The Autonomous Business AI, designed by CRTV Digital, is a groundbreaking solution tailored to assist startups and entrepreneurs in navigating the complex landscape of business formation and growth. Leveraging cutting-edge artificial intelligence technology, this system offers a comprehensive suite of services that include:

3 min read


[A] - Autonomous

[T] - Training

[L] - Layered

[A] - Agent

[S] - Systems

Business Model

Graph for the Business Model

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Business Model Tasks

  • - Business Model Generation

Business Model Data

Business Model Data

Business Model Notes

Creating a business model for an AI agent involves considering the unique capabilities and needs of AI technology, as well as addressing market demands and potential competitors. Below is a simplified business model for an AI-based personal productivity assistant named “ProdBot.”

  • Value Proposition - “ProdBot helps professionals and students enhance their productivity by smartly organizing their tasks, suggesting optimal times for breaks and work, and integrating seamlessly with other digital tools.”
  • Customer Segments -
    • Professionals with busy schedules.
    • Students looking for study aids.
    • Small businesses seeking organizational tools.
  • Channels -
    • Direct online sales through the ProdBot website.
    • Partnerships with productivity-related websites and apps.
    • Affiliate marketing through influencers focused on productivity.
  • Customer Relationships -
    • Automated customer service and FAQs.
    • Human-augmented support for complex issues.
    • Community forums for users to share tips and tricks.
  • Revenue Streams -
    • Monthly/Annual subscriptions for premium features.
    • Freemium model where basic functionalities are free, but advanced features come with a cost.
    • Licensing the AI tech to other productivity tool companies.
  • Key Resources -
    • Proprietary AI algorithms.
    • Cloud infrastructure for data storage and processing.
    • Product development and customer support teams.
  • Key Activities -
    • Continuous improvement of the AI model based on user data.
    • Marketing and partnership outreach.
    • User experience (UX) optimization.
  • Key Partnerships -
    • Productivity bloggers and vloggers for promotions.
    • Tech companies for integration opportunities (e.g., calendar apps, task managers, etc.)
    • Cloud service providers for infrastructure needs.
  • Cost Structure -
    • Cloud hosting and data storage costs.
    • R&D for AI model improvements.
    • Marketing and sales expenses.
    • Personnel salaries and benefits.
  • Unique Selling Points (USPs) -
    • Personalized productivity insights based on individual user behavior.
    • Seamless integration with other popular digital tools.
    • Constant learning and improvement of suggestions based on user feedback.

Potential Challenges

  • Data Privacy Concerns :

    • Ensuring user data is stored and processed securely is paramount. ProdBot should be GDPR compliant and transparent about data usage policies.
  • Competition :

    • The productivity tool market is crowded. ProdBot must consistently innovate and provide unique value to users.
  • Scalability :

    • As the user base grows, so does the need for processing power and storage. An efficient, scalable infrastructure is essential.
Next Steps:
    1. Prototype Development
    1. Beta Testing with a small group of users
    1. Iterative improvement based on feedback
    1. Full launch and marketing push

This business model is a foundation. As with all ventures, continuous adaptation to real-world feedback and changing market conditions is key to long-term success.

Data Structure Model

Data Structure Description Here

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Data Structure

Data Structure Description Here

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Integration with Existing Tools

Understanding the diverse needs of startups, the AI offers seamless integration with existing business tools and platforms. This ensures a cohesive and user-friendly experience.

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Integration Break Down

Understanding the diverse needs of startups

This part emphasizes that startups have a variety of needs. Startups come in different shapes, sizes, industries, and have various business models. Their requirements might range from marketing tools to inventory management or even collaboration software. Recognizing these diverse needs means that the solution being discussed (in this case, the AI) is designed to cater to a wide range of different startup necessities.

The AI offers seamless integration with existing business tools and platforms

This suggests that the AI in question isn’t trying to replace every tool a startup already uses. Instead, it’s designed to easily integrate or work alongside these existing tools. This could mean that it can pull data from other software, enhance the capabilities of current tools, or work in tandem with them without causing disruptions.

This ensures a cohesive and user-friendly experience

The primary goal of the AI’s integration capabilities is to create a unified experience for the user. By integrating seamlessly with existing tools, users don’t have to switch back and forth between multiple platforms. It’s all interconnected, creating a smoother workflow. When tools work together without hitches, it usually leads to a more straightforward, intuitive user experience.

Integration Tool Summary

This AI system understands that every startup is unique and has its own specific needs. Instead of making startups change their current tools, the AI can easily connect and work with those tools. As a result, users get a smooth and easy-to-use experience.

