🤖 Agentic AI

This is one of the most popular programming languages for Machine Learning and Artificial Intelligence. It comes with a vast variety of libraries, considerably speeding up development time. We put it to great use while developing your backend. Inside the toolbox: Flask, FastApi, Django

Docker containerization technology ensures consistent deployment across all environments. We use Docker to package applications with all dependencies, making deployment faster, more reliable, and scalable.

Distributed version control system

GPT-4, GPT-4o, and o-series models power our intelligent chatbots with advanced reasoning, function calling, and multimodal capabilities. We integrate OpenAI APIs for natural conversation, code generation, and complex task automation.

LangChain & LangGraph provide the orchestration layer for building complex LLM applications. We use chains, agents, tools, and memory to create sophisticated AI workflows that go beyond simple Q&A — from RAG pipelines to multi-step autonomous agents.

Pinecone, Weaviate, ChromaDB, and Milvus power our semantic search and RAG systems. Vector databases store embeddings for fast similarity search, enabling chatbots to retrieve relevant context from your knowledge base in milliseconds.

RAG combines retrieval from your documents with LLM generation for accurate, grounded responses. We build end-to-end RAG pipelines — chunking, embedding, indexing, retrieval, and generation — so your chatbot answers from YOUR data, not hallucinations.

CrewAI, AutoGen, and custom agent architectures enable multi-agent collaboration for complex tasks. Our agents plan, use tools, delegate sub-tasks, and verify results — turning LLMs from chatbots into autonomous digital workers that get things done.

OpenAI text-embedding-3, Cohere Embed, and open-source models like BGE and E5 convert text into dense vector representations. High-quality embeddings are the foundation of semantic search, clustering, and RAG accuracy — we select and fine-tune the right model for your domain.

Nous Research's Hermes Agent is a state-of-the-art autonomous AI agent framework. It combines tool-calling, memory across sessions, multi-provider model routing, and skill-based workflows. We deploy Hermes for 24/7 autonomous operations — from code review and deployment to research and customer-facing agents.

OpenCode is a terminal-native AI coding agent that reads your entire codebase, plans changes, edits files, and verifies results — all from the command line. We use OpenCode for autonomous PR generation, refactoring, and code review, turning hours of manual work into minutes of agent-driven execution.

OpenClaw is an open-source framework for building autonomous AI agents with persistent state, scheduled tasks, and multi-channel communication. We leverage OpenClaw for production agent deployments that run continuously — monitoring feeds, processing data, and responding to triggers across Slack, Telegram, email, and webhooks.

Anthropic's Model Context Protocol is the standard for connecting AI agents to external tools and data sources. We build MCP servers that give agents secure access to databases, APIs, file systems, and business applications — turning LLMs from isolated chatbots into fully-integrated digital workers that operate your real tools.

Anthropic's Claude Code is a powerful AI coding agent that understands entire codebases, performs complex refactors, and generates production-quality code. Integrated with MCP for tool access, we deploy Claude Code for autonomous feature development, bug fixing, and codebase migration projects.

OpenAI's Codex CLI is a terminal-based coding agent that uses GPT models to understand requirements, write code, run tests, and iterate autonomously. We use Codex for rapid prototyping, test generation, and as part of multi-agent workflows where specialized agents collaborate on complex software projects.