DroneDeploy’s announced a new agentic AI solution for automated progress tracking named Progress AI. Its target audience consists of construction and project teams seeking comprehensive progress visibility, but the product is intentionally designed for use cases that span energy, utilities (solar, O&G), and any organization that constructs physical assets.
The solution addresses key business problems such as accurately tracking project milestones, as well as validating pay applications, reducing trade conflicts, and minimizing the overhead that comes from manual progress documentation. Progress AI combines advanced vision-language models (VLMs) with aerial and ground reality capture data (photos, walks, panos, drone flights) to deliver unified, automated insights into project status -- often in minutes rather than days. This allows teams to be more proactive, collaborative, and effective without adding headcount or complexity.
Product features
Progress AI’s features include automated detection of active, stalled, or completed work on-site. “The solution generates contextual progress reports that can highlight project bottlenecks, trade sequencing, inspection readiness, and milestone completion. It also provides support for both aerial and ground use cases, benefiting a variety of asset types and enabling organization wide insights, and voice and chat-based interfaces for easy data querying and report access”, explains Jim McCartney.
The solution is designed to flag inconsistent or missing data within its analysis process. Gaps or conflicts get surfaced as part of the auto-generated progress reports, so users are immediately aware of the integrity and potential shortcomings of their source material. This reporting ensures that teams can address issues with their capture cadence or data coverage as part of their daily workflow.
Additional benefits
Progress AI is more than just milestone tracking as it provides the following additional benefits, such as reduced manual reconciliation and faster validation of project documents such as pay applications, as well as conflict reduction between trades by automatically pointing out stacked or stalled trades by location and where progress delays may occur, empowering teams to shift from reactive to proactive management via timely insights. Additionally, it provides streamlined reporting that doesn’t add extra administrative burden, and scalability across different types of assets and organizations, enabling enterprise-level progress measurement and visibility.
Leveraging Vision-language models (VLMs)
Progress AI leverages advanced vision-language models (VLMs) to interpret “real-world visual data from drone flights and 360 walkthroughs”. Vision-language models (VLMs) are AI algorithms that combine image (vision) understanding with natural language reasoning, explains McCartney: “Progress AI leverages VLMs to analyze both aerial and ground imagery for construction activity, progress changes, and work status. This visual information is translated into contextual, actionable text summaries and reports, and allow users to ask complex questions about site activity, and get relevant, text-based answers”.
Progress AI is accessible via voice or chat interfaces, meaning users can engage with the system in natural language, either by speaking or typing. Users can ask specific, targeted questions about their project’s status, and receive instant, AI-augmented answers and visual/summary progress reports.
Reports are delivered through DroneDeploy web app integrations, appearing as contextual overlays or downloadable summaries. This seamless delivery ensures users can incorporate insights directly into their daily decision-making processes. Users also receive an email summary of progress reports.
