Setting up conversational AI is exactly what we can help you with. Check out the steps below that we use to make certain your robot you build with us will be the best representation of your company.

Data Collection

Gather all of the relevant data to the function that is your goal. Think of everything a person would need to know to complete the task and then train the AI with as much written information as possible. If you have good training manuals, uploading them will pretty much ensure a very capable an skilled robot.

Preprocessing

Clean and preprocess the data to remove noise, correct spelling errors, tokenize the text, and handle other linguistic nuances. This step ensures consistency and quality in the training data.

Model Selection

Choose an appropriate architecture for your conversational AI model, such as sequence-to-sequence models like Transformer-based architectures, recurrent neural networks (RNNs), or variants like LSTM (Long Short-Term Memory) networks.

Training

Train your chosen model using the preprocessed data. This involves optimizing the model’s parameters through iterations to minimize the difference between its predictions and the actual responses in the training data. Techniques like gradient descent and backpropagation are commonly used in this phase.

Evaluation

Assess the performance of your trained model using evaluation metrics such as perplexity, BLEU score, or human evaluation. This step helps ensure that the model generates relevant and coherent responses.

Fine-tuning

Fine-tune the model based on the evaluation results to address any shortcomings or improve its performance further. This may involve adjusting hyperparameters, adding more training data, or refining the training process.

Testing

Test the trained model with unseen data or in a simulated environment to validate its effectiveness and generalization capabilities. This step helps identify any remaining issues or areas for improvement.

Deployment

Deploy the trained conversational AI model in real-world applications, such as chatbots, virtual assistants, or customer service platforms. Monitor its performance in production and continue refining it based on user feedback and usage patterns.

Here is also a guide on setting up conversational AI on your own with Python.

We went through this guide on a recent conversational AI build and found that it was quite useful in reinforcing natural language processing capabilities.