LLC Formation Model

Guiding users through the legal maze of creating a Limited Liability Company (LLC), the AI provides step-by-step assistance in filing the necessary documents, understanding state-specific regulations, and ensuring compliance with all legal requirements.

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LLC Formation Model Notes

  • In the modern digital era, the process of forming a Limited Liability Company (LLC) can be streamlined using machine learning (ML).

  • An ML-based software solution for LLC formation can:

    • Data Collection :
      • Gather required information from users through smart forms that predict and pre-fill fields based on user input patterns and regional norms.
    • Document Review and Preparation :
      • Automate the creation of articles of organization and other necessary documents, ensuring accuracy and adherence to regional requirements using trained models.
    • Regulatory Compliance :
      • Utilize predictive models to stay updated with evolving state-specific regulations and requirements, offering users guidance on ensuring their LLC remains compliant.
    • Recommendations :
      • Based on historical data and user profiles, recommend optimal LLC structures, banking services, or insurance products tailored to the specific needs of the business.
    • Monitoring :
      • Continuously monitor the business environment and provide alerts to LLC owners about relevant changes in legislation or other pertinent factors.
    • Feedback Loop :
      • Incorporate user feedback and data from successful LLC formations to refine and enhance the predictive accuracy and efficiency of the system.

In essence, leveraging machine learning in the domain of LLC formation can expedite the setup process, reduce errors, ensure compliance, and offer tailored recommendations, making the overall experience more efficient and user-friendly for budding entrepreneurs.

  • Document Metadata : Each document can have metadata attributes such as:

    • Document type : (e.g., contract, will, deposition, etc.)
    • Date created
    • Last modified date
    • Lawyer/Paralegal assigned
    • Client information
    • Case number/reference
    • Status (draft, finalized, etc.)
  • File Storage :

    • Appwrite offers storage functionality where you can store, retrieve, and manage the actual document files (like PDFs, DOCX, etc.)
  • Document Versioning :

    • Given that legal documents can be revised multiple times, you can maintain versions of each document.
    • Appwrite’s storage can be used for this purpose.
    • You could use a naming convention or metadata to distinguish between different versions.
  • Notifications and Communication :

    • Use Appwrite’s functions (which can act like serverless functions) to integrate with third-party notification services.
    • This could be used to notify a lawyer when a document has been edited or reviewed by a client, etc.
  • Search Functionality :

    • Given the vast number of documents that a law firm might manage, it’s crucial to have an efficient search mechanism.
    • While Appwrite doesn’t provide an out-of-the-box search engine, you can use its database querying capabilities and potentially integrate with other search solutions for more advanced searching and indexing.
  • Security and Compliance :

    • Encryption: Ensure documents are encrypted at rest and in transit.
    • Regular Backups: Establish regular backup mechanisms, especially given the sensitivity and importance of legal documents.
    • Audit Trails: Maintain logs of who accessed or modified documents, especially for compliance and security reasons.
  • APIs and Integration:

    • Appwrite provides APIs to manage collections, which means you can integrate the document system with other tools the law firm might be using, like case management systems or billing platforms.
  • UI/UX :

    • While Appwrite provides the backend capabilities, you’d need to build a frontend tailored to the needs of the law firm’s staff and potentially its clients.
  • Access from Multiple Devices :

    • Since lawyers and paralegals might need access from different devices, ensure that the frontend is responsive and can work on mobile devices and tablets.

Remember that while Appwrite provides a lot of capabilities, the specific requirements of a law firm and the intricacies of managing legal documents might necessitate custom solutions and integrations. Always prioritize security, privacy, and compliance in every architectural and design decision.

Terms of Service Document

Building a terms of service (ToS) documentation generator using Appwrite would involve combining your legal knowledge (or that of your colleagues) with the technical tools provided by Appwrite.

Appwrite provides a set of backend services that you can utilize to store, manage, and retrieve data for your application. In the case of a ToS documentation generator, you can use Appwrite to store templates of legal documents and then populate those templates with data provided by users to create custom ToS documents.

