IArgos is a prototype of an AI-assisted investigation workspace designed to help analysts and investigators organize, categorize, search, and prioritize large collections of images based on an investigation context.
The project explores how multimodal AI (vision + language) can augment human decision-making in sensitive analytical workflows, rather than replace it.
⚠️ This repository is a research / prototype project. It is not production-ready and makes explicit design trade-offs for clarity, experimentation, and explainability.
In many investigative or analytical contexts (law enforcement, journalism, compliance, intelligence, auditing), analysts face:
- Hundreds of heterogeneous images
- Weak or missing metadata
- High cognitive load when deciding what matters
- Manual, error-prone triage processes
IArgos proposes a structured workflow where AI assists with:
- First-pass categorization
- Context-aware relevance scoring
- Visual exploration and filtering
while keeping the human fully in control of final judgment.
- The user defines an investigation context (free-text description).
- All downstream scoring and prioritization explicitly reference this context.
- Automatic image captioning and visual question answering
- Extraction of descriptive text and semantic tags
- Each image is assigned to one or more high-level semantic categories (e.g. people, vehicles, documents, weapons, indoor/outdoor, etc.)
- Explicit conflict resolution logic (e.g. indoor vs outdoor)
- Transparent rule-based post-processing on top of model outputs
- Textual search over generated descriptions
- Synonym-aware and multilingual-friendly queries
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Custom relevance score (0–100) combining:
- Detected categories
- Richness of description
- Semantic overlap with investigation context
- Exact and partial keyword matches
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Images classified into three operational buckets:
- 🟢 Relevant
- 🟡 To review
- 🔴 Likely irrelevant
- Multi-page Gradio interface
- Clickable statistics and filters
- Progressive disclosure: overview → drill-down
This project intentionally avoids an end-to-end opaque model.
Design choices emphasize:
- Interpretability over raw accuracy
- Deterministic scoring logic layered on top of AI outputs
- Explicit heuristics that can be inspected, modified, or debated
This makes IArgos suitable as:
- A research prototype
- A discussion artifact with domain experts
- A foundation for more robust, audited systems
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Language: Python
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UI: Gradio (multi-page Blocks)
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Vision-Language Model: BLIP (captioning + VQA)
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Architecture:
- Stateful session-based analysis
- No persistence (in-memory only)
- Modular scoring and categorization pipeline
It is:
- A serious exploration of AI-assisted analytical workflows
- A demonstration of multimodal reasoning pipelines
- A portfolio project showcasing system-level thinking
It is not:
- A production-grade forensic tool
- A claim of automated decision-making
- A benchmark-optimized ML system
- Heuristic-based scoring (not learned end-to-end)
- No long-term storage or audit logging
- Performance not optimized for large-scale datasets
- Model bias and error propagation not formally evaluated
These limitations are explicit and intentional at this stage.
- Replace heuristic scoring with learned ranking models
- Add embedding-based semantic similarity (instead of string matching)
- Introduce active learning from analyst feedback
- Formal evaluation on real investigative datasets
- Stronger security, logging, and access controls
This repository is part of a broader effort to explore:
- Human-centered AI
- AI as a decision-support system
- Practical multimodal pipelines beyond demos
It is shared for discussion, critique, and learning, not as a finished product.
MIT License
If you are a researcher or practitioner interested in investigative AI, explainable systems, or applied multimodal pipelines, this project is meant to be read, questioned, and challenged.