Compare Future AGI vs. Sapling AI Writing Assistant

Utilize our automated insights and customizable metrics to assess, enhance, and perpetually refine your GenAI models. Future AGI streamlines the evaluation of AI model outputs by automatically scoring them, which removes the necessity for manual quality assurance assessments. As a result, your QA team can redirect their efforts toward more strategic initiatives, potentially boosting their efficiency and capacity by as much as tenfold. This ensures that your AI-driven customer interactions remain consistently positive and aligned with your brand identity. By optimizing your models, you can highlight the most pertinent and engaging content tailored to each user. Additionally, you can fine-tune your models to produce the most precise summaries for your audience. Future AGI empowers you to establish bespoke metrics that assess your AI model's accuracy according to the specific priorities of your use case. You can articulate your essential metrics in natural language, providing your QA team with greater adaptability and authority to evaluate model performance. This approach guarantees that your assessments are in harmony with your business goals, transcending conventional metrics such as relevance while promoting a more comprehensive evaluation framework. Embracing this method not only enhances model performance but also fosters a culture of continuous improvement within your organization.

AI assistant for customer-facing departments. Sapling works with messaging platforms and CRMs to assist agents in creating personalized responses. Managers can gain conversational insights that will help them coach and prepare their teams. Sapling offers functionality for autocomplete, spelling, grammar checking, as well as snippets/canned message/text expander. All services can be hosted on-premise or in the cloud to meet security and privacy requirements. The Grammar and Spelling APIs allow Sapling's language models to be integrated into custom applications and use-cases. Sapling Suggest pulls relevant answers from a team knowledge database and allows agents to respond faster to customer inquiries by clicking on the desired response. Instead of giving brittle, decision tree-based chatbots, empower agents.