INNOVIM’s capabilities span the full data lifecycle, from collection to actionable insight. Our scientists and engineers design and operate sensor-based systems on Earth and in space, manage secure, scalable data infrastructure, and apply advanced analytics and decision-support technologies to turn complex data into mission-ready intelligence.
Data Engineering, Science & Data Analytics
End-to-End Data Lifecycle Engineering: Architect secure, automated pipelines to ingest, transform, validate, and publish structured and unstructured data from diverse sources—including EO sensors, telemetry, and field systems—enabling near-real-time operational insight.
AI-Augmented Scientific Analytics: Deploy AI/ML models using frameworks like TensorFlow, PyTorch, and Scikit-learn to extract insights from high-dimensional mission data. Integrate physics-informed models and ensemble learning to support complex environmental and geospatial analysis.
Cloud-Native, Container-Orchestrated Architectures: Build elastic, portable data environments using Kubernetes and Docker for orchestrating scalable, fault-tolerant AI/ML and data pipelines across hybrid and multi-cloud platforms (e.g., AWS GovCloud, Azure IL5, OpenShift).
MLOps for Model Lifecycle Management: Operationalize machine learning with CI/CD for models using tools like Kubeflow, MLflow, and Argo Workflows—supporting reproducibility, automated retraining, and secure model versioning across mission workloads.
Real-Time, High-Throughput Data Processing: Engineer distributed compute pipelines with Apache Spark, Dask, and Ray, alongside real-time stream processing frameworks like Kafka, Flink, and Pulsar to handle petabyte-scale data at mission tempo.
Advanced Visualization & Immersive UI/UX: Develop interactive dashboards (Plotly, Grafana), custom web apps (React, D3.js), and 3D geospatial tools (CesiumJS, Kepler.gl) to visualize scientific and operational data in intuitive, mission-relevant formats.
Federated Data Governance & Zero Trust Compliance: Implement policy-driven access controls, audit trails, and metadata tagging across Kubernetes-managed data environments. Ensure alignment with Zero Trust principles, CUI protections, and federal data mandates (FISMA, OMB M-21-31, DoD CIO Zero Trust Strategy).
Software and Systems Engineering
Secure Agile DevSecOps at Scale: Integrate security throughout the CI/CD pipeline using infrastructure-as-code, automated scanning, and container orchestration tools to ensure rapid, secure deployments.
Cyber-Resilient Systems & Continuous ATO: Design systems for RMF compliance with automated evidence generation, continuous monitoring, and machine-readable ATO frameworks to support rapid mission launches.
Modular Open Systems Architecture (MOSA): Engineer interoperable, plug-and-play components aligned to DoD’s MOSA principles, enabling flexibility, reuse, and lifecycle affordability.
Human-Centered Product Development: Co-develop systems with mission users through stakeholder-driven design sprints, usability prototyping, and feedback loops to ensure adoption and operational fit.
Automated Test & Assurance Frameworks: Deploy end-to-end automated testing (unit, integration, regression, security) to deliver quality at speed while reducing manual overhead and risk.
Tailored Application & Integration Solutions: Develop, integrate, and sustain complex mission applications that span COTS/GOTS platforms, open-source tools, and legacy-to-modern cloud transitions.
Enterprise Mission-Critical Information Systems
Enterprise-Grade Systems Engineering: Design and sustain high-assurance enterprise systems for mission-critical workloads—supporting continuous ops, classified data processing, and performance SLAs.
Resilient, Adaptive Infrastructure: Engineer infrastructure that is fault-tolerant, self-healing, and cloud-agnostic, supporting failover, autoscaling, and multi-region availability zones.
Hybrid Cloud Modernization & Migration: Modernize legacy systems into containerized microservices and migrate mission workloads to secure FedRAMP-authorized cloud platforms (e.g., AWS GovCloud, Azure IL5).
Advanced Database Engineering: Optimize mission-critical databases for latency, scale, and security—supporting NoSQL, time-series, geospatial, and streaming data workloads.
HPC & Edge Computing Enablement: Enable high-performance modeling and low-latency field analytics through scalable HPC clusters, GPU acceleration, and tactical edge compute deployments.
API-Centric, Microservices Architecture: Build service-oriented systems using REST/gRPC APIs and container orchestration (e.g., Kubernetes) for interoperability, flexibility, and future-proofing.
Science and Weather Applications
Full-Spectrum Earth Data Stewardship: Manage the lifecycle of Earth observation data from sensor calibration and ingestion to archive, fusion, and dissemination—supporting scientific integrity and mission reproducibility.
AI-Enhanced Weather & Climate Modeling: Advance traditional numerical models with AI/ML surrogates, data assimilation algorithms, and bias correction models to improve speed and accuracy.
Extreme Weather Event Forecasting: Deploy probabilistic analytics and impact-based prediction tools to assess the likelihood and severity of extreme weather events, supporting readiness and resilience.
Integrated Forecast Systems: Develop interoperable, end-to-end environmental forecasting systems that fuse satellite, airborne, and ground-based data to support cross-agency situational awareness.
Climate Mission Science Leadership: Provide mission support for climate and cryosphere programs (e.g., ICESat-2, JPSS) with domain-specific science operations and system design.
Environmental Risk & Policy Support: Deliver data-driven policy advisories and risk assessments to inform infrastructure planning, defense logistics, and climate adaptation strategies.
Who We Serve
INNOVIM serves US government agencies including the Department of Defense (DoD), National Oceanic and Atmospheric Administration (NOAA), and National Aeronautics and Space Administration (NASA).
