Building GenAI Into Your Corporate Toolkit

From Concept to Reality: Getting Started

Generative artificial intelligence (GenAI) is transforming how we interact with technology by enabling machines to autonomously create new content, insights and solutions. Unlike traditional AI, which focuses on analyzing existing data, GenAI uses advanced models like generative adversarial networks (GANs), variational autoencoders and transformer-based architectures, such as GPT, to generate entirely new outputs, whether text, images, code or simulations. This makes GenAI a powerful tool across creative industries, product design and data augmentation, where it mimics human-like patterns and structures to produce original content. Its versatility is driving innovations like automated content generation and synthetic data creation for machine learning.

At Aptive, innovation experts are leveraging GenAI to enhance operations and deliver advanced solutions for clients. By integrating generative models into their processes, Aptive automates content production, improves creative workflows and empowers decision-making with AI-generated insights. With expertise in natural language and code generation, image synthesis and data augmentation, Aptive is pushing the boundaries of innovation, offering unparalleled value across industries such as marketing, health care and finance.

Creating an AI Chatbot

When Aptive’s innovation team answered the call for a company chatbot, team members first conducted extensive research on existing marketplace tools. This research was essential to understand the capabilities, limitations and potential of third-party and open-source platforms, and to assess whether they could meet our needs or serve as a foundation for our own AI-driven products.

Commercial and Third-Party Solutions. The rise of conversational AI has led to various third-party platforms, such as OpenAI’s GPT API, Google’s Dialogflow, Microsoft’s Azure Bot Service and IBM’s Watson Assistant. These tools offer robust features like natural language understanding, multi-channel integration and broad conversational capabilities. However, relying on external platforms can limit customization and flexibility, especially for specific business requirements. Concerns around data privacy, ownership and long-term subscription costs also pose challenges, particularly when scaling operations.

Open-Source Alternatives. Open-source frameworks like Rasa, Botpress and Microsoft’s Bot Framework provide more control over customization and data privacy, making them attractive for building tailored chatbots. Open-source large language models (LLMs), such as GPT-Neo and GPT-J, offer access to state-of-the-art generative models without proprietary constraints. However, these solutions require significant technical expertise in machine learning, natural language processing (NLP) and software engineering. Moreover, organizations are responsible for maintaining, updating and securing these tools, which demand considerable resources.

Going It Alone

While third-party and open-source solutions offer powerful options, our team found limitations in flexibility, privacy and long-term scalability. After a thorough examination of both commercial and open-source options, we concluded that while many of the available tools offer impressive functionality, none are fully aligned with our specific vision and unique capabilities and business needs. Aptive needed a solution that could provide deep customization, full control over data and the flexibility to scale and innovate without the limitations imposed by third-party services. By developing an in-house solution, we could ensure our AI aligns perfectly with our strategic objectives and offer clients a tailored, cutting-edge experience. We therefore decided to develop our own AI chatbot and LLM in-house.

We began by collaborating with our PeopleOps department to identify an initial use case that would maximize the impact of our AI solution within Aptive. This approach allowed us to address a real business need while also ensuring the chatbot’s functionality aligned with our operational goals.

Stay tuned for Part 2 of this series to follow our development journey to create a customized company AI chatbot.