ToS Outline
  1. Setup Appwrite:

    • Install Appwrite on your server if it’s not already set up.
    • Follow the official documentation for the installation guide located here.
  2. Create a Project in Appwrite:

    • Once installed, you’d create a new project within Appwrite.
    • This project would be dedicated to your ToS generator application.
  3. ToS Template Storage:

    • Design a collection in Appwrite for storing various legal templates.
    • This collection can have fields like templateName, content, placeholders, etc.
  4. API for Template Management:

    • You’d probably want to have CRUD (Create, Read, Update, Delete) operations available for these templates.
    • Use Appwrite’s built-in features to provide these endpoints.
  5. User Interface:

    • Create a user-friendly interface where the user can:
    • Select the type of ToS they need.
    • Provide any required information to fill into the template (e.g., company name, jurisdiction, etc.).
    • Generate the ToS based on their selections and inputs.
    • This could be a web-based application or a desktop application, depending on your firm’s needs.
  6. Generating the ToS:

    • When the user provides all the necessary information and selects a ToS template:
    • Retrieve the chosen template from Appwrite.
    • Replace all the placeholders in the template with the provided data.
    • Return the generated document to the user, either for download or via email.
  7. Additional Features:

    • Document History :
      • Using Appwrite’s storage, you can also keep a versioned history of generated documents if needed.
    • User Accounts :
      • Using Appwrite’s built-in user management, you can let users create accounts, allowing them to save their information for faster document generation in the future or to track their past generated documents.
    • Notifications :
      • Appwrite provides functionalities for sending out email notifications.
      • You could use this feature to notify users about updates to templates or to deliver their generated documents.
  8. Security and Privacy:

    • Given that you’re dealing with legal documents, ensuring security and privacy should be a top concern. Make sure data at rest and in transit is encrypted.
    • You might also want to consider allowing users to delete their generated ToS if they are stored, to meet various data privacy regulations.
  9. Regular Template Updates:

    • Legal requirements change over time.
    • Ensure that there’s a procedure in place to regularly review and update the ToS templates.
  10. Testing & Deployment

    • Before deploying, ensure that you rigorously test the application for both functionality and security vulnerabilities.
    • Once everything is ready and thoroughly tested, deploy your application to a production environment.

This is a high-level overview, and the actual implementation might require a deeper understanding of your firm’s specific needs and the intricacies of legal document generation. However, this should give you a good starting point on how to approach this problem using Appwrite.

Privacy Document

We would follow the same layout and concept for the ToS but have it shift towards focusing on privacy.

Disclaimer Document

Similar to the ToS and Privacy documentation, we would just shift the data over towards disclaimer.

EULA Document

The EULA would be an extension of various softwares that they would be using, a bit of a collection of multiple different EULAs as well. Thus also similar to the ToS, Privacy and Disclaimer.

Operational Document

The scope for the operational document would have to be different because if its a Single LLC verse a multi-person LLC.

Reference for Operational Documents

By-Law Document

Generating bylaws for an LLC using machine learning is a complex task, as bylaws are legal documents with specific clauses and provisions that require careful construction to ensure they are valid and enforceable. Nonetheless, a high-level approach to this problem would involve the following steps:

  1. Data Collection:

    • Gather as many LLC bylaws as possible. The more diverse and comprehensive your dataset, the better.
    • Note: Ensure you have the legal rights to access and use this data.
    • Process and clean the data to remove any personal or confidential information.
  2. Data Preprocessing:

    • Tokenize the bylaws.
    • Breaking down the bylaws into sentences or smaller chunks can make the training process smoother.
    • Ensure consistent formatting and structure across all documents. This might involve removing headers/footers, page numbers, etc.
  3. Choose a Model Architecture:

    • For text generation tasks, models like RNNs, LSTMs, and Transformers (like GPT-2 or GPT-3) have proven to be successful.
    • If using a transformer-based model, you might consider fine-tuning an existing pre-trained model on your bylaws dataset.
  4. Training:

    • Feed your processed data into the chosen model.
    • Depending on the model and dataset size, this step might require significant computational power.
    • Monitor metrics like loss and, if possible, some form of qualitative evaluation to ensure the generated bylaws make sense.
  5. Evaluation:

    • After training, you need to evaluate the generated bylaws.
    • Ideally, involve legal professionals in this step.
    • They can assess the quality, consistency, and legality of the generated documents.
    • Use feedback to refine and retrain the model as necessary.
  6. Deployment:

    • Once you’re satisfied with the model’s performance, develop an interface or application where users can input specific parameters or requirements for their LLC, and the model generates appropriate bylaws.
  7. Post-deployment Monitoring:

    • Regularly check the generated bylaws to ensure they remain consistent with legal standards, especially if laws change over time.
    • It’s advisable to always have a legal professional review any machine-generated bylaws before they are finalized to avoid potential legal issues.
  8. Limitations & Ethical Considerations:

    • A machine learning model is only as good as the data it’s trained on. Ensure you are not perpetuating any biases or problematic practices from your dataset.
    • Always be transparent with users about the origin and potential limitations of the generated bylaws. Include disclaimers recommending professional legal review before use.
    • Consider potential privacy implications when gathering and using datasets.

