For years, we’ve consulted alongside our customers to build our Artificial Intelligence platform backwards, starting from solving the mission critical enterprise problems.
Our goal is to provide the world’s most seamless experience for working with real-time data, one that enables people to gain rich insights into their dynamic environment.To achieve this, we create intelligent platform products that unlock the value of big data by layering industry-preferred applications on top of a fully integrated human-augmented and machine-assisted analysis.
Winning in the “Knowledge Age” requires overmatch from an adversary not just through firepower, but, more importantly, via decision-making (the OODA Loop: Observe, Orient, Decide, Act).
To achieve an overmatch, raw data from all the sources must be ingested effortlessly, computed on the go, and translated into intelligent decisions. It can then be pushed out as required to the Forward Edge of the Battle Area (FEBA) as a single source of truth for rapid assimilation by end users. Especially with big data being harvested by Communications, Surveillance, Intelligence and Reconnaissance, Space, Electronic Warfare, and Cyber Security assets combined with data from individual battlefield sensors (individuals, vehicles, tactical UAV) across multiple domains, there is an unprecedented gap to aggregate and process this data into knowledge, for cognitive assimilation and decision making.
Knowledge must be rapid, accurate, and continuous to enable decision superiority for the evolving, agile, amorphous ADF. To deliver tactical advantage, this information must be translated into knowledge.
When information is provided unfiltered, and ‘en masse’ to tactical elements, it rapidly results in incorrect interpretation, disorientation, information overload, unsound tactical actions, and/or decision paralysis.
A methodology must be applied for rapidly integrating data from multiple sources to discover relevant patterns, determine and identify change, and characterise those patterns to drive collection and create decision advantage.
Unlike the traditional intelligence cycle, which decomposes multidisciplinary collection requirements from a description of the target signature or behaviour, the concept of large-scale data filtering of events, entities, and transactions to develop understanding through geographical, spatial, and temporal correlation across multiple data platforms is a need of the Australian Department of Defence.
FABRIQ FOR DATA SUPREMACY & COGNITIVE INTEGRATION
In the Knowledge centric warfare era, the ability of belligerents to gather data has increased exponentially. IoT is meant to make your operations smarter. But, too often, IoT frameworks rely on a fragile web of custom APIs, contractors, and microservices. Not only are these point solutions costly and prone to disruption, but they’re siloed from all other key data assets across the enterprise. Our solutions specialise in systems integration and interoperability solutions, with a focus on knitting disparate databases, IOT systems, apps, and platforms together, so each microcomponent works together harmoniously, delivering a capability greater than just the sum of its elements.
Today’s national security forces require systems that can maximise the effectiveness of their digital capability without disrupting their existing operations. Information already being presented to the Defence operation centres could be piped into other systems and provide a much more uniform solution for J1-J8 and also exchange officers from the five eyes community of nations (US, UK, Canada, and NZ) who are largely embedded in Operation and Planning.
IMPROVING & ACCELERATING DECISION-MAKING AT THE EDGE
Strengthening Mission Command
FABRIQ continuously improves operations by:
Bolstering Soldier & Unit Readiness
Drive Information Dominance
Improving Global Force Management
Delivering a Single Source of Truth
Reducing Spend & Optimising ROI
Enhancing Decision Making at the Edge
Harmonising Multi-Cloud & Hybrid-Cloud Portability
Mission planning is a detail-oriented process that is critical to achieving success and ensuring the safety of everyone involved. It requires analysing historical data, assessing multiple risk factors, simulating numerous scenarios, and more. But no matter how in-depth the existing plan is, the unforeseeable can happen during the operation.
FABRIQ provides mission-critical capabilities that integrate data and intelligence from the battlefield, UAV and unmanned systems, weather, IoT sensors, and more. In the command centre or in the field, our mission-planning solutions help defence operators quickly update and adjust strategies and tactics based on real-time information and conditions. In today’s rapidly changing defence landscape, militaries, systems integrators, and OEMs need mission-critical data available quickly, securely, and all in one place.
With FABRIQ, organisations can deploy applications that fuse and feature imagery, maps, military symbology, terrain models, real-time weather data, live video feeds, moving tracks, target indicators, and more inter-departmentally or even internationally.
