Real-Time, RF Agentic Intelligence
Developed jointly by DataRobot and Deepwave, and powered by the latest NVIDIA AI for Enterprise, the Radio Intelligence Agent (RIA) solution transforms raw RF signals into conversational intelligence, with the entire lifecycle orchestrated by the trusted, integrated control plane of the DataRobot Agent Workforce Platform.
Autonomous Intelligence for RF
Artificial Intelligence is advancing from simple analytical tools into autonomous agents capable of reasoning, planning, and acting, an essential shift for applications such as Radio Frequency (RF) intelligence.
Today, security agencies must analyze massive streams of RF data in real-time. The challenges are formidable:
- Dynamic RF signals demand rapid updates to analysis methods.
- Edge operations in emergency environments make manual analysis too slow.
- High data complexity and volume overwhelm existing platforms.
Introducing the Radio Intelligence Agent
The Radio Intelligence Agent is an autonomous system that translates raw RF signals into proactive, conversational intelligence. It doesn’t simply retrieve information— it interprets, reasons, and recommends.
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- Situational Awareness: Fusion of multiple data streams into analyst-ready insights.
- Recommended Actions: Real-time analysis of signals with recommended next steps to support responses.
- Adaptability: Actionable, secure analysis for a range of missions. Solution can be developed locally and deployed seamlessly to private clouds or FedRAMP-authorized infrastructure.
How RIA Works
The Radio Intelligence Agent begins by using Deepwave’s high-performance edge processing platform to intercept and process radio frequency signals. This delivers deep signal processing, speech recognition, and speech-to-text conversion all at the edge – cutting network data backhaul by a factor of 10 million. Deepwave’s AirStack Edge then reports this data to the DataRobot platform.
The DataRobot Agent Workforce Platform, co-developed with NVIDIA, provides the RIA with an integrated control plane that orchestrates the entire agentic lifecycle and is fully deployable in air-gapped, on-premises, and high-security deployments. This enables agencies and organizations to build, operate, and govern custom-tailored agentic AI solutions where the data resides, while maintaining full control and visibility across every layer of the stack. All data processing and communication between components occurs within the agency’s security perimeter to guarantee complete data sovereignty.
The agent is powered by vetted and tested components, including hardened NVIDIA Inference Microservices, which are built from a trusted, STIG-ready base layer for FedRAMP compliance. The agent’s core intelligence is powered by NVIDIA Nemotron models, which provide high-level reasoning capabilities. The Streaming Data to RAG blueprint enables the GPU-accelerated pipeline to continuously capture, transcribe, and index RF signals, unlocking real-time situational awareness.
Finally, the agent uses Deepwave’s orchestration to actively recollect new RF intelligence by running new models, recording signals, or broadcasting signals.
RIA’s Enabeling Technologies
Imagine receiving complex RF Intelligence, trusted, high-fidelity, and actionable, in seconds, simply by asking a question. DataRobot, Deepwave, and NVIDIA have strategically partnered to make this capability a reality.
First, Deepwave’s AIR-T edge platform receives and analyzes the RF signals using AirStack software, powered by embedded NVIDIA GPUs. The newest AirStack component, AirStack Edge, adds a secure API with FIPS-grade encryption, allowing signal processing applications and NVIDIA Riva Speech and translation AI models to be deployed directly on AIR-T devices. This end-to-end process runs securely and in real time, delivering extracted AI-generated data into the agentic workflows orchestrated by DataRobot.
The solution’s agentic capability is rooted in a two-part system that leverages NVIDIA Nemotron models to interpret context and generate sophisticated responses.
- Query Interpreter: This component is responsible for understanding the user’s initial intent, translating the natural language question into a defined information need.
- Information Retriever: This agent executes the necessary searches, retrieves relevant transcript chunks, and synthesizes the final, cohesive answer by connecting diverse data points and applying reasoning to the retrieved text.
This functionality is delivered through a high-performance Retrieval-Augmented Generation (RAG) architecture. The autonomous agent first consults a vector database, which stores semantic embeddings of transcribed audio and sensor metadata, to find the most relevant information before generating a coherent response. The AIR-T generated metadata is fully customizable and contains critical signal intelligence, including the frequency, location, and reception time of the collected data.
Critical to the solution’s real-time capability is the NVIDIA Streaming Data to RAG Blueprint. This architecture enables the workflow to move from simple data lookup to autonomous, proactive intelligence. The GPU-accelerated software-defined radio (SDR) pipeline continuously captures, transcribes, and indexes RF signals in real-time, unlocking continuous situational awareness.
