Machine Intelligence Division

AI Agent Development &
Custom LLM Solutions

An AI agent is an autonomous system that reasons through multi-step tasks, calls tools and databases, and self-corrects. MobAppAI builds these as secure, sandboxed pipelines for enterprise workflows, alongside RAG, semantic search, and custom model fine-tuning.

Cognitive AI Agents

We develop self-correcting cognitive agents that use structured reasoning loops to evaluate complex tasks, query databases, invoke tool integrations, and synthesize outputs.

Chain-of-Thought ReasoningMulti-Agent OrchestrationsTool Use & Function Calling
Operational MetricACTIVE
Security Level
Encrypted Sandbox Loop
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Semantic Vector Search

Replace obsolete search structures. We train high-performance vector embedding indexes that query massive databases and identify semantic context with sub-second retrieval speeds.

RAG Database PipelinesHybrid Keyword/Vector IndexingMulti-Format Content Scraping
Operational MetricACTIVE
Security Level
Encrypted Sandbox Loop
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Autonomous Background Workflows

We deploy background processes that run continuously on cron networks to scrape market trends, run periodic analysis, automate compliance audits, and route critical events.

Automated Cron ExecutionIntelligent Failover SystemsDense Ingestion Analytics
Operational MetricACTIVE
Security Level
Encrypted Sandbox Loop
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Custom Model Engineering

Maximize performance and minimize token expenses. We specialize in fine-tuning lighter, specialized models (e.g. Llama-3, Qwen) for narrow, high-frequency corporate tasks.

Parameter-Efficient Fine-TuningPrivate Local DeploymentsStructured Output Parsing
Operational MetricACTIVE
Security Level
Encrypted Sandbox Loop
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Explore Our AI Services

Deep dives into the core capabilities we deliver — each built secure, evaluated, and handed over in full.

RAG Implementation
Grounded, cited answers on your own data.
LLM Fine-Tuning
Adapt smaller, cheaper models to your tasks.
Semantic & Vector Search
Search by meaning, not just keywords.
AI Workflow Automation
Agents that take action across your systems.

Related guides: What is RAG?, RAG vs fine-tuning, AI agent vs chatbot.

AI Solutions FAQ

What is RAG (retrieval-augmented generation)?
RAG is a technique where an AI model retrieves relevant information from your own databases or documents before generating an answer, so responses are grounded in your data rather than the model's training alone. It's the foundation of accurate enterprise AI assistants and search.
Should a business fine-tune an LLM or use RAG?
Use RAG when answers must reflect current, changing data. Fine-tune when you need a model to adopt a specific format, tone, or narrow task at lower cost per call. Many enterprise systems combine both.
What is semantic (vector) search?
Semantic search uses vector embeddings to find results by meaning rather than exact keywords, so a query returns relevant content even when the wording differs. It improves search across large, mixed-format databases.