Modern military operations consume information from multiple domains, satellite imagery, RF/waveform intercepts, human speech/audio, live video feeds, SQL/ NoSQL/graph databases, and streaming sensor APIs. These sources evolve rapidly and rarely share formats, schemas, or protocols, creating friction at the exact moment warfighters need decisive, real-time insight. The CADIMI system resolves both the interoperability problem and the cross-domain fusion problem by combining Retrieval-Augmented Generation (RAG), a vector knowledge repository, and a layered team of modality-specific Domain Expert AIs, a MAD Critic AI, and an Expert Recommender AI that together produce a single, vetted, actionable response.
System overview aligned to the new architecture
1) Diverse Sources of Information
CADIMI ingests and/or queries: military and HQ databases (SQL/NoSQL/graph), situational awareness systems, satellite communications, ships/aircraft radar, Link-16 and other GEO/ISR feeds, audio/radio waveforms, human speech, video streams, and raw sensor APIs/JSON. Inputs may arrive as files, streams, or API responses.
2) AI-Based Data Processing Module orchestrates four automated capabilities after a Primary Categorization of Data into Video / Audio / Image / Text:
- Format & Interface Identification
- Classifies input types: JSON/CSV/XML, SQL/Graph, Satellite Image, Sensor data, Audio waves, Human speech, Video frames.
- Uses metadata, file extensions, filename/URL patterns, header inspection, and light content sampling to recognize structure.
- Infers expected fields, datatypes, units, coordinate frames, crypto/encryption expectations, and transport/protocol traits.
- Data Handling & Transformation
- Converts formats (e.g., CSV→JSON, XML→structured DB rows; image tiling/normalization; audio resampling/featureization; video frame extraction).
- Maps fields/columns across systems; performs unit and CRS (coordinate reference system) normalization; executes cleaning and noise filtering.
- Extracts modality-appropriate features (e.g., spectrograms for audio/waves, embeddings for speech/text, radiometric correction for satellite imagery).
- AI / LLM / ML Model Selection
- Selects the best model per modality/task (e.g., Satellite Image Expert, Audio Waves Expert, Human Speech Expert, Video Feed Expert, Encryption/Protocol Expert).
- Attaches task-specific prompts, decoding constraints, and post-processing rules; justifies model choice based on input type and mission goal.
- Dynamic API & Protocol Adaptation
- Builds call structures and parsing logic for current endpoints; detects and adapts to version/field changes.
- Defines execution flow: Call API → Parse/Extract → Convert/Normalize → Dispatch to Expert AI.
- Maintains adapters for communication protocols (e.g., Link-16 message mapping) and data services.
3) Vector Database of Knowledge Powered by RAG
CADIMI stores rules, adapters, schemas, prompts, and processing playbooks as embeddings (plus text chunks and metadata). A reranker improves precision, while ANN with HNSW ensures fast retrieval at scale.
4) Natural-language request and retrieval.
A warfighter request is embedded and used to sequentially retrieve—via RAG—the exact knowledge needed to execute the mission query, with ANN+HNSW providing efficient, high-recall context. The retrieval order is:
- Format & Interface Identification knowledge — how to recognize the incoming data’s type/structure and required security/crypto.
- Data Handling & Transformation rules — conversion steps, mappings, normalization, feature extraction for the detected modality.
- Model Selection guidance — which Domain Expert AIs to activate, prompts/parameters, and success criteria.
- Dynamic API & Protocol Adaptation — endpoints, authentication, request/response schemas, parse trees, and step-by-step execution.
This assembled context becomes a structured execution plan.
5) Domain Expert AIs execute in parallel.
Based on the plan, CADIMI fans out to the relevant Domain Expert AIs—e.g., Satellite Image Expert AI, Audio Waves Expert AI, Human Speech Expert AI, Video Feed Expert AI—each producing an initial intel response grounded in its modality and the retrieved rules.
6) MAD Critic AI performs adjudication.
The MAD (Multi-Agent Debate) Critic AI evaluates the experts’ outputs, flags low-confidence or compromised results, and runs a short, targeted debate loop to resolve contradictions and close gaps. The goal is to elevate agreement and evidence quality across modalities before anything reaches the operator.
7) Expert Recommender AI synthesizes the final product.
After the critic stage, the Expert Recommender AI composes a single, refined intel response that fuses consistent signals across image/audio/speech/video/text sources, preserving traceability to inputs and transformation steps.
8) Uncompromised and Refined Response
The final output is returned to the Warfighter Information System in a fixed, schema-consistent format so it can slot directly into existing C2/ISR tools without manual re-work.