialized Domain-Specific AI Models

While general-purpose LLMs excel at a wide range of tasks, specialized domain-specific models offer unique advantages for applications in specific industries. If you are building a startup in a specific domain, you might be able to get additional leverage from some of these models. 

Here are several open-source domain-specific LLMs currently making waves:


  • BioBERT: Trained on a massive corpus of biomedical literature, BioBERT excels at tasks like clinical text analysis, drug discovery, and medical question-answering.
  • Med2Vec: Leverages word embedding techniques to analyze medical text and extract relevant information for clinical decision support and personalized medicine.
  • clinBERT: Specifically designed for clinical text analysis, clinBERT excels at tasks like identifying medical entities, relationships, and events in clinical reports and electronic health records.


  • ChatLAW: Trained on legal documents and case studies, ChatLAW assists legal professionals with tasks like contract review, legal research, and document drafting.
  • CaseLawBERT: Analyzes legal documents and identifies relevant case law, enabling lawyers to research precedents and build stronger arguments.
  • Legal-BERT: Focuses on understanding legal language and reasoning, offering potential applications in legal chatbots and automated document review.


  • DeBERTa for Healthcare: Specifically trained on healthcare datasets, DeBERTa for Healthcare excels at tasks like medical information extraction, clinical trial analysis, and patient profiling.
  • CliNER: Identifies and classifies clinical entities in text, facilitating data analysis and clinical decision support.
  • MedMentions: Extracts clinically relevant information from discharge summaries and other medical documents, supporting healthcare research and patient care.


  • EduBERT: Trained on educational materials, EduBERT understands educational language and can be used for tasks like question-answering, student assessment, and personalized learning.
  • Albert-Base: Analyzes educational content and can be used for tasks like generating personalized study guides and recommending relevant learning resources.
  • XLNet-Base: Offers a comprehensive understanding of educational language and can be used for tasks like creating engaging learning materials and providing personalized feedback to students.

Choosing the Right Generative AI Technologies

In early stage startups, Generative AI can provide tremendous leverage to the key functions of Sales, Marketing and Development. Here's how:


  • Lead Generation: StarCoder and Code-Llama generate code for automated lead generation tools, including email campaigns and chatbots, thereby reducing manual effort and increasing lead capture.
  • Personalized Sales Pitches: Llama2 analyzes customer data and generates personalized sales pitches, enhancing customer engagement and conversion rates.
  • Real-Time Sales Insights: Whisper transcribes sales calls, enabling AI-powered analysis to identify key customer insights and improve sales strategies.
  • Automated Sales Reporting: StarCoder generates sales reports from data analysis, saving time and improving reporting accuracy and efficiency.


  • Targeted Content Generation: Llama2 and Falcon create targeted marketing content, including website copy, blog posts, and social media posts, personalized for specific audience segments.          
  • Automated Content Distribution: StarCoder automates social media content scheduling and distribution, ensuring consistent brand messaging across platforms.
  • Campaign Optimization & Analysis: Whisper analyzes user feedback in audio and video formats, providing valuable insights for optimizing marketing campaigns.
  • Personalized Marketing Experiences: Mistral generates personalized product recommendations and offers based on customer data, enhancing customer engagement and satisfaction.


  • Code Generation & Automation: StarCoder and Code-Llama automate repetitive coding tasks, such as writing boilerplate code and generating unit tests, freeing up developers for more complex tasks.
  • API Documentation & Generation: Mistral generates API documentation from natural language descriptions, improving developer communication and understanding.
  • Bug Detection & Code Optimization: StarCoder analyzes code for potential bugs and suggests improvements, enhancing code quality and performance.
  • Data Pipeline Automation: Whisper transcribes audio data into text, enabling automated data pipelines that process audio content for further analysis and development tasks.

How to Self-Host Your Cloud Technology Stack with AI Inference Capabilities

The future of the cloud stack for startups lies in a hybrid approach, effectively leveraging both CPU and GPU compute resources. This combination unlocks unparalleled performance, scalability, and cost-efficiency, helping startups to thrive in the age of AI.

Components of a Hybrid Cloud Stack

  • Centralized Cloud Platform: A robust cloud platform like E2E Cloud provides the foundation for the cloud stack, offering essential services like storage, networking, and security.
  • CPU-Based Instances: Handles general-purpose tasks like web applications, data processing, and basic AI inference.
  • GPU-Based Instances: Dedicated for computationally intensive AI tasks like model training and advanced inference, offering faster processing speeds and higher throughput, and using GPUs such as HGX 8xH100, A100 and more.
  • Containerization and Orchestration: Docker and Kubernetes facilitate containerization of applications, allowing efficient deployment and scaling across both CPU and GPU environments.
  • AI Frameworks and Libraries: TensorFlow, PyTorch, Keras and other AI frameworks provide the necessary tools and libraries for building and deploying AI models on the hybrid cloud stack.

Why Hybrid?

