Company
Portfolio Data
Back to Company SearchMACHINA COGNITA TECHNOLOGIES, INC.
UEI: MP37XZUHS7C3
Number of Employees: 23
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2020
11
Phase I Awards
6
Phase II Awards
54.55%
Conversion Rate
$1,730,989
Phase I Dollars
$10,987,052
Phase II Dollars
$12,718,041
Total Awarded
Awards
Immersive Multi-modal Fusion for Operational Real-time Capability Evaluation (IMFORCE)
Amount: $139,979 Topic: N252-103
Naval warfighters operating in contested maritime environments must make rapid, high-stakes decisions based on an overwhelming volume of sensor and intelligence data. These decisions often involve coordinating forces across multiple domains including sea, air, land, space, and cyber, while responding to dynamic threats such as missile strikes, unmanned systems, and adversarial assets. However, the current approach to situational awareness and mission planning relies heavily on manual analysis by multidisciplinary teams, who must sift through disparate datasets to interpret adversary movements, assess vulnerabilities, and develop response strategies.This time-consuming, human-centric process is vulnerable to error, cognitive bias, and missed indicators, resulting in delayed or suboptimal decision-making. The challenge is magnified by the complexity of modern naval operations, where joint planning, red/blue force simulation, and real-time mission rehearsal are essential for maintaining an advantage. Todayís tools lack the immersive realism, automated content generation, and intelligent adversary modeling needed to support these critical workflows. Operators are left with static visuals, siloed data feeds, and limited simulation capability, making it difficult to explore "what-if" scenarios, validate engagement plans, or rehearse responses in a collaborative setting. Despite advances in virtual environments and AI, there remains no unified system that can fuse multi-source intelligence into high-fidelity, adaptive 4D simulations with traceable logic and real-time user interaction. Without an intelligent, immersive platform to support operational foresight and dynamic mission rehearsal, naval teams continue to face elevated risk, coordination breakdowns, and diminished decision superiority.To address the urgent need for rapid, accurate, and collaborative decision-making in contested maritime environments, Machina Cognita Technologies Inc. (MCT), in partnership with TRACE, proposes the Immersive Multi-modal Fusion for Operational Real-time Capability Evaluation (IMFORCE) system. IMFORCE is designed to transform raw multi-source intelligence into immersive, AI-generated 4D operational environments that enhance situational awareness, scenario rehearsal, and strategic planning. Leveraging generative mesh algorithms and large language models (LLMs), IMFORCE enables users to generate complex naval engagement scenarios from simple natural language prompts. These scenarios ranging from adversarial submarine positioning in underwater tunnels to coastal missile strikes, are rendered in real-time through advanced text-to-3D capabilities and enriched by retrieval-augmented generation pipelines that ground decisions in doctrinal data. IMFORCE not only supports dynamic simulation of red/blue force strategies using AI, but also incorporates bias detection and explainability metrics to ensure transparent, data-driven planning.Ý
Tagged as:
SBIR
Phase I
2026
DOW
NAVY
Natural-language Ontologies for battlespace Visualizations and Analytics (NOVA)
Amount: $1,749,941 Topic: N254-D08
Modern warfare demands rapid, data-driven decision-making to maintain a tactical advantage over near-peer and peer threats. However, current command and control (C2) systems rely on outdated technology that does not fully leverage rich data available from sources throughout the theater of operations. This lack of advanced data processing hampers warfighters' ability to assess threats, predict enemy movements, and respond effectively in dynamic combat environments. The Maritime Tactical Command and Control (MTC2) system is being developed as the Navy’s next-generation C2 platform. MTC2 aims to replace legacy planning tools, introduce advanced decision aids, and enhance situational awareness through a Common Operational Picture (COP). A key requirement for MTC2 is the ability to process and represent data in a way that quickly creates understanding, moving beyond rigid schemas to a flexible, human- and machine-readable format. For effective battlefield decision support, MTC2 must integrate artificial intelligence (AI), machine learning (ML), and graph-based analytics to enable near-real-time platform and weapons suitability determination. Additionally, the system must support low-code/no-code data analytics, allowing warfighters to customize applications without extensive programming knowledge. By evolving data representation and enhancing speed of analysis, MTC2 will support the kill chain process, ensuring faster threat predictive capabilities and greater operational effectiveness in theater. To meet the above challenges and provide the desired capabilities, Machina Cognita Technologies (MCT) proposes Natural-language Ontologies for battlespace Visualizations and Analytics (NOVA) System, shown above in Figure 1. NOVA is an end-to-end data management solution for operational-level battlespace command and control (C2) decision support. Domain-specific Ontologies will provide universal schema-less human and machine-understandable data representation across all C2 data sources. A large language model (LLM)-enabled data translation pipeline will dynamically express multimodal data and rapidly retrieve C2 information. Finally, the NOVA data management system powers follow-on COP microservices, no/low code analytics, and a dedicated client application to reduce kill chain analysis, reduce warfighter workload, and increase situational awareness. Overall, the NOVA system will streamline multi-modal data representation across battlespace COP sources, enhance warfighter situational awareness, and accelerate kill chain analytics.
