Engineering intelligence into business systems
Artificial intelligence offers opportunities to improve information processing, automate selected tasks and support data-informed decisions. LOG APPS approaches AI as an engineering discipline requiring careful attention to use cases, data, model limitations, integration, reliability and human oversight.
01 | AI Model Development & Customization
Evaluate suitable algorithms and models for defined tasks.
Use-case feasibility and model selection.
Data preparation, customization and evaluation.
Accuracy, reliability and operational assessment.
02 | Large Language Models & NLP
Build applications that work with natural language and institutional content.
Enterprise assistants and question-answering tools.
Arabic and English text processing, classification and extraction.
Semantic search, summarization and controlled content generation.
03 | Retrieval-Augmented Generation (RAG)
Connect language models to curated enterprise knowledge.
Knowledge repositories and document retrieval.
Embeddings, vector databases and source-linked answers.
Evaluation of retrieval quality and answer relevance.
04 | Intelligent Document Processing
Extract value from unstructured records.
Automatic classification and information extraction.
Entity recognition, document summarization and intelligent archive search.
05 | Data Analytics & Predictive Models
Identify patterns and support analytical decisions.
Operational data analysis and performance indicators.
Classification, prediction and anomaly detection.
Model evaluation and limitations.
06 | Intelligent Process Automation
Combine business rules and intelligent processing.
Request and correspondence classification.
Data extraction, routing and event-driven workflows.
Integration with operational applications.
07 | Enterprise AI Integration
Connect intelligent capabilities with existing systems.
APIs, data flows and permission controls.
Operational logging, versioning and performance.
Cost, scalability and human review requirements.
08 | AI Governance, Safety & Quality
Apply controls proportionate to the sensitivity of the use case.
Data quality, output accuracy and privacy.
Human oversight and monitoring after deployment.
Documented limitations and risk management.
AI is a practical capability to improve enterprise systems, not an end in itself.