Dottie info (not linked on public site)

Dottie has many use cases. Some are:

  • Customer support:
    • Email support: Automate responses to frequently asked questions.​
    • Chat support: Provide real-time responses to customer inquiries.
    • Voice support: Build a voice-enabled support system.
    • Multi-lingual support: Provide support services in multiple languages.
    • Call center automation: Assist call center agents with automated responses.
    • Personalized support: Provide personalized support based on individual needs.
    • Social media support: Automatically respond to social media conversations.
    • Knowledge management: Automatically categorize and tag customer support 
  • Sales Assistant:
    • Lead generation: Identify potential leads.​
    • Improved Targeting
    • Personalization
    • Sales support: Automate frequently asked questions.
    • Customer segmentation: Categorize customers automatically.
    • Pricing optimization: Identify optimal pricing strategies.
    • Sales forecasting: Predict future sales trends.
    • Competitor analysis: Analyze competitor data.
    • Sales coaching: Provide automated coaching.


Dottie uses the following technologies:

  • Recurrent neural networks (RNNs): RNNs are a type of neural network that can handle sequential data, making them ideal for language modeling tasks. They can be used to develop custom-trained LLMs by training the network on large amounts of text data and tuning the network’s hyperparameters for optimal 
  • Long short-term memory (LSTM) networks: LSTMs are a type of RNN that can better handle long-range dependencies in sequential data. They have been widely used in natural language processing tasks, including language modeling, and can be used to develop custom-trained LLMs.
  • Prompt chaining: Prompt chaining involves using a sequence of prompts to guide the generation of text from a language model. This technique can be used to generate longer and more coherent responses to user inputs, making it a useful approach for developing custom LLMs for chatbots and conversational 
  • Transformer networks: Transformer networks are a type of neural network architecture that has gained popularity for language modeling tasks in recent years. They use a self-attention mechanism to capture dependencies between different parts of the input sequence and have been shown to outperform RNN-based models on many natural language processing tasks.
  • Transfer learning: Transfer learning involves taking a pre-trained language model, such as GPT-3, and fine-tuning it on a specific domain or task. This approach can significantly reduce the amount of data needed to train a custom LLM and improve its performance.



The benefits of Dottie are:

  • Improved accuracy: A custom-trained LLM can be trained on specific datasets related to a particular domain or use case, improving the accuracy of its predictions and responses.
  • Personalization: Custom-trained LLMs can be trained on specific customer data to provide personalized responses based on individual needs, preferences, and historical interactions.
  • Reduced response times: LLMs can help automate customer support operations, enabling faster response times to customer inquiries and reducing the workload on human support agents.
  • Scalability: LLMs can scale with increasing volumes of customer interactions and provide support in multiple languages without requiring additional staff.
  • Cost savings: Automating customer support operations with LLMs can reduce labor costs and improve efficiency, enabling companies to allocate resources to other areas of the business.
  • Dottie has application in all industries and verticals





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