Tagged as:
SBIR
Phase II
2026
DOW
NAVY
Automated LLM-based Intelligence Compilation and Evaluation (ALICE)
Amount: $139,939 Topic: N252-109
Military mission success is directly impacted by the quality and recency of the intelligence supporting the decision-making process. Currently, the Marine Corps Planning Process (MCPP) approach to Intelligence Preparation of the Battlespace (IPB) resulting in the generation and vetting of information for a daily intelligence report is time-consuming, manpower intensive, and requires significant cognitive load. The IPB process requires information to be collected and correlated from a wide variety of locations and modalities to generate the content required for the IPBĺs wide range of topics including Defining the Operational Environment, Effects on Operations, Friendly Capabilities, Evaluation of the Adversary, Adversarial Course of Action Analysis, Information Requirements (IR/PIR/SIR) development, and Effective Exploitation (e.g. Cueing, Redundancy, Mix, Integration, and/or Coordination). A system that was capable of automatically finding the best sources, extracting key information, correlating the information across data sources and modalities, and providing rapid intuitive access to the derived knowledge in a way that enables the end consumer to understand the reliability of the information would be of tremendous benefit to the military. Therefore, the Machina Cognita Technologies (MCT) Team including Covan Group and Serco propose to develop the Automated LLM-based Intelligence Compilation and Evaluation (ALICE) system. The ALICE system will ingest, parse, tag, analyze, and correlate the available data sources and make them easily accessible via a REST API.
Tagged as:
SBIR
Phase I
2026
DOW
NAVY
Marine - eLearning Design and Guidance Engine (Marine-EDGE)
Amount: $139,944 Topic: N252-112
US Marine Corps (USMC) training and education (T&E) programs are critical to ensure mission readiness for executing expeditionary operations. Current T&E programs are not adequately preparing the USMC for the future operating environment. Many current T&E programs rely on outdated programs of instruction (POIs) that use static slides, written exams, and minimal experiential learning. Recent advancement of generative artificial intelligence (GenAI) technologies provide an opportunity to transform current USMC T&E systems, making it easier to design and implement targeted course materials, develop more effective courses, and accelerate student learning. The USMC seeks to modernize its T&E by integrating AI-enabled tools that support instructional system design, content generation, and legacy content conversion. However, the current instructional landscape presents critical structural, technical, and cultural challenges. POI curriculum design and approval is manual and time consuming. Legacy course content is composed of outdated static materials and minimal experiential learning. Course content generation is complex and challenging to tailor to student and instructor needs at scale.To meet the above challenges and provide the desired capabilities, Machina Cognita Technologies (MCT) and Cognitive Performance Group (CPG) propose the Marine – eLearning Design and Guidance Engine (Marine-EDGE). Marine-EDGE is a USMC instructional design and content generation assistant that supports curriculum developers, POI managers, and instructors throughout course planning and with real-time content delivery. It provides guidance at each stage of the instructional design process to ensure alignment with training objectives, learning science principles, and operational needs. A natural language processing (NLP) knowledge graph (KG) will be constructed and learn representations of exemplary course design through CPG’s deep expertise in human-centered design (HCD) approaches and cognitive science. A fine-tuned Mixture of Experts (MoE) large language model (LLM) with graph-retrieval augmented generation (RAG) will be trained to rapidly generate targeted course and curriculum guidance as a design assistant to POI course directors. A curriculum design pipeline, supported by a plug-and-play LLM architecture, will accelerate USMC POI development and approval through interactive curriculum design and expert course curation advice. Finally, a content recommendation pipeline will facilitate rapid prototyping of updated interactive content for both course creation and legacy content conversion. Across all POI development workflows, personnel will interact with GenAI assistance through an intuitive graphic user interface (GUI) that supports user selected templates, drag-and-drop content, and conversational chatbot messaging. Altogether, the system will modernize USMC eLearning, increase Marine schoolhouse production, and improve operational mission readiness.