ALGOREUS, an ML SPAWNER for DEFENCE CAPABILITY FORCE MULTIPLIER
For mission-critical enterprises such as the ADF, machine learning plays a significant role in modern warfare systems, such as autonomous weapons. Although autonomous weapons have been in existence for more than a century, machine learning acts as a force multiplier to accelerate AI innovation through a data-driven decision precision for an overmatch against an adversary. There is a need for a platform that can bring key decision-makers, operational staff, and data scientists together, offering a seamless yet flexible ML environment. This is where ALGOREUS comes in.
An ML Spawner that lets enterprises: train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity across all chains of command while maintaining model performance in production with confidence. It accelerates time to value with industry-leading machine learning operations, open-source interoperability, and integrated tools. The dependable platform is intended for responsible AI applications in machine learning, with fairness and explainability built in. ALGOREUS includes built-in governance, security, and compliance for operating machine learning workloads anywhere.
For mission-critical enterprises such as the ADF, machine learning plays a significant role in modern warfare systems, such as autonomous weapons. Although autonomous weapons have been in existence for more than a century, machine learning acts as a force multiplier to accelerate AI innovation through a data-driven decision precision for an overmatch against an adversary. There is a need for a platform that can bring key decision makers, operational staff, and data scientists together, offering a seamless yet flexible ML environment. This is where ALGOREUS comes in.
BENEFITS OF ALGOREUS
Train models without code, minimal expertise required
Take advantage of AutoML to build models in less time. Use ALGOREUS with state-of-the-art, pre-trained APIs for computer vision, language, structured data, and conversation.
Accelerate models to production
Data scientists can move faster with purpose-built tools for training, tuning, and deploying ML models. Reduce training time and cost with optimised AI infrastructure. ALGOREUS allows mission-critical enterprises to achieve faster model development, deliver higher quality ML models, and faster deployment and production.
Manage your models with confidence
Remove the complexity of model maintenance with MLOps tooling such as ALGOREUS Pipelines, to streamline running ML pipelines, and ALGOREUS Feature Store to serve, and use AI technologies as ML features. Lastly, to use ALGOREUS Model Interpretability to white-box your model predictions in dashboards so that everyone in your chain of command that makes decisions can use the models with confidence.
It allows for massive scalability and administration by allowing thousands of models to be supervised, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment. ALGOREUS, in particular, enables more tightly-coupled communication across data teams, decreasing conflict with devops and IT, and boosting release velocity.
Machine learning models often need regulatory scrutiny and drift-check, and ALGOREUS enables greater transparency and faster response to such requests and ensures greater compliance with an organisation’s or industry’s policies.
With Ml-assisted operating pictures and integrated alert, automation, and AI capabilities, our solutions do not replace the warfighter but rather augment human strengths. The result is a more capable Defence force, equipped to make better decisions at a faster speed than the adversary.
APPLICATIONS OF ALGOREUS
Our collaborative mission planning environment enables the ADF to simulate competing courses of action; assess costs, opportunities, and tradeoffs; and prepare to take rapid, decisive action when it matters most. ALGOREUS also helps to build enhanced decision-support system for the defence sector, such as intelligent drones, automatic cruise missiles, automatic weapons that take decisions in accordance with suspicious objects. It helps humans in the loop to make a decision by analysing data and predicting the best course of action for them.
The National Security system of any country is one of the most important parts to maintain the security of the whole nation. Hence military/defence systems are most sensitive to cyberattacks, as it can lead to loss of crucial information and can also compromise the National Security. To this end, AI and ML embedded systems powered by ALGOREUS can automatically protect networks, computer programs, and data from any kind of unauthorised access. Further, ML-enabled web security systems can record the pattern of cyberattacks and develop counter-attack tools to tackle them.
Immediate medical aid is not always available on the battlefield. It may be a lack of actual medics in the area, injuries beyond the skills of a medic, or a matter of triage, where medical personnel prioritise the most badly injured. AI in medicine is a well-established field in the commercial sector, but not so much in the defence. An AI-enabled ML system like ALGOREUS can support medics in diagnosing injuries, monitoring patients, and providing treatment plans when immediate evacuation of the patient is not possible. The idea is to provide the medic with decision support when it comes to handling traumatic injury by using an ensemble of classifiers, frequently tuned via machine learning to predict life-threatening and difficult-to-detect injuries. It would present the steps to treat those injuries through a simple, easy-to-use interface that tailors itself to the needs and skill level of the user.
