AKILI
AI-Enabled Conservation Data Management and Biodiversity Analytics
Conservation science is increasingly data-rich—camera traps, sensors, field observations, tracking, and emerging data streams like acoustics and eDNA can generate massive volumes of information. But valuable insight is often buried in the scale and complexity of the data, and made harder by the need to share, standardize, and collaborate across organizations.
The Akili Program is an AI-enabled conservation science platform designed to help teams manage, enrich, analyze, and share sensor-driven biodiversity data. Akili reduces the manual burden of processing large datasets and strengthens scientific workflows by supporting machine learning, computer vision, and open standards—so researchers and practitioners can spend less time sorting files and more time generating defensible ecological insight.
What is the Akili Program?
Akili is an open-source platform built to support end-to-end biodiversity data workflows—from sensor and project management to AI-assisted processing, human review, and export/sharing for analysis and reporting.
Akili is designed to:
Manage diverse sensor data (starting with camera trap workflows, with future provisions for additional sensor types)
Securely facilitate data interchange across multiple stakeholders
Enrich sensor data using both human methods and automated AI tools for tagging and augmentation
Provide an extensible ecosystem of AI tools that support data management and collation
Enable collaboration through open standards and integration points
Preserve data sovereignty, giving stakeholders control over where data is stored and how it is shared
Why AI-enabled CONSERVATION intelligence matters
Conservation science teams face a common bottleneck: too much data and not enough time. Akili uses AI to accelerate the highest-friction parts of ecological monitoring—especially the tasks that slow down analysis and delay decisions.
Akili supports conservation science use cases where AI and machine learning can add immediate value, including:
Separating “empty” media so reviewers focus on the images that matter
Camera trap data analytics to strengthen biodiversity assessment and reporting
Species distributions and habitat use insights across space and time
Habitat quality assessment using machine learning-derived indicators
Behavioral analysis to identify patterns in movement and activity
Individual identification and recapture studies to support monitoring over time
Acoustic analysis (where supported) for species detection in challenging environments
Population and health monitoring by detecting ecological signals and anomalies
eDNA analysis (planned future support) to broaden biodiversity assessment capability
Real-time notifications and workflows that help teams respond faster to priority signals
Data export and interoperability that make multi-platform conservation science easier and more reproducible
What Akili helps teams do
Akili is designed to streamline the complete conservation science workflow, helping teams:
Organize projects, sensors, deployments, and metadata for structured, long-term monitoring programs
Upload/import large datasets (including bulk upload of photos and historical metadata) without fragile workarounds
Process media with ML models to filter, tag, and enrich data at scale (including blank/“empty” detection workflows)
Review, confirm, and annotate AI outputs so datasets remain accurate, transparent, and scientifically defensible
Filter and export targeted subsets of data for research, reporting, and analysis
Share data securely across teams and organizations, with flexible access controls and licensing approaches
Support low-connectivity and offline workflows, enabling practical use in remote field conditions
Improve data sovereignty by supporting a federated approach to storage and access
Integrate with complementary conservation technology tools to reduce duplicated effort and accelerate collaboration
KEY capabilities
AI-enabled analysis and decision support
Akili is built to support AI-forward conservation science without forcing teams into brittle, one-off pipelines. Core capabilities include:
AI/ML processing of media using conservation-relevant models (with support for common blank/subject detection workflows)
A workflow for human confirmation and annotation of AI tags to improve accuracy and scientific confidence
The ability to integrate with a variety of AI tools and providers—supporting diverse analytical needs
An ML Model Marketplace concept that enables broader reuse and accessibility of models, while respecting licensing and ownership choices
Operational intelligence that supports real-world workflows
Akili is designed around field realities—remote deployments, variable capacity, and inconsistent connectivity—so teams can run reliable monitoring programs at scale:
Robust upload/import pathways for large datasets and historical metadata
Support for low-connectivity and no-connectivity environments, including offline-oriented tooling (where required)
Multi-language support to enable broader global participation (including support for common names and local workflows)
Practical project and deployment management features that keep monitoring efforts organized and repeatable over time
Stronger coordination across teams and partners
Akili strengthens collaboration by making conservation data more portable, reusable, and interoperable:
Adoption of the Camera Trap Metadata Standard (CTMS) to support consistent data structure and sharing
Integration pathways and APIs designed for interoperability, including bulk upload and export workflows
Compatibility with common conservation platforms and analysis tools, supporting cross-team workflows and shared standards
Secure sharing controls—built to enable collaboration while protecting sensitive information and ownership rights
Prevention-first conservation outcomes
In conservation science, “prevention-first” means earlier ecological signal detection, faster learning, and better adaptive management. Akili supports that shift by:
Reducing time-to-insight from field collection to analysis-ready datasets
Increasing monitoring consistency across seasons, sites, and teams
Enabling higher-frequency, lower-friction review and reporting cycles
Helping teams detect change sooner—supporting more proactive, evidence-based ecosystem management
Who the Akili Program is for
Akili is designed for the conservation science and monitoring community, including:
Conservation scientists and biodiversity monitoring teams
Protected area ecologists and research units
Conservation NGOs and landscape initiatives running long-term monitoring programs
Universities and research institutions collaborating across sites and regions
Community-based monitoring programs that need practical, field-ready workflows
AI/ML developers and conservation tech partners building models, tools, and integrations for biodiversity data
Results that matter: faster insight, stronger science
Akili strengthens conservation science by reducing the manual burden of large dataset processing, improving data standardization, and enabling repeatable, transparent workflows for AI-assisted analysis. The results include:
Less time spent sorting and cleaning raw media and metadata
More consistent datasets for rigorous analysis and long-term comparability
Stronger collaboration through open standards and secure sharing
Faster turnaround from field collection to analysis-ready outputs
AI-ready biodiversity datasets that support better monitoring, reporting, and adaptive management
Get started with the Akili Program
If you’re looking for AI for conservation science, machine learning for biodiversity monitoring, computer vision camera trap analysis, and standards-based data workflows that scale across projects and partners, the Akili Program is designed to help your team move from raw sensor data to reliable ecological insight—faster and more consistently.
Contact us to explore Akili implementation, integrations, and how the platform can support your monitoring goals and research priorities.