RIA Leverages Agentic Tools
Behind RIA’s autonomy lies a powerful suite of agentic tools — specialized systems that give the platform the ability to reason, act, and adapt without human intervention. These tools orchestrate complex RF workflows, perform rapid data analysis, and transform raw signals into actionable intelligence in real time. Together, they form the cognitive backbone of RIA, enabling it to continuously learn from evolving signal environments and make proactive, mission-critical decisions with speed and precision.
The solution is equipped with several specialized tools that enable this advanced workflow:
A Collaborative Approach to Mission-Critical AI
Bringing together Deepwave’s edge RF intelligence, DataRobot’s lifecycle management, and NVIDIA’s accelerated foundation, this solution empowers agencies to act with speed, precision, and confidence. It enables the shift from reactive monitoring to proactive intelligence—delivering mission-ready AI at the edge.
“The Radio Intelligence Agent is a new category of capabilities for special operations teams. Developed in collaboration with Deepwave and NVIDIA, this full-stack AI-native solution analyzes RF data in real time, allows analysts to engage through a voice-enabled assistant, and delivers actionable insights that drive mission success.”
– Jay Schuren, DataRobot Chief Revenue Officer
Take the Next Step
Learn more about how the Radio Intelligence Agent can help advance your agency’s AI ambitions. Contact Deepwave today
Contact Deepwave Today
Frequently Asked Questions
What unique competitive advantage does the RIA provide over traditional RF Intelligence platforms?
The RIA solution delivers autonomous, proactive RF intelligence rather than merely retrieving data, representing a fundamental shift from reactive monitoring to strategic action. The system integrates real-time processing from Deepwave’s high-performance edge platform with the agentic orchestration of the DataRobot platform, powered by NVIDIA’s accelerated computing. This architecture enables multi-data stream fusion into analyst-ready insights and proactively recommends immediate actions, significantly accelerating decision timelines and operational tempo, particularly in dynamic or contested environments.
Does the RIA solution require cloud connectivity, or does it support on-premises and air gapped deployments?
The Radio Intelligence Agent is explicitly engineered for deployment flexibility to meet high-security mission requirements. The system is designed to run entirely on-premises, within your agency’s security perimeter. The integrated DataRobot Agent Workforce Platform, which orchestrates the entire agentic lifecycle, is fully deployable in air gapped environments. This ensures all data processing, communication, and intelligence generation occur locally, guaranteeing complete data sovereignty and maintaining security compliance without relying on external cloud services.
How does the RIA platform ensure low-latency performance and overcome data processing bottlenecks?
Deepwave addresses the core challenge of data volume and latency by performing deep signal processing at the edge using our high-performance platform, incorporating NVIDIA GPUs. This unique architecture cuts network data backhaul by a factor of 10 million by converting raw RF signals into structured data and conversational intelligence directly where the data is collected. Furthermore, the entire signal processing and RAG pipeline is GPU-accelerated using the NVIDIA Streaming Data to RAG blueprint, ensuring continuous, real-time situational awareness and minimizing delays inherent in traditional data transfer models.
What technical components enable RIA's conversational intelligence capabilities?
RIA’s sophisticated conversational intelligence is rooted in a highly performant Retrieval-Augmented Generation (RAG) architecture. This architecture utilizes Deepwave’s AIR-T platform for real-time demodulation, speech recognition, and generating rich, customizable metadata (including frequency, location, and reception time). The core intelligence, powered by NVIDIA Nemotron models, employs a two-part system: the Query Interpreter understands user intent, and the Information Retriever executes searches against a vector database containing semantic embeddings to synthesize accurate, context-driven responses.
How is security and compliance addressed?
Security is built into every layer: FIPS‑mode encryption, STIG‑ready NVIDIA NIM™ microservices, controlled APIs at the edge, and a complete audit trail across ingestion, retrieval, and agent actions. The platform’s integrated governance lets agencies enforce policy while scaling model deployment and monitoring.
What measurable benefits should we expect, and how can test drive it?
RIA shifts from bulk backhaul to edge extraction, reducing network load by up to 10,000,000× while speeding time‑to‑answer for mission queries. We typically begin with a proof‑of‑concept that includes: hardware/software spec for one system, a model‑development pathway, connected vs. air‑gapped CONOPS, and three pricing configurations (A/B/C). Contact us for details.