Tagged as:
SBIR
Phase I
2026
DOW
NAVY
Automated Battle Rhythm Assistant (ABRA)
Amount: $139,944 Topic: N252-097
Machina Cognita Technologies (MCT) proposes the Automated Battle Rhythm Assistant (ABRA), a Large Language Model (LLM)-based solution for automating and optimizing battle rhythm management for USW-DSS operators. ABRA will incorporate an array of task-specific modules. First, a Cognitive Battle Rhythm Management Interface will leverage dynamic, context-aware checklists. Secondly, a Task Flow Complexity Manager will employ graph network analysis to prioritize tasks based on live data inputs and dependencies, optimizing workflow efficiency. And finally, as an option-period extension, an LLM-based Briefing Generator will automate the creation of concise, accurate reports integrating contextual metadata and visualized data. Upon operator initiation or automatic anomaly detection, ABRA will dynamically adjust task prioritization, presenting operators with the most critical information for analysis. ABRA will then generate tailored checklists, integrate relevant data feeds, and, if activated, produce automated briefing materials adhering to pre-defined templates and incorporating visual aids. ABRA will ultimately ease the cognitive burden on USW-DSS operators, accelerate decision-making, and improve overall system performance in complex undersea warfare scenarios. ABRA will offer multiple direct benefits to users, including:Time and cost savings through a significant reduction in the time personnel spend creating briefings and performing data analysis, allowing them to focus on critical tasks.Enhanced situational awareness through automated data fusion, task prioritization, and the integration of contextual metadata.Improved decision-making through streamlined workflows, reduced cognitive load, and the provision of consistent, accurate information.The technical work of ABRA will develop new key capabilities. First, ABRA will offer automated task prioritization via the Task Flow Complexity Manager. This module will employ graph-based analysis to identify critical paths and dynamically adjust task sequencing based on real-time data and operator input. Secondly, the LLM-based Briefing Generator will integrate consolidated sensor data and contextual metadata into automatically generated reports, ensuring adherence to standardized formats and consistency in measurements. This agile reporting capability enhances explainability by providing clear, concise summaries of complex data. Thirdly, ABRA will offer dynamic checklist generation, allowing for flexible and customized task management. And finally, the system will leverage ML techniques to build and refine anomaly detection models, enabling proactive identification of potential threats and enhancing overall situational awareness.Through the innovative use of LLMs, graph-based analysis, and automated data processing, ABRA will result in more efficient battle rhythm management, reduced need for manual intervention, and improved operational effectiveness for navy surface platforms.
Tagged as:
SBIR
Phase I
2025
DOW
NAVY
General Report Automation for Problem and Hazard Observation in Systems (GRAPHOS)
Amount: $139,894 Topic: N251-025
Machina Cognita Technologies (MCT) proposes General Report Automation for Problem and Hazard Observations in Systems (GRAPHOS), a Large Language Model (LLM)-based solution for the automated generation of enhanced, standardized problem reports for Aegis Combat System (ACS) in navy surface platforms. GRAPHOS will incorporate an array of task-specific LLMs. First, a Document-Standards Engine will interpret and enforce document structures based on existing, high-quality report examples. Secondly, a Report-Synthesis Engine will integrate analytic insights and contextual metadata into generated reports that adhere to identified document standards regarding content and format. And finally, as an option-period extension, a Machine-Learning (ML)-Configuration Engine will build pipelines for training models to detect anomalies in live ACS data streams automatically. Upon operator-triggered or automatic anomaly detection, GRAPHOS will solicit problem summaries from operators, identify correlated cases from a centralized database describing prior anomalies, and apply the generated document standards for problem report structure. GRAPHOS will then output and store standardized reports to facilitate holistic reports analysis. GRAPHOS will ultimately ease the reporting burden for personnel and accelerate ACS testing and development, thereby improving system performance in navy surface platforms. The technical work of GRAPHOS will develop new key capabilities. First, GRAPHOS will offer automated creation and enforcement of document standards via the Document-Standards Engine. This engine will use a finetuned Document-Standards LLM to understand operator objectives, select relevant examples from previous problem reports, and generate explainable report-generation instructions and template rules. This approach fully automates the creation and application of document standards for various types of reports. Secondly, the GRAPHOS Report-Synthesis Engine will integrate consolidated sensor data and metadata into LLM-generated reports, ensuring adherence to document standards and consistency in measurements. This agile problem-reporting capability enhances explainability by incorporating contextual information from data analysis results. Thirdly, GRAPHOS will offer holistic analysis of underlying trends and patterns across levels and sites. And finally, the GRAPHOS ML-Configuration Engine will develop an LLM for configuring and executing ML training pipelines to build anomaly-detection models for ACS data streams. By automating the selection of model types, feature extraction methods, hyperparameters, and other critical choices, this approach significantly reduces manual effort while providing explanations for each generated design specification.
