Successful artificial intelligence development depends on innovation and customizing the exact machine learning approach that addresses your unique challenge. Our roadmap to success is our methodology.



We start by digging deep into your challenge and subject matter. We will always seek to understand before we tell you what you need.

Artificial intelligence and machine learning are tools to help you achieve your goals. Before a CoVar engineer picks the right tool for your situation, we have to understand your problem. We study it in-depth because that’s the only way we can succeed together.

We start by asking questions—many questions—about your data, sensors, deployment strategies, error costs, key metrics, and end-users. We’ll sign an NDA so we can play with your data and do a quick trade-study and data analysis iteration. This helps us formulate a plan.

Then we generate requirements, a detailed statement of work, a timeline, and a budget.

Understanding Your Business

Machine learning without context is short-sighted and dangerous. Expect our engineers to become domain experts in your subject matter so that the final product is cutting-edge and ready to be used in the field—what we call mission-grade.

Our engineers have become world experts in subject areas ranging from wellbore management and kick detection to vehicle classification in IR imagery, clinical use of health databases, and more.



We will improve the value of your data and help you understand your world.

Machine learning can find the hidden gems in your data that lead to better decision-making. But as good as our algorithms are, they depend on quality data. That is why a key component of our engagement is data quality assurance and data understanding.

  • 01.

    Before we apply any machine learning, we dig into your data with precision to find edge-cases, correct erroneous data labels, understand anomalies, and ensure that our machine learning processes are not using bad information.

  • 02.

    Once the data is clean, we can begin to understand it. We use custom visualization and analytics tools to identify trends and clusters in your data and determine underlying causal factors.

  • 03.

    No one can find every piece of bad data, but our process will identify data problems that would derail any artificial intelligence or machine learning before it starts.

  • 04. Generating High-Quality Data Labels

    If your data isn't labeled, we will help you determine the truth about your data.

  • 05.

    When labeled data is required, the quality of the labels is as important as the quality of the data. Our labeling team is trained in aided target recognition (AiTR) and image analysis. We label object bounding boxes and segmentations carefully and consistently.

  • 06.

    Our labelers are fast and efficient because we use powerful, configurable open-source data labeling tools, hosted in a secure environment.

  • 07.

    After labels are generated, we quality control every image to ensure that data labels are correct and verified before they become part of our training process. When training data is limited, we use physics-based models to augment available data with synthetic training data.

  • 08.

    Our robust data labeling processes have made us the de-facto ground-truth managers for large-scale, multi-contractor and multi-University projects. We are the contractor Army AiTR researchers trust when they do not have time to label their data. We have built one of the world's largest collections of labeled imagery from oil and gas drilling rigs, as well as the largest body of labeled AiTR data outside the U.S. Army.



When it is time to develop an algorithm, we customize our pre-processing, metrics, and machine learning algorithms to get every ounce of performance from your data.

We have been going that extra mile for decades. Twenty years ago, anyone could download a support vector machine, but we were first to deploy the first generation of AiTR to detect landmines for the Army. Today, anyone can download detection algorithms like YOLO, but only our machine learning algorithms and implementations are robust enough to win blind tests and get deployed to real-world harsh environments.

While other companies chase the hottest 100-million-parameter black box, we design artificial intelligence and machine learning solutions that are right-sized to your data and real-time requirements. We use state-of-the-art models and build custom pre-processing and artificial intelligence and machine learning architectures.

Precision and recall are not relevant to most of our customers. Instead of forcing you to re-interpret your problem using off-the-shelf statistical terms, we will develop metrics that focus on what matters to you—which means solving your problem and being able to describe system performance to your users in meaningful terms.



We use industry-best practices so that the performance we report is the performance you can expect.

We use stringent cross-validation approaches to ensure that our algorithms are learning something real and will stand the test of time.

Our performance estimates are robust because we work hard to ensure our training is thorough, our datasets are balanced, and our test sets are representative.



We understand that our technology will become part of your team’s workflow, so we design a great user experience into our algorithm development.

Successful artificial intelligence and machine learning systems build trust with end-users by defining clear criteria for their decisions and operating in a consistent, explainable manner. Since we have taken the time to understand your problem domain, we can build algorithms that are explainable in terms your end-user understands and trusts.

For example, when we deployed smart kick detection to oil and gas rigs, we built a system that works with drillers’ understanding of the wellbore, not against it.

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Once our scientists find the best solution for your problem, our engineers use real-time implementation and system integration to bring our algorithms to life—in the cloud, on the edge, or in the actual sky.

CoVar cares about your long-term success. We provide complete engineering solutions and stay with you from basic algorithm research through large-scale system deployment.

Our software engineers use our proprietary CONAN architecture to easily and reliably transition from proof-of-concept to deployed code.

Aided Target Recognition
Our advanced database search engine is deployed in a massively parallel web-service architecture. Our computer vision technologies have been deployed to edge-computing platforms on off-shore oil rigs. Our AiTR systems are implemented on low-power NVIDIA boards, and on-board small unmanned aerial systems. Peter Torrione // Chief Technical Officer, CoVar


Successful software is not static. Users will always want more performance, new capabilities, and a better user experience. We will continue to support your algorithms and make modifications after your system is deployed. Our software management process lets us make improvements to your deployed system while maintaining performance on historical data.

Interested? Take a deeper dive into UAS.