Objectives of the Product

Organisations across sectors such as agriculture, forestry, infrastructure, and insurance face major barriers in applying Earth observation (EO) data. Creating tailored AI models for tasks like object detection or segmentation requires costly manual labelling, deep technical expertise, and months of development time. As a result, many EO projects are delayed, scaled back, or abandoned.

FlyPix AI addresses these challenges with the AI Visual Copilot, a no-code SaaS solution that streamlines EO model creation. By combining one-shot learning with AI-assisted annotation, the Visual Copilot reduces labelling efforts by up to 95% and cuts model development time from months to days. Users without AI expertise can interactively define models, leverage smart labelling suggestions, and deploy fit-for-purpose solutions at scale.

The Visual Copilot integrates seamlessly into the FlyPix AI platform, supporting diverse EO data sources from satellites, drones, and aerial imagery. Its modular design ensures scalability, adaptability, and rapid prototyping for a wide range of applications, from land use monitoring and biodiversity protection to damage assessment and infrastructure tracking.

This solution empowers customers to transform raw EO data into actionable insights faster, cheaper, and with greater flexibility, unlocking new opportunities for data-driven decision-making across industries and public authorities.

---

Customers and their Needs

The targeted customers include geospatial and geographic information systems (GIS) service providers, aerial inspection firms, insurers, public authorities, and environmental agencies. These organisations rely on EO data to monitor assets, assess risks, track environmental change, and support critical decisions. However, they face significant challenges in leveraging EO data effectively.

Key needs include:

• Faster model creation: Current workflows require extensive manual labelling and weeks of preparation, delaying projects.
• Lower costs: Tailored AI models demand high budgets, often consuming most of a project’s resources.
• Ease of use: Many customers lack in-house AI expertise, making advanced EO analytics inaccessible.
• Scalability: Models are difficult to adapt across datasets, regions, or new use cases.
• Confidence in ROI: High upfront investment and uncertain outcomes discourage adoption, especially among SMEs and public bodies.

Through the activity, pilot customers such as insurers, GIS firms, and aerial inspection providers will directly test the Visual Copilot, providing feedback on workflows and usability. By enabling no-code, AI-assisted model creation that reduces costs and time by up to 95%

---

Targeted customer/users countries

Based on the proposal, the targeted customer/users’ countries are Germany, Austria, Netherlands, and the rest of EU countries; United Kingdom, USA, Japan and India.

In addition, the product is positioned for broader uptake across Europe, with future expansion to North America and Asia-Pacific.

---

Product description

FlyPix AI Visual Copilot is a no-code, cloud-based solution that accelerates the creation of AI models for EO data. It enables users to perform object detection, segmentation, and tracking on satellite, aerial, and drone imagery with minimal technical expertise. Customers interact through an intuitive interface where they can define tasks visually, apply AI-assisted labelling, and deploy models within hours rather than months.

Innovation lies in the one-shot learning approach, enabling model creation from a single example, combined with automated labelling and modular scalability. This transforms EO analytics into a fast, affordable, and user-friendly process, unlocking new opportunities for industries such as agriculture, forestry, insurance, and infrastructure monitoring.

---

Added Value

FlyPix AI Visual Copilot delivers a step-change in how organisations build AI models for Earth observation by eliminating the biggest barriers—cost, time, and lack of expertise. Competing platforms require large, annotated datasets, weeks of preparation, and advanced technical knowledge, making EO-driven insights inaccessible to many organisations.

In contrast, FlyPix AI Visual Copilot allows users to build accurate detection or segmentation models from just a single example. Combined with AI-assisted annotation, it reduces labelling efforts by up to 95% and lowers project costs by as much as 70%. This makes EO analytics affordable for small enterprises, local authorities and NGOs, in addition to large commercial players.

Unlike competitors focused on narrow use cases, Visual Copilot offers broad versatility—supporting tasks from agriculture monitoring and forestry management to insurance damage assessment and infrastructure tracking. Its no-code interface empowers non-experts to create, validate, and deploy models quickly, accelerating decision-making in critical areas such as climate resilience, urban planning, and disaster response. The added value lies in democratising EO model creation: transforming months of expert-driven work into a simple, scalable, and cost-effective workflow that unlocks new opportunities for innovation and impact across industries.

---

Current Status

AI Visual Copilot activity builds on a working prototype already integrated into the FlyPix AI platform. Initial proof-of-concept projects have been successfully delivered for customers in insurance, forestry, and infrastructure, validating both the technical approach and market demand. Letters of Interest (LoIs) from pilot customers confirm strong commitment to adopt the solution once available.

Currently, the team is refining the prototype into a scalable SaaS module, focusing on one-shot learning, AI-assisted labelling, and seamless EO data integration. Pilot users are engaged in requirements gathering and early validation to ensure usability and alignment with operational needs. The upcoming phase will expand testing with multiple pilot customers, finalise integration with EO data providers, and prepare the platform for commercial rollout. With InCubed’s support, the activity is on track to deliver a validated, market-ready product by early 2026.