Building AI Agents to reinvent recruitment for companies and candidates

A single web page has hundreds of elements. Our AI Agents combine advanced Machine Learning techniques to handle the complex task of web page interpretation. Task predictions are performed through state-of-the-art Computer Vision and fused with the output of HTML analysis and Optical Character Recognition to yield a solid grounding of UI detections.

SonicJobs' proprietary models are trained with a combination of Supervised, Unsupervised and Self-Supervised techniques, and leverage a continuously growing amount of proprietary real-world data coming from our production environment, along with synthetic data generated using in-house technology.

Input UI controls and widgets on web pages exhibit great variability and a broad range of implementation options, including custom-made date pickers, dynamically-populated lists, overlays and pop-ups. Our AI Agents understand these input controls and interact with them, which is a very challenging task.

The AI Agents are able to leverage the HTML structure of an input control, as well as its visual aspect and any surrounding readable information, to pick the most suitable technique to interact with it using a vast repertoire of options. The range of techniques guarantees ample coverage of the interaction patterns encountered in web pages.

Building upon a deep domain-grounded understanding of web pages, the AI Agents identify the most proper set of actions to be undertaken in order to advance the job application flow, filtering elements on the page that are not required. This includes understanding screening questions, resume upload, and clicking on the 'Next' and "Apply" buttons, to progress along multiple pages within the job application flow.

If the outcome of the chosen action is not what is expected, a buddy Agent specialized on error detection advises on correction or recovery measures to allow the main Agent to proceed with the flow.

Moreover, the AI Agents implement a Strategic Reasoning level, which affects the navigation flow from a broader perspective, allowing the Agent to try alternative paths to maximize success. An example of Strategic Reasoning is the capability of the Agent to deal with conditional questions with multiple dependencies.

The choice of relying on AI rather than classical automation to interact with web pages allows the AI Agents to adapt to small or big changes for known job application flows as well as completely new and unknown flows.

SonicJobs proprietary technology, based on tactical and strategic reasoning coupled with error recovery, provides unmatched resilience and sets the standard in the Web AI Agents space.

In addition, the unique architecture of the AI Agents, includes advances in Multi-Agents frameworks, Multimodal Models and Model Ensembles, allows for a rapid integration of new technological evolutions, such as those tied to LLMs and Foundational Models.

For enterprise AI Agent solutions, the interconnectivity between the domain-specific application layer and the general-purpose reasoning layer needs to be very strong. This is because accuracy and reliability are very important for enterprises, particularly for mission-critical workflows. Consequently, the architecture of the Agents needs to be specific to the domain (e.g. jobs), as well as generalizable enough to be expanded to further domains.

Our AI Agent technology is built for scale, with the core models targeted at capturing features that are common across websites (e.g. UI elements detection).