Full Delivery Case Study
ASSes
Objective
Determine the ecological and operational viability of Anemo’s monitoring system and its readiness for real-world trials.
Tasks
Technical review of underwater camera hardware and AI analytics against operational conditions typical of offshore and coastal environments.
Identification of critical technical risks (power autonomy, anti-biofouling, data fidelity) and ecological constraints (visibility, species detection biases).
Gap analysis mapped to Technology Readiness Levels (TRL) to benchmark readiness for trial deployments.
Outcomes
Aquatic Dynamics provided an independent feasibility assessment that clarified technical and evidential risks, prioritised refinement needs, and outlined pragmatic steps required to reach deployable TRL. This appraisal informed decision-making on pilot planning and risk mitigation early in development.
Define
Objective
Align the technology’s capabilities with appropriate use-cases, stakeholder requirements, and data needs for regulatory acceptance.
Tasks
Defined priority monitoring objectives (e.g., species abundance, richness, behavioural assessments) that would deliver defensible metrics for clients and regulators.
Developed evidence requirements that transition Anemo’s outputs from exploratory to operational insight, including species identification accuracy, sampling protocols, and temporal resolution needs.
Established a monitoring plan template linking data outputs to ecological questions relevant to offshore infrastructure, ports, and nature-inclusive structures.
Outcomes
A tailored evidence framework that ensured subsequent pilots generated usable, defensible biodiversity data aligned with regulatory expectations and scientific best practice.
Demonstrate
Objective
Design a pilot that would validate the technology in situ and generate robust ecological data under real environmental conditions.
Tasks
Developed a pilot design encompassing site selection, deployment configuration (camera positioning, survey duration, environmental variability), and monitoring protocols.
Integrated environmental variables like tide, light, and turbidity into experimental design to ensure broad applicability of results.
Built a monitoring framework to evaluate system performance (hardware reliability, AI detection accuracy, data completeness) against agreed evidence tiers.
Outcomes
A defensible pilot design capable of demonstrating operational performance and ecological insight — laying the foundation for scientific interpretation and value validation.
