Intimebot – A Dialogue Agent for Timekeeping Support (2025)

Abstract: This demo paper presents intimebot, an AI-powered timekeeping solution designed to assist with timekeeping. Timekeeping is a fundamental but also overwhelming and complex task in many professional services practices. Our intimebot demo demonstrates how Artificial Intelligence can be utilized to implement a more efficient timekeeping process within a firm. Based on brief work descriptions provided by the timekeeper, intimebot is able to (1) predict the relevant combination of client, matter, and phase, (2) estimate the work effort hours, and (3) rewrite and normalize the provided work description into a compliant narrative. This can save a significant amount of time for busy professionals while ensuring terms of business compliance and best practices.

## introduction timekeeping constitutes a fundamental process in professional services business operations because, when properly done, it ensures accurate and timely billing, which is a necessary condition for a healthy revenue stream for the firm. however, timekeeping is also typically a very taxing and overwhelming task to the busy professionals, who can see their potential billing hours significantly impacted due to poor timekeeping practices @xcite . supporting timekeeping activities with natural language processing technologies is a compelling proposition because of its potential impact on both firms and professionals. on the firm side, more timely and accurate timekeeping reduces revenue losses due to compliance issues, missed work items, and delayed billing cycles. on the professional side, proper timekeeping support can reduce the burden of clerical tasks, improve the quality of reporting, and increase the amount of effective billable hour availability. this demo paper presents intimebot, a dialogue agent to support professional services timekeeping tasks. the system starts with a brief description of the work to be reported. from there, it interactively guides the user through the process of creating a compliant timecard, including the corresponding narrative following the required guidelines. ## related work multiple recommendations and guidelines have been proposed to improve timekeeping practices over time @xcite @xcite . similarly, a myriad of timekeeping solutions and tools are available @xcite @xcite . more recently, advancements in generative ai along with the corresponding enablement of agentic frameworks are steering timekeeping automation into a new era of possibilities, specifically for the case of generative ai applications @xcite . our proposed intimebot demo and experimental framework represents an important incremental effort in that new direction. ## problem statement the intimebot framework focuses on the creation of a compliant timecard from a brief description of the work done, which is typically a memo entry or personal note provided by the timekeeper in a timely manner. from such a brief work description, all timecard fields are to be estimated. these are: the client for whom the work is done, the corresponding matter and phase, as well as the estimated amount of worked hours and the narrative. the narrative must be compliant with both firm stylistic and formatting guidelines, as well as to terms of business agreed for the specific client and matter. the brief description can be provided by means of two different modalities: text or speech. ## system description the intimebot system implements an interactive workflow, which integrates different technologies (supervised classification, information retrieval, natural language understanding and generation) along with support data (including matter histories and timekeeper's previous entries), to estimate the complete set of timecard information. figure 1 depicts the overall workflow of the system. the "predictive models" block of the diagram in the figure comprises four different components: client-matter-phase prediction: for a given timekeeper and work description, it predicts the proper combination of client, matter and phase. work hours (effort) estimation: for a given work description, it estimates the expected value and distribution of the corresponding work hours. narrative correction and normalization: given the work description, it rewrites it into a proper narrative that follows the firm's formatting and stylistic guidelines. compliance validation: this sub-module checks the resulting proposed timecard against contractual commitments for potential compliance issues. each of these four components is described in further detail in the following subsections. ## user experience the user experience of intimebot is designed to be an interactive framework in which the user and the system are able to collaborate, building over time the needed data resources for improving prediction performance, while improving the efficiency and overall experience of the timekeeping process for the user. the current intimebot user experience is divided in four stages: reporting, selection, validation and submission. all these four stages, which are described next, are illustrated in figure 2 . reporting: in this step, the timekeeper enters the brief description of the work conducted. two input modalities (text and speech) are available. selection: after the input is provided, the clientmatter-phase prediction model will select the top work-units matching the provided descriptions, for which the timekeeper is required to manually select the correct one. validation: after work-unit selection, the effort estimation, narrative correction and normalization, and compliance validation models are used to generate the proposed timecard. at this stage, the timekeeper can revise and edit the timecard. submission: after validating the timecard, the timekeeper can submit it, for which the system will provide a submission confirmation message. additionally, the intimebot platform taps on our existent smart-memo feature, integrated via text and/or voice input. timer functionalities can also be used to prompt the timekeepers for descriptions after, or even before, the timer is activated. finally, the framework allows for the implementation and evaluation of gamification ideas to encourage timekeepers to report their time entries as soon as they complete their work. ## future work the presented intimebot demo system constitutes an experimental framework for showcasing and testing ai and ml capabilities in the timekeeping space. as part of this experimental framework, there are a few novel strategies and features we plan to test. these include: