AIソリューションで変革を

MicrocosmWorks does both depending on the use case. We fine-tune and deploy pre-trained LLMs from OpenAI, Anthropic Claude, and Google Gemini for conversational AI, content generation, and RAG applications. For specialized tasks like computer vision, anomaly detection, or domain-specific NLP, we train custom models using TensorFlow, PyTorch, and AWS SageMaker. Over 150 AI projects inform our model selection process.

MicrocosmWorks builds AI agents using LangChain, AutoGPT, and custom agentic frameworks that enable autonomous reasoning, tool use, and multi-step task execution. For multi-agent systems, we implement orchestration patterns where specialized agents collaborate on complex workflows with shared memory management and tool integration. Our agents integrate with your existing APIs, databases, and business systems.

Yes, RAG implementation is one of our most requested AI services. MicrocosmWorks builds production-grade RAG pipelines using vector databases like Pinecone, Milvus, or FAISS, combined with embedding models from OpenAI or open-source alternatives. We handle document ingestion, chunking strategy, hybrid search with BM25 and vector similarity, and citation-grounded responses that reduce hallucination in enterprise knowledge systems.

MicrocosmWorks builds facial recognition, object detection, OCR, virtual try-on, and automated surveillance systems using deep learning architectures. We have deployed computer vision in healthcare for medical image analysis, retail for shelf monitoring, manufacturing for quality inspection, and security for real-time threat detection. Our solutions run on edge devices, cloud GPUs, or hybrid configurations depending on latency requirements.

An AI proof-of-concept at MicrocosmWorks typically takes 4-8 weeks and costs $10,000-$30,000 at development rates of $20-$50/hr. This covers data assessment, model selection, initial training or fine-tuning, integration with one production system, and a performance benchmark report. We have achieved a 95% accuracy rate across our AI projects, with 3x faster time-to-insight compared to in-house development efforts.