Logistics & Transportation
For each successful ADF operation, it is required to effectively transport essential components such as goods, weapons, ammunition, etc. Embedding ALGOREUS in the transportation system can reduce transportation costs and also human operational efforts. Strategists can leverage our solutions for resilient logistics to assess competing supply chain strategies, plan for disruptions, and redistribute existing resources to where they are needed most.
Target Recognition and Tracking
Machine learning and artificial intelligence are also involved in enhancing the accuracy of target recognition in complex combat environments. These techniques allow defence forces to gain an in-depth understanding of potential operation areas by analysing reports, documents, news feeds, and all forms of unstructured information for situational analysis and a prediction advantage by leveraging the information flowing from FABRIQ into Algoreus.
Defence combat Training
Machine learning enables computers or machines to train the troopers with various combat systems deployed in various operations in warzones. It provides stimulation and training with various software engineering skills that help during a difficult situation. Reinforcement learning in AlGOREUS can help in building a combat training system where they learn by reward and punishment as feedback. This approach becomes more significant in maintaining an enhanced training system for all chains of command where continuous and dedicated training is essential.
Threat monitoring is defined as a network monitoring solution/system, which is dedicated to analysing, evaluating, and monitoring an organisation's network and endpoints to prevent various security measures such as network intrusion, ransomware, and other malware attacks. Machine Learning in ALGOREUS helps in threat detection through various categories such as Configuration, Modeling, Indicator, and Threat Behaviour. Algorithms are trained to run pattern recognition, and detect the malware behaviours or ransomware attacks before it enters the system. AI also plays a vital role in developing an intelligent system for threat awareness, such as drones. These drones are equipped with intelligent software and algorithms that enable them to detect threats, analyse them, and prevent them from entering into an uncharted territory through the Machine Learning advantage.
The ADF protects our country from border attacks by ensuring dedicated patrolling of the country inside and outside the borders. Although soldiers are always positioned to patrol the border, nowadays, various smart sensors and intelligent machines such as drones are playing a crucial role in the border security system. These drones can be equipped with AUTO-ML capabilities of ALGOREUS that would detect, analyse, and inform against any suspicious activity by sending information to edge data centres in real-time.
An integrated machine learning environment where you can build, train, deploy, and analyse your models all in the same application.
An auto ML service that gives people with no coding experience the ability to build models and make predictions with them.
An endpoint option for hosting your ML model. Automatically scales in capacity to serve your endpoint traffic. Removes the need to select instance types or manage scaling policies on an endpoint.
ALGOREUS Recommender Engine
Get recommendations on instance types and configurations (e.g. instance count, container parameters and model optimizations) to use your ML models and workloads.
ALGOREUS Lineage Tracking
Track the lineage of end-to-end machine learning workflows.
ALGOREUS Model Monitor
Monitor and analyse models in production (endpoints) to detect data drift and deviations in model quality.
ALGOREUS Edge Manager
Optimise custom models for edge devices, create and manage fleets and run models with an efficient runtime.
Maximise the long-term reward that an agent receives as a result of its actions.
ALGOREUS Model Registry
Inspect training parameters and data throughout the training process. Automatically detect and alert users to commonly occurring errors such as parameter values getting too large or small in Model Registry. Versioning, artefact, approval workflow, and cross account support for deployment of your machine learning models.
ALGOREUS Feature Store
A centralised store for features and associated metadata so features can be easily discovered and reused. You can create two types of stores, an Online or Offline store. The Online Store can be used for low latency, real-time inference use cases and the Offline Store can be used for training and batch inference.
ALGOREUS Human in the loop (HIL)
Build the workflows required for human review and feedback of ML predictions. ALGOREUS brings operational feedback to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers. It self-learns based on the feedback provided to form better generalised predictions for your next training.
From control centres to battlefields, deploy the comprehensive competency of FABRIQ + ALGOREUS for data supremacy and decision precision.
Bring your models alongside your data using FABRIQ’s native platform integrations.
