The AI development market has exploded since GPT-4 became publicly accessible. Today virtually every software agency claims AI capabilities — but the difference between an agency that wraps an API call in a chatbot widget and one that can build production-grade ML systems, RAG pipelines, and custom-trained models is enormous. Choosing the wrong partner costs time, money, and competitive advantage.
Two Types of AI Development
- LLM-powered applications: RAG chatbots, document processing, and workflow automation using OpenAI, Anthropic, or Google APIs
- Custom ML systems: Training models on proprietary data for classification, prediction, computer vision, or NLP requiring specialized performance
Red Flags When Evaluating AI Partners
Be cautious of agencies that cannot explain model selection rationale, have no examples of custom-trained models, cannot articulate how they handle hallucination, have no MLOps monitoring infrastructure, or over-promise accuracy benchmarks without validation datasets.
Key Questions to Ask Any AI Agency
- 1What is your process for deciding whether ML is actually the right solution vs. simpler rule-based logic?
- 2How do you validate model accuracy before production deployment?
- 3What does your MLOps infrastructure look like for monitoring model drift?
- 4Can you share examples of models you have trained on client-specific data?
- 5How do you handle data privacy and compliance for sensitive training datasets?
How Hubmicrooo Approaches AI Projects
Our AI practice starts with a data assessment — evaluating data volume, quality, and readiness before recommending an approach. We build LLM applications using RAG architecture for knowledge-intensive use cases and custom ML models when domain-specific accuracy requires it. Every production AI system includes a monitoring dashboard to track performance and catch drift.
Discuss Your AI Project With Our Team
Get in TouchFrequently Asked Questions
How much does custom AI development cost?
LLM-powered applications (RAG chatbots, document automation) typically range from $8,000 to $30,000. Custom ML model training with data pipelines and MLOps infrastructure ranges from $25,000 to $150,000+ depending on data complexity and integration requirements.
Do I need a large dataset to build an AI solution?
Not always. For LLM-based RAG applications, excellent results are achievable with a few hundred documents. For training custom ML classification models, you typically need a few thousand labeled examples minimum. We assess your data situation during a free discovery session.
What AI frameworks and models do you use?
We work with OpenAI (GPT-4o), Anthropic (Claude), and Google (Gemini), as well as open-source models (Llama, Mistral). For custom ML, we use Python, TensorFlow, and PyTorch. Our MLOps stack includes AWS SageMaker, MLflow, and FastAPI for model serving.
How do you ensure AI outputs are accurate and safe?
We implement confidence thresholds, output filtering, human-in-the-loop workflows for high-stakes decisions, and continuous production monitoring. For LLM applications, we use prompt hardening, guardrails, and structured output schemas to minimize hallucinations.