Non-Disclosure Agreement Document

This would be similar to how we form the By-Laws, there would be a bulk amount of data for the NDA templates, then generates the NDA based upon user input.

Market Research Model

Utilizing advanced data analytics, the AI conducts thorough market research to identify target audiences, analyze competitors, and uncover industry trends. This empowers startups to make data-driven decisions and strategically position themselves in the market.

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Market Research Model Tasks

  • - Market Research Model Generation
  • - Relationship of Market Research Model and User Model
  • - Relationship of Market Research Model and Business Model
  • - Collapse Tags and Anti Tags into Data Blog? Or keep Isolated?

Market Research Model Data

Market Research Model Data

We will not be using suapbase, instead we will be using appwrite.

create table
  market_research_model (
    id bigint generated by default as identity primary key,
    inserted_at timestamp with time zone default timezone ('utc'::text, now()) not null,
    updated_at timestamp with time zone default timezone ('utc'::text, now()) not null,
    tags jsonb,
    anti_tags jsonb,
    name text,
    auth_id uuid,
    constraint market_research_model_auth_id_fkey foreign key (auth_id) references auth.users (id)

alter table market_research_model
  enable row level security;

create policy "Users can select their own market research model." on market_research_model
  for select using (auth.uid() = auth_id);

create policy "Users can insert their own market research model." on market_research_model
  for insert with check (auth.uid() = auth_id);

create policy "Users can update their own market research model." on market_research_model
  for update using (auth.uid() = auth_id);

Market Research Model Notes

Place here @grat

Market Primary Research

Here would be GPT4 based notes. : query the internet Hoover’s Company Profiles – information about more than 40,000 global public and non-public companies including location, financials, competitors, officers, and more. OxResearch – succinct articles covering regional economic and political developments of significance from a network of 1,000 faculty members at Oxford, other leading universities, and think-tanks. Snapshots – market research overviews on 40+ industries and 40 countries : custom data loaders

KG+VDB RAG LLM per agent Microsoft Azure Arize Phoenix (LLM Observability)

Member Model

The model for the member

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User Model Tasks

  • - User Model Generation
  • - User Register with Captcha Protection
  • - Payment Management
  • - Email Recovery and Authentication

Member Model extending User->Appwrite in Python

Example of Member Model


class Member:
    def __init__(self, user_id, name, registration, status, email, email_verification, password_update, preferences, roles): = user_id  # unique identifier = name  # name of the user
        self.registration = registration  # registration timestamp
        self.status = status  # account status = email  # email address
        self.email_verification = email_verification  # email verification status
        self.password_update = password_update  # last password update timestamp
        self.preferences = preferences  # user preferences
        self.roles = roles  # user roles

    def is_active(self):
        return self.status == 1

    def is_email_verified(self):
        return self.email_verification

    def has_role(self, role):
        return role in self.roles

    # You can add more methods to interact with the Member object

Member Model Extends User Object

Appwrite is a development platform that provides back-end services for web and mobile developers. One of its core features is the user management system. The User object in Appwrite represents an individual user of an application.

User Object

Region Model

The core reigion model for the project

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Support and Accessibility

With round-the-clock availability, the AI ensures that help is always at hand. Whether it's legal queries or marketing advice, the system provides real-time assistance, making the entrepreneurial journey smoother and more manageable.

Atlas Notes

Whatever you want


Just pick one below and the sky is yours!

Autonomous Tacit Learning Agent System

Autonomous Training Layered Agent System

Agent That Learns, Agent that Sings


The application stack utilizes React on the frontend to provide a dynamic and responsive user interface. On the backend, Appwrite serves as a secure and scalable platform for managing user data, authentication, and other server-side operations. This combination offers a modern, full-stack solution for web applications.