Tagged as:
SBIR
Phase I
2025
DOW
NAVY
State-based Machine Aided Real Time Strategy (SMARTS)
Amount: $2,999,942 Topic: N201-077
Military operations require fast, decisive, and accurate decision making to accomplish missions with optimal performance and minimization of exposure to risk. Military leaders are forced to make these decisions in high-pressure situations with changing circumstances, incomplete information, very short time frames, and minimal margin for error. Advancements in Artificial Intelligence (AI), specifically Reinforcement Learning (RL) and Deep Learning (DL), are enabling computers to accomplish tasks under similar conditions by recognizing patterns across massive data streams. However, the lack of transparency and explainability of AI systems has made it unfeasible for these decision makers to put lives at risk based on black-box algorithms. In addition, the integration of AI systems into existing military operations presents challenges in how military personnel communicate with autonomous systems. There is a direct need for a system that can understand both the underlying machinations of autonomous systems and the doctrine-based communication patterns of military operations. To solve these shortcomings, the Machina Cognita Technologies (MCT) team is developing the State-based Machine Aided Real Time Strategy (SMARTS) system and the SMARTS Translation Engine. The SMARTS engine provides users with the ability to analyze an array of potential sequences of actions (or decision tracks), the risks associated with each of these actions, and the required capabilities and effectiveness for units to execute the actions. The SMARTS Translation Engine allows autonomous systems and humans to communicate without requiring military personnel to modify existing processes, procedures, and training. In particular, the SMARTS Translation Engine will enable the two-way conversion between military doctrine-based and formatted communication and machine-understandable messages and control. Machina Cognita Technologies (MCT), in partnership with Covan Group and Unitary Labs, proposes to enhance the SMARTS system’s of military doctrine and operations. To accomplish this goal, the MCT team will improve the SMARTS system’s Natural Language Processing (NLP) pipeline and Semantic Reasoning capability built upon Bidirectional Encoder Representations from Transformer (BERT) models, develop an Unstated Knowledge Model including support for speaker personality/military role models, and generate a Socio-Pragmatic Knowledge Repository based on military operations. Through these components, the SMARTS system will have the added functionality of extrapolating unstated tasks, conditions, and criteria based on human-generated operations and communications, ensuring that the translations are fully described and based upon military doctrine and governing documentation.
