
Project Insight helps organizations connect project data across teams and systems to make better decisions about their business. As a strategic project and portfolio management platform, it brings together resource and capacity planning, financial management, project requests, execution, governance, and portfolio performance.
The result is a connected project data backbone that gives PMOs and executives visibility into organization-wide work and supports trustworthy reporting, forecasting, and AI analysis.
Teams can continue using familiar tools such as Jira, Azure DevOps, Microsoft, Salesforce, ServiceNow, and HubSpot while Project Insight brings their project information into one central command center, reducing manual data entry and disconnected reporting.
Manage projects, programs, and portfolios using flexible workflows, intelligent scheduling, Gantt charts, Kanban boards, time tracking, budget management, and customizable dashboards. Leaders can monitor workloads, project health, costs, and strategic priorities without chasing information across departments.
AI-powered features help create project plans, turn meeting discussions into action items, recommend resources, forecast capacity, and evaluate what-if scenarios.
From growing teams outgrowing spreadsheets to established PMOs with complex business processes, Project Insight adapts to the way organizations work through configurable processes, dedicated onboarding support, and free view-only access for clients and stakeholders.
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Highcharts, a Javascript-based charting library, makes it easy to add interactive charts and graphs to web or mobile projects of any size.
Highcharts is used by more than 80% of the 100 biggest companies in the world, as well as thousands of developers from a variety of industries, including finance, publishing, application development, and data science.
Highcharts is in active development since 2009. It remains a favorite among developers due to its robust feature set and ease-of-use documentation, accessibility features and vibrant community.
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Thesys Agent Builder
Thesys Agent Builder is a no-code solution designed for the development and deployment of interactive AI applications that utilize structured user interfaces such as charts, cards, tables, forms, slides, and reports, all driven by the C1 Generative UI engine. Users have the flexibility to upload various data types, including files, URLs, and databases, while also connecting to different tools, setting the agent’s tone and behavior through natural language commands, and choosing styles that align with their brand identity, ultimately enabling them to launch a live agent for web use or site embedding without the need for frontend coding. This platform allows the creation of AI agents that efficiently analyze and visualize data, respond to inquiries with engaging insights, produce visual summaries and reports, and deliver comprehensive, actionable responses derived from integrated data sources in a matter of minutes. It prioritizes genuine interactivity and practical functionality, transforming conversational exchanges into dynamic UI components that empower users to delve into, assess, and act upon the information presented. By streamlining the development process, Thesys Agent Builder makes advanced AI technology accessible to users without coding skills, promoting a more inclusive approach to interactive application creation.
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Manifest
Manifest, developed by Omfang, serves as an action layer for AI agents, transforming any webpage into a structured representation that delineates the capabilities available to an agent. By submitting a URL to the Manifest API, developers obtain a clean JSON output that details the state of the page, encompassing available buttons, forms, inputs, navigation links, mandatory fields, and interdependencies among actions. Manifest operates by loading the webpage in a genuine Chromium browser after executing JavaScript, examining the accessibility tree utilized by screen readers, and cross-referencing it with the underlying DOM to capture intricate details often overlooked by the accessibility layer, such as input types, placeholders, disabled states, and submission prerequisites. Once potential actions are identified, their locators are resolved with precision instead of relying on model-generated guesses; any ambiguous matches are flagged as uncertain rather than assumed. This process provides agents with a machine-readable framework that enhances their reasoning capabilities regarding interactions like clicking, filling, or submitting without depending on screenshots or fragile CSS selectors. Ultimately, Manifest elevates the efficiency and reliability of AI agents in navigating and interacting with web content.
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