Import models as code, libraries, or trained model artefacts from ALGOREUS
Access versioning, branching, reproducibility, and lineage capabilities of FABRIQ
Tie your models back to the processes that drive your organisation with the FABRIQ Ontology.
Define a robust foundation for AI-powered end-user workflows, with granular security and governance
Release and inject your models directly into core applications, without adapters or glue-code
Build feature-rich compound applications in hours instead of months
Enrich deployed models with decision data from your organisation’s analysts, operators, and decision-makers.
Facilitate collaboration between AI/ML and operations teams through shared applications
Enable operators to monitor, retrain, and improve your models with real-time feedback
Automatically write decision data back to both the Ontology and corresponding systems of action
Build Models in ALGOREUS with FABRIQ
ALGOREUS implements the complete model lifecycle, spanning problem definition, development of one or more candidate solutions, evaluation of these solutions, deployment, monitoring, and iteration. Modelling Objectives provide the backbone of the model lifecycle for any problem. Core FABRIQ functionality extends the traditional model lifecycle upstream (i.e., data enrichment and management) and downstream (i.e., operationalization and feedback). The combination of end-to-end capability and interoperability means that FABRIQ customers can continue to leverage investments that are already working for them, while using ALGOREUS to augment all machine-learning challenges and provide intelligent decision precision for their enterprise operations.
Enrich and manage data
The fuel for any modeling use case is data. This includes not only data ingested from multiple source systems, but also data derived and captured throughout the model and use case lifecycle — business logic, feature sets, labels, model predictions, instance-level outcomes, end-user actions/decisions, and more. FABRIQ provides the tools to not just integrate data from anywhere, but also transform, enrich, permission, catalog, quality-control, govern, and maintain it. This is accomplished through an entire platform-wide approach such as:
Lineage which spans across datasets, logic, models, and actions to enable build automation, security, transparency, and downstream attribution. It versions data and code consistently, to simulate how logic changes impact downstream features, metrics, and model-driven decisions.
Monitoring of data health, distributions, model feedback, and pipeline health, to enable AI delivery at scale.
Evaluate and manage models
Model evaluation and management capabilities, rooted in ALGOREUS Model Monitor, are critical to streamlining and assuring successful, ongoing operationalization of modelling projects, whether by production pipelines, user-facing applications, or other systems. It serves as a searchable catalog for model candidates, capturing models, versions, and event-specific metadata per-submission. It then enables problem solvers to:
Explore the set of model submissions along various metadata dimensions and model metrics
Define model standards, and set up rails and governance around required reviews from various stakeholders, as well as release and deployment processes
Integrate ALGOREUS’ model inputs and outputs to the FABRIQ Ontology, enabling connectivity with operational applications and event scenarios
Implement a systematic testing and evaluation (T&E) plan via software, leveraging managed metrics
Perform continuous integration and continuous deployment (CI/CD) of models
The ultimate goal of a modelling workflow is often deploying models to users and systems that drive decisions and actions. ALGOREUS provides a variety of options for deploying models directly, as well as tools for seamlessly incorporating model deployments into production use. Models can be deployed into managed batch inference pipelines, interactively-queryable “Live” API endpoints, or even external systems (e.g., enterprise on-prem systems, or edge hardware, or multi-cloud). Pipeline-based batch deployments are suitable for recurring large-scale processing, and benefit from FABRIQ’s data enrichment and management capabilities described earlier, such as versioning, orchestration, health checks, and lineage. Their outputs can be consumed by downstream pipelines and applications or synced to external systems. Deployments are especially powerful when models are bound to the FABRIQ Ontology. Integrations enable user-facing applications to:
Leverage direct and linked properties of input objects, execute Ontology-based scenario analyses using the model, and incorporate the model into broader simulations.
Perform actions that propose or commit real operational changes
Capture actions and feedback as new data via writeback. This provides business and modelling teams a powerful data asset for monitoring, understanding, and improving production performance, as well as identifying and adapting to new circumstances
Collectively, these enable rapid construction and iteration of data-powered workflows and processes that are robust enough for an enterprise's critical path, while closing the loop with model development, evaluation, and management.
“As the battlespace becomes increasingly contested, human-machine teams need to enable operators to collect cognitive intelligence in complex environments, ensuring our warfighters get rich information they need when they need it.”
- Dan Saldi, Founder and AI Developer