Actionable insights are the results of data analytics procedures, extracted to make informed decisions.
Oxtractor extracts actionable insights by proposing an aspect-extraction focused generative AI & NLP approach.
Multi-modal Retrieval Augmented Generation
Understanding information beyond text.
Traditional retrieval-augmented generation (RAG) systems focus mostly on text, often missing critical insights hidden in tables, charts, figures, and multi-page documents. At Oxtractor, we extend RAG into the multi-modal space, combining visual and textual evidence to deliver more accurate, context-aware answers.
Our approach builds on the latest research in document question answering and multi-modal reasoning. Instead of flattening everything into plain text, we preserve visual information (like graphs or slide layouts) and integrate it alongside written content. This allows our systems to:
Analyse long and complex documents across multiple formats.
Fuse visual and textual evidence for more faithful answers.
Reduce errors and hallucinations by grounding responses in retrieved facts.
The result is a robust multi-modal RAG framework that supports decision-making in sectors such as education, healthcare, finance, retail, and hospitality — wherever insights are scattered across text and visuals.
Agentic AI and Tooling
Autonomous agents that learn, reason, and act.
Generative AI is moving beyond conversation into agentic systems — AI agents that can perform tasks, interact with tools, and evolve over time. At Oxtractor, we design agentic AI tailored for complex domains such as healthcare, education, and finance.
Our approach focuses on three core innovations:
Memory-Augmented Agents: Agents that learn from past interactions, improving their accuracy in diagnosing patients, tutoring students, or advising clients over time.
GUI-Based Workflow Simulation: Agents capable of navigating real-world digital environments (e.g. hospital systems, school platforms, banking apps) to perform tasks such as triage, course planning, or credit applications.
Advanced Reasoning Architectures: Prompting strategies and tool integrations that enable agents to handle complex, multi-step reasoning — whether in clinical decision-making, classroom problem-solving, or financial planning.
By embedding these capabilities into safe, feedback-rich environments, we create agents that are not only autonomous but also reliable, testable, and scalable. This positions Oxtractor at the forefront of agentic AI, building the foundations for next-generation decision-support systems across multiple industries.
Long-Term Memory Approaches
Making AI systems remember, adapt, and stay consistent.
Large language models are powerful, but they often struggle with long-term memory — forgetting earlier interactions, losing context, or mixing information across tasks. At Oxtractor, we design advanced memory architectures that allow AI agents to stay coherent, accurate, and adaptive over extended use.
Our research-driven approach introduces:
Hybrid Memory Systems: Combining semantic (dense) and keyword-based (sparse) retrieval to improve relevance and reduce information loss.
Smart Reranking: Using additional ranking layers to ensure only the most relevant context is passed into the reasoning process.
Task-Specific Isolation: Keeping memory separated by user, task, or session to prevent cross-contamination and maintain precision.
Rigorous Evaluation: Benchmarking against long-term datasets to measure factual accuracy, consistency, and temporal grounding.
The result is a scalable, reliable memory framework that enables AI agents to handle complex, ongoing interactions — whether supporting clinicians over multiple patient visits, assisting teachers across a semester, or tracking financial strategies over months.
Conversational AI
Emotionally intelligent, adaptive, and human-centred dialogue systems.
Conversational AI has advanced far beyond simple chatbots. At Oxtractor, we focus on creating emotionally intelligent agents that can adapt, respond, and engage in human-like interactions across education, healthcare, finance, retail, and hospitality.
Our research-driven approach explores how large language models (LLMs), combined with reinforcement and imitation learning, enable agents to:
Simulate personality and memory, ensuring consistent, engaging conversations.
Adapt to dynamic inputs, adjusting tone and content for different users and contexts.
Collaborate in teams, balancing task objectives with empathy and cooperation.
Operate in open, real-world environments, interpreting cues and responding flexibly to social dynamics.
These systems go beyond answering questions — they are designed to support, guide, and interact responsibly, whether as virtual assistants for patients, tutors for students, advisors in finance, or customer service agents in hospitality.
The result is a new generation of conversational AI that is adaptive, ethically aligned, and capable of building trust in complex human–AI interactions.
Aspect Extraction for Insights
text > aspects > insights
Structured Aspect Extraction
Deep text understanding is a key problem with the increasing amount of produced textual social data. Extracting such a structure of principal entity, related entities and aspects automatically has previously been deemed too challenging.
The extraction of aspects - target entities, their aspects and values - from social data streaming through digital channels is crucial for a better semantic representation. There is no NLP-based AI approach that automatically determines the aspects to extract and is capable of recognizing hierarchical aspect structures other than Oxtractor.
Deep Learning
for NLP
Deep Learning for Natural Language Processing
Given a set of product reviews or descriptions, word embeddings trained with the traditional deep learning models do not explicitly capture the domain relatedness of a token in a review just as they do not capture the sentiment information of the tokens in the reviews explicitly. However, for particular NLP tasks such as sentiment classification or aspect term extraction, it might be crucial to capture more than the syntactic contexts of the words.
For the tasks of aspect term extraction or opinion target extraction, predicting the domain-relatedness distribution of text based on input ngram is the primary solution to integrating domain information into word embeddings.
Big Data Analytics
Real-time
Real-time Analytics
We apply logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data.
