The Problem:
One of the foremost concerns with using public LLMs like ChatGPT is the potential exposure of sensitive or confidential data. Queries containing proprietary information might be stored by the LLM provider, creating substantial data privacy risks. Organizations need to be cautious about submitting sensitive data to public LLMs to prevent leaks and breaches.

How Bulbul Resolves It:
Bulbul takes data privacy and security seriously. By intercepting questions from users and responses from the LLM, Bulbul ensures that sensitive information is not sent to third-party servers. Our extensive checks on both our servers and local systems safeguard organizations from data leaks through user queries. This multi-layered approach guarantees that your data remains confidential and secure.

The Problem:
LLMs can generate biased, offensive, or inaccurate content due to inherent biases in their training data or algorithms. Organizations have limited control over the outputs, which can conflict with their brand values or messaging, potentially harming their reputation.

How Bulbul Resolves It:
Bulbul provides a solution by intercepting and reviewing responses before they reach the user. We ensure that the content aligns with company policies and values, filtering out any inappropriate or biased information. By maintaining control over the output, Bulbul helps organizations present consistent and brand-appropriate messaging.

The Problem:
LLMs can produce plausible-sounding but factually incorrect information, a phenomenon known as "hallucinations." Relying on such erroneous outputs can lead to costly mistakes and the spread of misinformation within an organization.

How Bulbul Resolves It:
Bulbul employs a multitiered agent system that vets responses for accuracy before presenting them to the user. This rigorous vetting process ensures that the information provided is correct and reliable, significantly reducing the risk of errors and misinformation.

The Problem:
While LLMs possess broad knowledge, they often lack the depth required for specialized domains or tasks. This limitation can reduce their effectiveness in industries that demand domain-specific expertise.

How Bulbul Resolves It:
Bulbul enhances LLM capabilities by injecting the context of the user and the business into queries. This approach ensures that domain knowledge necessary for accurate responses is always available. Additionally, Bulbul can inject context related to specific events or meetings, tailoring responses to be highly relevant and context-specific.

The Problem:
Integrating LLMs into existing workflows, software, and systems can be complex and resource-intensive. This complexity can hinder the seamless adoption of LLMs in organizational environments.

How Bulbul Resolves It:
Bulbul is designed with modularity in mind, offering easy integration with existing systems. We provide webhooks and straightforward interaction methods with third-party platforms such as Slack, Notion, and others. This flexibility ensures that Bulbul can be smoothly incorporated into your organization's existing workflows.