Our Solutions reduce community of supply challenges saving 50%

The Internet has put data at our fingertips. Geolocation. Services. Sensors. Lots of it - Big.  This Geo, Open, Big, IoT, Voice (GOBI-Voice) data is changing needs for planning, monitoring, decision making, modeling, predictive, advisors. Compute and storage is enabling this fast and cheap. Fast and Cheap runs counter to making this FAIR - Findable, Accessible, Interoperable, Re-usable . Too many formats, silos, rules, volume. Our Solutions shift workforce away from manual and time-consuming data collection to higher value analysis, modeling, and decision support. We make and connect data ready for knowledge use.

We are technology Agnostic. We maximize existing investment while designing to new components and architectures

As a way to leapfrog clients, our clients take advantage of our leading advancements in data platform progression at the highest levels in all industries. Combined with some investments at xentity in cloud native serverless data aggregation, integration solutions, exploring our efforts in Open Source comparted to licensed models - either migration to or improved use of their licenses investments. Our architectures establish more containerization, ITIL and DevOps (some cases DevSecOps) management. Our solution expand clients from traditional data flows into geospatial and open data integration with expansion into big data models, real-time data hub and data lake solutions.

  • Geospatial Data – Web Services, Web Apps, ESRI Silver Partner, Boundless / Open Layers (QGIS), CARTO, OGC, KML, Open GIS, QGIS, GeoJSON, SAFE FME, FOSS4G stacks (GeoNetwork, GeoServer, TerriaJS, Kepler, Leaflet, Terraframe), Metadata Schemas (FGDC, ISO, Project Open Data
  • Open Data - Open Data Catalogs Technologies such as CKAN, Socrata, ODS, and Various Data Pipeline technologies (Cloud Serverless, Informatica, Kettle, NodeJS packages).
  • Big Data – NoSQL, Hadoop, PEGA, Mongo, ElasticSearch, Luciad, BrainSpace, Lakes, Warehouses, 4V’s, Warehouse, Ponds, Puddles, Marts, real-time filtering/processing, and versed in AWS, GCP, and Azure Cloud Platform frameworks
  • All Data –  SEO, Data-Driven Workflow, Openrefine, CSV, Excel, Informatica, NodeJS, SAFE FME, Data wrangling, RDBMS, unstructured, virtual ETL, SAP BO, HTML5/CSS/Javascript, Business Intelligence, Mobile, MVC JS
  • Partnerships - AWS, GCP, ESRI, CARTO, Hexagon


Data helps understand “What happened?”

Whether its raw feeds, simple datafiles, published datasets, logs, content, we provide a broad series of Integration and Interoperable data platform services: Data 


  • Pipelines (ELT, ETL, Serverless, COTS)
  •  Standards-based Transformations (Transformations, Formats, Encoding)
  • Real-Time Data Stream Hubs
  • Data Microservices
  • Data services (WMS, WFS, WCS, REST/XML Web Service)
  • Database Connectors
  • Field Collection Apps
  • Open Data Catalogs Metadata Inventory
  • Data Asset Registry & Search (Transformations, Datasets, Systems, Apps, Web Services, Feature, Layers, Metadata, Field Maps, Containers, algorithms, ontologies, scripts, code repo’s, reports) 

Challenges to Address

  • Data Aggregation, Ingestion and creation solutions lean serverless, highly configurable, ELT over ETL
  • Data Orchestration Services look to in-line perform data standards validation, automate workflows, proactively detect schema and data anomalies, and automate metadata insights
  • Data Management established metadata prioritization and analytics, cataalog, authoritative data management, discovery, and archive approaches.
  • Automate on-going Data integration to support information and knowledge solutions
  • Streamlining processes and data by consolidating systems and technology
  • Wrangling and extracting, transforming and loading (ETL) data to make it more useable and consumable
  • Lower your disproportionate high investment in aging data production and loading systems
  • Address data infrastruct inefficiencies: aging expensive 'iron servers'/hardware, unmanaged or oversize cloud instances, and modernize technologies.
  • Address and Govern Data maturity
  • Integrate governance planning with metadata repositories connecting goals, metrics, use cases, data assets, systems, and technologies to support prioritization and complexity.
  • Maximize full value from sensor investment by making higher access and available to analytics efforts 


Information joins data to analyze complicated questions to historically find out “Why did it happen?”

