Tell us about your project

AI agents take things a step further - they don’t just analyze data, they act on it. We explore AI agents for automating communication, support, and analytics. These systems can learn, adapt, and add entirely new value to your processes. At Fermicoding, we see AI agents as teammates for humans, not replacements. They extend your team’s capabilities, giving you more time and focus for strategy and creativity.

Model fine tuning

Model fine tuning

We don’t just deploy pre-trained AI models — we fine-tune them to fit your specific business logic, domain vocabulary, and workflow nuances. Whether it's adapting open-source models like LLaMA or DeepSeek to your internal knowledge base, or optimizing lightweight models for cost-efficient inference, we make sure the output isn't just smart — it's relevant. From structured prompt engineering to full adapter-based tuning, we align the model behavior with your real-world needs.

Agent implementation

Agent implementation

We design and implement AI agents that integrate seamlessly with your existing systems. Whether built through n8n or developed from the ground up in Python, PHP, or Node.js, each solution is tailored to fit your business processes. Our agents go beyond simple responses — they retain context and learn over time through persistent memory powered by databases such as Redis, Supabase, or SQL. The result is an intelligent assistant that adapts, evolves, and continues delivering value long after deployment.

MCP (Model Context Protocol)

MCP (Model Context Protocol)

We implement MCP as part of every serious AI agent we build. It’s how the agent keeps track of context — knowing what it’s doing, why it’s doing it, and when to switch focus. Instead of losing track mid-task, the agent carries context across interactions, passing it to the model automatically, not manually. The result is consistency, accuracy, and conversations that actually make sense. We don’t do disconnected replies — we do structured context.

Agentic RAG (Retreival Augmented Generation)

Agentic RAG (Retreival Augmented Generation)

We don’t fine-tune models just to have them go stale next month. Instead of locking knowledge into static weights, we implement Agentic RAG — where the agent dynamically retrieves the latest data, thinks through the task, and acts accordingly. New documents? Updated policies? No room for hallucinations? No problem. The agent knows how to fetch what it needs when it needs it. You stay current without retraining every time your content changes. That’s how real-time AI should work.

Private hosting

Private hosting

We handle privately hosted agents, tailored to your setup — not tied to someone else’s cloud. The agent runs where you need it, with the performance and privacy you expect, without burning your budget. No hidden platform fees. No lock-in. Just a system that works — fast, lean, and under your control.