  • Matching Workload Needs: Different tasks require different resources. CPUs excel at general-purpose tasks like web serving and database management, while GPUs shine in computationally intensive tasks like AI training and inference.
  • Optimizing Costs: Utilizing CPUs for everyday tasks and GPUs for specific AI workloads optimizes resource allocation, minimizing costs while ensuring peak performance where it matters most.
  • Future-Proofing Infrastructure: By embracing a hybrid architecture, startups prepare for the ever-increasing role of AI in their operations, ensuring their infrastructure can adapt to evolving needs.

Guiding Startups through the Generative AI Landscape: The Role of Incubators and Accelerators

Incubators and accelerators play a crucial role in supporting startups by providing resources, mentorship, and access to networks. As Generative AI technologies rapidly evolve, these organizations have a unique opportunity to guide startups in leveraging them effectively.

Key ways in which I believe incubators and accelerators can play a role are:

  • Technology Selection: Incubators and accelerators can help startups identify the most appropriate generative AI models and tools based on their specific needs and goals. This includes assessing technical capabilities, resource requirements, and compatibility with existing workflows.
  • Integration and Implementation: Providing guidance on integrating generative AI tools seamlessly into existing processes and workflows. This involves identifying integration points, automating tasks, and establishing data pipelines for optimal performance.
  • Training and Development: Offering training workshops and resources to help startups build their internal expertise in using generative AI models. This includes training on model functionalities, best practices, and troubleshooting techniques.
  • Community Building: Fostering a vibrant community of startups and experts working with generative AI. This facilitates knowledge sharing, collaboration, and the cross-pollination of ideas.
  • Access to Resources: Connecting startups with relevant experts, service providers, and funding opportunities related to generative AI implementation.

By helping startups adopt AI early, incubators and accelerators can get tremendous value in the long run:

  • Increased Startup Success Rate: By equipping startups with the knowledge and tools needed to leverage generative AI effectively, incubators and accelerators can contribute significantly to their success.
  • Attracting and Retaining Top Talent: Demonstrating expertise in generative AI can attract talented individuals who are passionate about this technology, strengthening the overall startup ecosystem.
  • Innovation and Competitive Advantage: Supporting the development of AI-powered startups can lead to groundbreaking innovations and contribute to a more competitive industry landscape.
  • Enhanced Reputation and Visibility: By becoming a leader in fostering AI-powered startups, incubators and accelerators can gain recognition and attract high-potential startups.

Here are some examples of how incubators and accelerators can extend support to their cohort of startups:

  • Providing access to cloud computing resources and specialized hardware for training and running generative AI models. Read on to learn how you can work with us at E2E Networks for this. 
  • Organizing hackathons and challenges that encourage startups to explore and develop solutions using generative AI.
  • Partnering with AI research institutions and labs to facilitate knowledge exchange and access to cutting-edge technologies.
  • Hosting workshops and seminars featuring experts in generative AI to share best practices and insights.
  • Establishing dedicated mentorship programs that connect startups with experienced professionals in the field.

By actively supporting startups to navigate the generative AI landscape, incubators and accelerators can play a pivotal role in driving AI innovation and shaping the future of technology-driven businesses.

E2E Networks’ Role in Enabling Startups and Incubators

At E2E Networks, we are passionate about growing the AI ecosystem in India. We understand that access to cutting-edge technology, knowledge, and support is crucial for aspiring entrepreneurs and innovators. 

Empowering Startups and Incubators

  • Startup Credits: We offer generous credits specifically for startups and incubators, significantly reducing their financial burden. This allows them to experiment with our powerful AI infrastructure and develop innovative solutions without limitations.
  • Hackathons and Workshops: We organize engaging hackathons and workshops around generative AI, catering to emerging startups. These events provide a platform for collaboration, knowledge exchange, and building groundbreaking projects.
  • Mentorship Programs: We connect promising startups with experienced AI professionals who offer invaluable guidance and support throughout their journey. This helps navigate the complexities of developing and deploying AI solutions, ultimately leading to higher success rates.

Beyond Startup Support

  • AI Infrastructure Development: We have invested heavily in building a robust AI-first cloud platform. This platform provides access to high-performance GPUs, advanced networking technologies, and intuitive tools, empowering developers to efficiently train, optimize, and deploy AI models.
  • Democratizing AI Knowledge: We are committed to making AI knowledge readily available to all. We actively engage in knowledge sharing through blog posts, research paper explainers, tutorials, and code walkthroughs. This empowers developers of all skill levels to understand and utilize AI technologies effectively.
  • Building a Vibrant Community: We are pushing for a thriving IRL community of AI enthusiasts and professionals. This community serves as a platform for collaboration, knowledge exchange, and networking. This cross-pollination of ideas accelerates the pace of innovation and creates an environment where everyone can learn and grow together.

If you are a startup looking for guidance on integrating AI into your stack, or an incubator or accelerator looking to discuss possible collaboration opportunities, do feel free to reach out directly by writing in to Let’s build India’s AI story together. 

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  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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