Information takes data and puts into specific relational or object data models to support very specific policy-driven processes. To support policy & compliance workflow, we support:


  • Process Management (Workflow platforms, Curation, Chaining) 
  • Relational databases
  • Warehouses
  • GIS & warehouse integration
  • ETL MIgration to ELT models
  • GIS supply workflows 
  • Geospatial Platforms 
  • Information Exchange Buses between systems (Electronic Data Interfaces, API to API, Web services, standards transformations)
  • Data Product Generation 
  • Basic reporting & visualization dashboards
  • Mobile applications

Challenges to Address

  • Lower the acquisition cost of data, systems, and technology
  • Establish core, foundational, authoritative, accessible datasets and reference models to integrate thematic and mission datasets consistently across business units.
  • Migrate Iron to Cloud IaaS solutions to reduce technical debt
  • Synchronized disconnected and integrate redundant GIS data workflows
  • Get information management activities back on track with agile approach to configurable application sets on common platforms
  • Tag, scan and make available non-relational data (legacy maps, scanned documents, etc.) in newer content, document and records management platforms.
  • Rapidly develop various using Agile Scrum methods in Web, Mobile, GIS, and Voice (skills)
  • Develop service integration to facilitate knowledge transfer and reuse
  • Engage your community in your change efforts with workflows including Gamification, Incentivization, CrowdSourcing, and Outcome-driven Governance
  • Create enterprise information for integrated change management


Knowledge requires agility to gain more accurate insight into findings to answer “What is or will be happening?” 

...while adding in the information context of Why. Knowledge is in the present, focuses more on monitoring real-time or near real-time for either operational or project-analysis decision support. To understand what is and will happen, solutions provide Decision support, modeling, analytics and monitoring. Solutions integrate STEM data, semantic data and multiple ontologies, temporospatial data versioning solutions,  and support advanced analytics on high-powered cloud or HPC. We provide solutions such as:


  • Search & Discovery
  • Orchestration (MQ, Link, Mediate, Broker, Publish, Syndicate)
  • Intelligent Sensor feed pre-processing
  • Data Warehouses & Beyond (Data Warehouses, Data Marts, Data Lakes)
  • Analytics (Visualization, Estimation, Sensitivity, Modeling, Simulation)
  • Modeling 
  • Data STEM Tools & Workbench solutions (Notebooks, Repositories, Containers). 

Challenges to Address

  • Improve discovery metadata, develop semantic web models, improve interpretative signals, introduce machine learning, Search Signals, increase SEO, catalog development
  • Reduce the large amount of time scientists spend in data engineering and IT setup & access
  • Shift these labor intensive hours to conducting advance decision support analysis and modeling.
  • Reduce access, discovery, and process burden on the customers, users, and citizens
  • Develop solutions collaboratively to encourage partnerships between Xentity and their clients.
  • Establish Performance Dashboards to provide decision support optics
  • Shift Performance reporting to include  trending, predictive, or other suggestive analytics
  • Align large sensor investments with analytics and use efforts


Wisdom seeks to provide the answer or recommended options to “What should we do?”

Most human wisdom is not pure classification sorting, algorithmic logic, or programmatic decisions.  Wisdom lives in a world of “chaotic and fluid and yet to be captured in a way to be used yet” data. Wisdom will require creation of prediction/solution set based on copious amounts of collaboration to truly bring the data together. These solutions will require support data to be built on top of the learned and signal data occurs relies heavily on data science and computer science models. which rely on natural language processing, higher order linked data, fuzzy logic, interpretive signals, artificial intelligence, sentiment analysis, and low-level atomic big data analytics. Without this context, which is both exciting and unsettling in the same moment, wisdom solutions will remain on the horizon in general, and be applicable for niche-specific solutions. While in its early phases the “What should we do about it?” our solutions offer:


  •  AI NLP Gap, Confidence Highlight, and Geospatial Extraction
  • ML classification, clustering, and predictive analytics.
  • Massive streaming aggregations
  • Data Science AI Workbench platforms
  • A/B Signal testing & acceptance/rejection lifecycle
  • Multiple-ontological and linked data solutions
  • Sentiment analysis
  • Low-level atomic big data analytics.

Challenges to Address

  • Create high quality AI/ML training data to address the promise of AI technology solutions - solutions rushed into production will not yield results replacing human interaction.
  • Integrate historical data with near real-time, and real-time data to support more robust and higher fidelity analytics
  • Provide Temporospatial analytics to automatically provide ranging guidance, predictive analysis, or recommendations 
  • Integrate beyond traditional sensor data, foundational data, and thematic data with newer data integration with social, behavioral, incident, or citation/criminal data