Tagged as:
SBIR
Phase II
2024
DOW
NAVY
Spatial-Temporal Agent-based Motion Prediction and Evasion Decision Engine (STAMPEDE)
Amount: $1,249,886 Topic: AF221-0013
The Personnel Recovery (PR) mission is vital to effectively plan and conduct military operations in overseas theaters. The military effort to prepare for and successfully execute the recovery of isolated personnel (IPs) is essential to maintaining force readiness, denying enemy critical intelligence, and protecting the lives of U.S. service members. The USAF is often a leader among services in integrating new PR resources and adopting new innovative technologies. Cutting-edge developments in Machine Learning (ML) and Artificial Intelligence (AI) have created an opportunity to advance current PR planning resources and operational PR support products. Deep Learning (DL) approaches are highly effective at identifying temporal and spatial patterns in geospatial data. Reinforcement Learning (RL) can discover successful behaviors and decisions in realistic military simulations. If harnessed effectively, AI and ML technologies can equip PR planners, coordinators, and IPs with a significant technological advantage over their adversaries. However, new technologies are often tough to deploy at scale. Broad-based adoption is difficult across large military and government organizations. Human behavior is challenging to model. To meet these challenges, Machina Cognita Technologies (MCT) proposes the Spatial-Temporal Agent-based Motion Prediction and Evasion Decision Engine (STAMPEDE). STAMPEDE will be designed to provide users at each level of the joint PR C2 architecture with guidance through an enhanced suite of DL-powered planning and coordination aids. The STAMPEDE system will ingest a wide array of data sources that characterize PR scenarios. STAMPEDE will be compatible with LandSAR mobility model plugin data and incorporate new dynamic data sources. STAMPEDE will return IP location probability maps at sequenced time intervals back to decision support tools. Accompanied by easily interpreted explanations, recommended evasion decisions that avoid capture will be output directly to PR planners and coordinators. STAMPEDE will enable PR planners and coordinators to identify exclusion zones that reduce SAR search areas and drive more efficient searches through spatial-temporal human motion pattern discovery. STAMPEDE’s IP evasion movement recommendations will guide AOR evasion plans of action (EPAs) and theater wide PR guidance using ML-powered prediction and recommendation tools. STAMPEDE will generate customized evasion planning and decision guidance tailored to specific threat scenarios, real-world geographically bounded locations, and individual IP mobility behaviors and circumstances. STAMPEDE’s evasion decision explanations will instill PR user trust in and drive greater PR user adoption of the system using intuitive ML explainability models. STAMPEDE will empower PR users to reduce operational costs and increase the likelihood of successful evasion, survival and recovery through ML model location predictions and evasion decisions recommendations.
Tagged as:
SBIR
Phase II
2024
DOW
USAF
Autonomous Vehicle Audio Translation for Airfield Readiness (AVATAR)
Amount: $139,969 Topic: N231-025
Machina Cognita Technologies (MCT) proposes the Autonomous Vehicle Audio Translation for Airfield Readiness (AVATAR) system for facilitating communications between airfield control authorities and Foreign Object Debris (FOD) removal vehicles. The system will provide a full conversational loop consisting of human-to-machine statement translation and a corresponding machine-to-human message- generation system. AVATAR will monitor continuous airfield radio signals and identify discrete speech segments despite challenges typical of airfield communications, including overlapping speakers, a variety of accents, and background noise. We will parse, classify, and filter the statements, relaying only those containing Air Traffic Control (ATC) instructions and questions for the relevant FOD-removal vehicles. When responding to ATC or negotiating permission for airfield navigation, the FOD-removal vehicle will have the ability to speak through AVATAR’s statement-generation module. Machine-generated messages will follow all airfield rules and regulations. Statements will be optimized for succinctness and directness to ensure a safe operating environment. The streamlined system will allow ATC to communicate with FOD-removal vehicles without unnecessary delay. In contrast to popular dialogue systems, AVATAR will maintain informational accuracy across conversations. The system will create a repository of situational information, including representations of ongoing movements of other airfield entities, histories of previous ATC statements, and details from daily operations plans. Relevant context from the repository will bolster the human-machine communication loop so that every ATC statement is understood accurately and every FOD-generated message is unambiguous. The system will incorporate geographic coordinates to provide spatial context for each translated statement and assist the FOD robot in averting potential collisions and airfield hazards. Each component of AVATAR will support an open interface with system users to promote compatibility. The system will provide processed data in any format necessary to ensure efficient communication. AVATAR-enabled FOD removal will increase airfield readiness and provide a much safer environment for naval aviation operations.
Tagged as:
SBIR
Phase I
2023
DOW
NAVY
Statistical Characterization and Operation Readiness assessment of ELINT/SIGINT Deep learning (SCORED)
Amount: $1,237,464 Topic: AF221-0022
The application of advances in Machine Learning and in particular Deep Learning (DL) to complex RF applications provides a great opportunity to advance DoD system capabilities. However, the black box nature of DL models requires additional advances in Exp
Tagged as:
SBIR
Phase II
2023
DOW
USAF