The Problem

Both blue chip companies and SMEs are under pressure to rapidly implement AI agent technology in order to remain competitive in the new AI-enabled landscape. However, off-the-shelf solutions such as ChatGPT and Claude suffer from the following problems that make them inappropriate for enterprise level application:

Data Sensitivity and Compliance Risks

Businesses often process confidential information that must remain secure and comply with industry regulations. Using third-party platforms like ChatGPT or Claude can raise concerns about data exposure and reliance on external servers.

Generic AI Agent Limitations

Out-of-the-box AI solutions struggle to meet specific business requirements, such as aligning with unique workflows, industry jargon, or customer interaction styles. These limitations make it difficult for organizations to leverage AI effectively.

Infrastructure Challenges for High Usage

For businesses with intensive AI needs, relying on pay-per-use platforms can lead to escalating costs. Additionally, third-party solutions might not be optimized for the scale and specificity of the business, increasing inefficiencies.

Lack of Brand and Operational Control

Pre-built AI solutions lack the customization needed to represent a business's unique identity and brand tone. Furthermore, their features may not fully align with internal operational goals, reducing the overall value they deliver.

Taken together, these challenges are blocking the adoption of agents at scale in business environments, leading enterprises to seek new solutions for creating their own in-house agents.