SMEs as Code are perpetual, immune to holidays, burnout, and salary negotiations. They require no management and effortlessly scale as your needs expand.

FAQs:

i) Will the system hallucinate? No, akin to a flight control system, three built-in processes consistently validate the high fidelity of the system’s responses.

ii) Is this an LLM wrapper? No, it’s a composite AI, combining multiple narrow AI solutions for a generalized AI feel. LLMs are utilized for translation and voice-to-text.

iii) Does my data leave my network? No, your metadata, encrypted, drives advanced features. QueryButler™, residing in your data center or cloud, integrates via Okta or cloud-native IAM.

iv) What is stored? Outside your system? Only metadata for performance. Inside your system? Everything – it’s a learning solution with a comprehensive mapping of past queries, serving as a subject matter expert in code.

v) Set up requirements for QueryButler™? EC2 or any Linux compute layer, a relational database like Aurora, a caching layer, and any Git source control. Grant read access, and QueryButler™ runs under a service account.

vi) How many clients do you have? Three out of the box – IoS iPhone/iPad app, a React web app, and a Slack app. Customization options available, including Microsoft Teams integration in the near future.

vii) Is QueryButler an RPA? No, it orchestrates various workflows internally and externally but is not shadow IT. The integration architecture is loosely coupled via message queues, not tightly coupled via direct plugins.

viii) Do we use a vector DB or knowledge graphs for LLM grounding? A vector DB stores metadata, designed for enterprise data, eliminating the need for a separate graph.