  • React





  • Starts Friday @ 12PM EST




  • In the context of machine learning agents, autonomous refers to the ability of an agent to make decisions, take actions, or perform tasks without explicit instructions from a human. Instead, the agent relies on its training, data inputs, algorithms, and sometimes even its own self-generated strategies to perform its functions. Here are a few key points to understand about autonomous machine learning agents:

    • Learning and Adapting : Autonomous agents not only operate on their own but also have the capacity to learn from their environment and experiences. This ability to adapt makes them more effective over time.
    • Decision-making : Autonomous agents make decisions based on their training data, learned experiences, and sometimes rules or guidelines embedded in them. They assess the current situation and take appropriate actions based on patterns they’ve recognized.
    • No Continuous Human Oversight : Once deployed, an autonomous agent doesn’t need constant human supervision. However, this doesn’t mean humans are out of the loop; there might be periodic checks, updates, or interventions, especially if the agent isn’t behaving as expected.
    • Self-correction : Some advanced autonomous agents can recognize when they make an error or when there’s a more efficient method to achieve their goal. They can then adapt their strategies accordingly.
    • Goal-oriented : Typically, autonomous agents have specific tasks or goals. Their autonomy is directed towards achieving these objectives as efficiently and effectively as possible.
    • Interaction with Environment : In many cases, especially with agents operating in complex environments, there is an element of interaction with the environment. The agent takes in information, processes it, makes decisions, acts, and then receives feedback from the environment, creating a continuous loop.
  • It’s essential to note that “autonomy” in machine learning is not absolute. The degree to which an agent is autonomous can vary. Some might only operate within strict boundaries or specific conditions, while others might have broader operational capabilities.


  • In the context of machine learning, training refers to the process by which a machine learning model learns from a set of data to make predictions or decisions without being explicitly programmed to perform the task. Here’s a more detailed breakdown:

    • Data : Training begins with a dataset, which consists of input data and the corresponding correct outputs. This dataset is called the training dataset.
    • Model Architecture : Depending on the problem at hand, an appropriate machine learning algorithm or model architecture is chosen. This could be a linear regression for simple trend predictions, a deep neural network for image recognition, or any other algorithm suitable for the specific task.
    • Learning Algorithm : The chosen model uses a learning algorithm to adjust its internal parameters. This adjustment happens iteratively, where the model makes a prediction using the training data, compares its prediction to the actual output, and then adjusts its parameters to reduce the error.
    • Objective/Cost/Loss Function : The difference between the model’s predictions and the actual output is measured using an objective function, sometimes referred to as a cost or loss function. The goal of training is to minimize this function.
    • Iteration : The learning algorithm typically makes multiple passes over the training data, each time updating the model’s parameters to reduce the error. The model is said to “converge” when additional training no longer significantly reduces the error.
    • Validation : While the model is being trained, it’s also important to periodically test its performance on a separate dataset (called the validation dataset) to ensure it’s not just memorizing the training data (a problem known as overfitting).
  • Once training is complete, the model should have adjusted its parameters such that it can make accurate predictions on new, unseen data. This final test, usually on another separate dataset called the test dataset, evaluates the model’s true predictive capability.


  • “Layer” is related to neural networks, especially deep learning, as the concept of layer refers to a collection of nodes operating together at a specific depth within the network.
  • Deep learning models, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can have multiple layers, which is why they’re often referred to as “deep” networks.


  • “Agent” typically refers to any entity that perceives its environment through sensors and acts upon that environment through actuators.
  • The primary goal of an agent is to perform actions that maximize some notion of cumulative reward.
  • The agent achieves this by learning the best strategy from its experiences, often without being explicitly programmed to perform a specific task.


  • System typically refers to a combination of algorithms, data, and processes designed to perform a specific task or set of tasks based on data-driven learning. Furthermore, a machine learning system can be broken down into several components:

    • Data Collection : Before training a model, relevant data must be collected. This could be images, text, sensor readings, or any other type of data.
    • Data Preprocessing : Once data is collected, it often needs to be cleaned, normalized, or transformed in some way to be suitable for training.
    • Model Selection : Based on the task at hand, a particular algorithm or type of model (e.g., neural network, decision tree, support vector machine) is chosen.
    • Training : This is the process where the chosen model learns patterns from the training data. The model adjusts its internal parameters based on the feedback from a loss function, which indicates how well the model is performing.
    • Validation : During or post-training, a separate set of data (validation data) is used to tune the model and prevent overfitting.
    • Testing : Once the model is trained and tuned, its performance is evaluated on a test set, which consists of data it has never seen before.
    • Deployment : If the model performs satisfactorily on the test set, it can be deployed in a real-world setting, whether it’s for predicting stock prices, diagnosing diseases, or any other application.
    • Maintenance and Monitoring : Once deployed, the performance of the model needs to be continuously monitored. It may require periodic retraining or fine-tuning based on new data or changing conditions.

The entire collection of these components and stages, orchestrated together to achieve a specific goal, can be referred to as a “machine learning system”. Such a system often interacts with other systems, like databases, user interfaces, or APIs, especially in production environments.