KENCOPA Launches AI Chat Editing Feature for Its Kencopa Schedule AI Agent

KENCOPA Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Sosuke Yasumura), a company working to reduce labor and automate construction sites, announced the launch of a new “AI Chat Editing Feature” for its construction-focused AI agent, the Kencopa Schedule AI Agent. Construction schedules are central tools for sharing plans from project start to completion. They help identify schedule risks and rework points early, support consensus-building among stakeholders, and enable progress management. However, schedules are not finished once created. They must be continuously updated in response to design changes, staffing constraints, and other changes, making schedule editing a frequent bottleneck in site operations. Schedule editing is often not limited to partial work. Even shifting one activity by a few days requires adjustments that preserve dependencies among related tasks. As a result, operations can become complex, requiring repeated edits while considering multiple factors, and the process tends to depend on the experience of skilled personnel. When adjustments across the entire schedule are needed, the inability to make batch changes can also force users to repeat the same work. This can cause schedules to fall behind actual site conditions, increase meetings and confirmations for updates, and slow decision-making. The AI Chat Editing Feature significantly reduces the burden of schedule editing by allowing users to edit schedules through chat instructions. It enables site staff to focus on “how the schedule should change” rather than “how to operate the tool,” making schedule edits faster and more reliable. With the new feature, users can continue editing a generated schedule through conversational instructions to the AI Copilot chat. Daily revisions such as moving tasks or adjusting durations can be performed by expressing intent in words, instead of manipulating each item on the screen one by one. The feature makes it easier to perform batch operations across an entire schedule and repeatedly create specific schedule patterns, reducing the effort required for post-generation adjustments. For example, users can issue consolidated instructions for changing the overall project schedule, reordering tasks, adding tasks, or splitting tasks, accelerating the process from reviewing changes to reflecting them in the schedule. Because chat instructions support a wide range of editing patterns, schedules can be handled more intuitively. This allows teams to spend more resources on the core question of how to design the schedule, rather than on tool operation, helping improve schedule quality. Since the background and intent of changes can be retained in words during editing, it also supports alignment and handoff among stakeholders. For cases such as creating parallel tasks, the AI can support duration adjustments based on conditions such as quantities and productivity assumptions, helping reduce dependence on individual expertise in schedule creation. Users can quickly test editing directions while reflecting the assumptions of each site, substantially reducing the burden in repeated schedule review and revision scenarios. Examples include using instructions to the AI Copilot to shift an entire schedule forward or backward by a specific period while maintaining relationships among tasks; automatically generating takt schedule lines, such as using the first floor as a reference to create takt schedules for the second floor and above; and consulting the AI when schedule compression is needed. By requesting a change to a two-team structure, the AI agent can understand quantity relationships and productivity rates and generate parallel tasks. The development team commented that, during development, they realized the difficulty of schedule editing lies not in “how to operate the tool,” but in “redesigning the schedule while looking across the entire project and accounting for the scope of impact.” Beyond organizing dependencies among tasks, schedule adjustment requires simultaneous consideration of multiple factors such as schedule constraints, site conditions, and resource assumptions, which often remain locked inside the minds of experienced workers. To address this, KENCOPA built a mechanism in which the AI agent supports editing based not only on relationships among tasks but also on these prerequisite conditions, allowing anyone to design and adjust schedules simply by expressing their intent in words. Freed from editing operations, users can spend more time on the essential task of determining how the schedule should be structured. The company will continue improving the feature as a bridge between site knowledge and engineering, using technology to draw out and expand schedule design capabilities. The Kencopa Schedule AI Agent not only reduces the time required to create schedules and operate execution schedules, but also automatically accumulates design documents, schedules, productivity rates, and construction data during use, building a company-specific knowledge database that supports both labor savings and technical succession. By simply uploading design documents such as drawings, specifications, and estimate documents, an AI trained on company-specific productivity rates and past projects can generate schedules through dialogue (patent pending). After creation, users can edit and operate the AI-generated draft schedule intuitively within the application. Actual progress input and designated PDF or Excel formats can be registered as templates and output flexibly for each site. The company’s proprietary schedule generation engine supports a wide range of project types, including building construction, civil engineering, plants, and facilities. Service website: https://kencopa.com/service-azuchi-3. Contact: https://kencopa.com/contact. The actual schedule generation flow is as follows: after a user uploads design documents, the AI organizes site information such as construction period, work types, address, quantities, surrounding environment, and constraints based on the documents it reads. It then proposes similar past projects automatically based on the construction plan and various similarity indicators. After the user selects a highly similar project, the AI generates a schedule using the selected project as a reference, along with the construction plan, site information, weather information, and other data. When needed, it also creates draft schedules through dialogue on work zone division and parallel tasks. The generated schedule can be edited and operated directly in the application, with functions including monthly and weekly schedules, simultaneous multi-user editing, actual progress line input, earned value curve display, insertion of shapes and images, and PDF or Excel output with freely registerable templates. Users can also shift schedule lines in bulk or split multiple tasks based on chat instructions to the AI. KENCOPA Inc. is a construction-focused AI startup with the mission “Copilot for Construction,” working to reduce labor and automate construction sites. Through the Kencopa Schedule AI Agent and Kencopa Construction AI Agent, the company enables AI to understand and process drawings, specifications, productivity rates, and construction data that previously depended on tacit knowledge, automating schedule creation, document creation, and knowledge search. Daily use also naturally accumulates company-specific data, contributing to young worker training and technical succession. In addition to its own products, KENCOPA offers an “i-Con2.0 Co-Creation” model for joint research and development tailored to individual company needs, through which it aims to seriously challenge the future of the construction industry. Company information: CEO: Sosuke Yasumura. Headquarters: 1-11-2 Hiroo, Shibuya-ku, Tokyo, BLOCKS Ebisu. Established: March 2024. Total funding raised: JPY 210 million. Business: development and provision of the Kencopa Schedule AI Agent and Kencopa Construction AI Agent, as well as joint research and development in construction-focused DX and AI. Corporate website: https://kencopa.com/. Awards and selections include the third cohort of “Kido,” the Kansai Startup Incubation Program hosted by the Osaka Business Development Agency; the Ministry of Internal Affairs and Communications’ R&D support program “ICT Startup League 2025”; Triangle Ehime 2.0 “National Co-Creation Hub Collaboration Category”; the Osaka Business Development Agency’s “OIH Silicon Valley Gateway Program”; Microsoft for Startups; and Manus for Startups.