MCP (Model Context Protocol)
Model Context Protocol (MCP) is an open standard created by Anthropic that allows AI models like Claude, ChatGPT, and Gemini to connect to external tools, databases, and APIs through a unified interface. Think of it as USB-C for AI — one protocol that works everywhere.
Before MCP, every AI integration required custom code — separate connectors for each tool, each database, each API. MCP standardizes this with a client-server architecture where AI models act as clients and your tools act as servers. This means you build one MCP server for your product, and it instantly works with Claude, ChatGPT, Cursor, and any MCP-compatible AI. We build custom MCP servers that expose your business logic, databases, and internal tools to AI models securely and efficiently.
RAG (Retrieval-Augmented Generation)
RAG is a technique that enhances AI responses by retrieving relevant information from your own data sources before generating an answer. Instead of relying only on training data, RAG grounds AI outputs in your actual documents, databases, and knowledge bases — reducing hallucinations and increasing accuracy.
A RAG system works in three steps: first, it converts your documents into vector embeddings and stores them in a vector database. When a user asks a question, the system searches for the most relevant chunks of information. Finally, it feeds those chunks to an LLM along with the question, producing an answer grounded in your actual data. This architecture is essential for enterprise AI — it keeps responses accurate, up-to-date, and traceable back to source documents. We build production RAG pipelines with hybrid search, reranking, and citation support.
Vibe Coding
Vibe coding is a development approach where developers use AI tools like Cursor, Lovable, Bolt, or Replit to generate code through natural language prompts rather than writing it manually. Coined by Andrej Karpathy in 2025, it enables rapid prototyping but often produces code that needs professional review before production deployment.
Vibe coding has democratized software development — non-technical founders can now build working prototypes in hours. However, AI-generated code often has hidden issues: security vulnerabilities, poor architecture, no error handling, and technical debt that compounds quickly. The result is apps that work in demos but break in production. We specialize in taking vibe-coded applications and transforming them into production-ready products — fixing architecture, adding proper auth, optimizing performance, and implementing CI/CD pipelines.
AI Agent
An AI agent is a software system powered by large language models that can autonomously plan, decide, and execute multi-step tasks. Unlike chatbots that only respond to questions, AI agents take actions — querying databases, calling APIs, sending emails, and making decisions based on real-time data.
AI agents represent the next evolution beyond chatbots. While a chatbot answers questions, an agent completes tasks. A support agent doesn't just explain your refund policy — it checks the order, processes the refund, sends the confirmation email, and updates the CRM. Agents use tool-calling, memory, and planning capabilities to break complex goals into steps and execute them. We build production AI agents with proper guardrails, human-in-the-loop controls, and monitoring — because autonomous systems need safety rails.
LLM (Large Language Model)
A Large Language Model is a neural network trained on massive text datasets that can understand and generate human-like text. Examples include GPT-4, Claude, Gemini, and Llama. LLMs power chatbots, code generation, content creation, and AI agents.
LLMs work by predicting the next token in a sequence, but at scale this simple mechanism produces remarkably capable systems. Modern LLMs can reason, follow complex instructions, write code, analyze data, and even use tools. Choosing the right LLM for your use case matters — GPT-4 excels at reasoning, Claude at long documents, Gemini at multimodal tasks, and open-source models like Llama offer privacy and cost advantages. We help you select, integrate, and optimize LLMs for your specific business requirements.
Fine-tuning
Fine-tuning is the process of training a pre-existing AI model on your specific data to improve its performance for your use case. Instead of building a model from scratch, you adapt an existing LLM to understand your industry terminology, writing style, or domain knowledge.
Fine-tuning sits between prompt engineering and training from scratch. You take a capable base model and train it on hundreds to thousands of examples from your domain. The result is a model that inherently understands your terminology, follows your formatting preferences, and produces higher-quality outputs for your specific task. Fine-tuning is ideal when prompt engineering hits its limits — when you need consistent style, domain expertise, or reduced latency. We handle the full pipeline: data preparation, training, evaluation, and deployment.
Vector Database
A vector database stores data as mathematical representations (embeddings) that capture semantic meaning, enabling AI systems to find similar content based on meaning rather than exact keyword matches. Essential for RAG systems, semantic search, and recommendation engines. Popular options include Pinecone, Weaviate, ChromaDB, and pgvector.
Traditional databases search by exact matches — you query 'refund policy' and get results containing those exact words. Vector databases understand meaning — searching 'refund policy' also returns results about 'return procedures,' 'money back guarantees,' and 'cancellation terms.' This works by converting text into high-dimensional vectors using embedding models, then using algorithms like HNSW or IVF to find nearest neighbors efficiently. We implement vector databases as part of RAG systems, recommendation engines, and semantic search features.
Prompt Engineering
Prompt engineering is the practice of designing and optimizing the instructions given to AI models to get better, more reliable outputs. It involves crafting system prompts, few-shot examples, chain-of-thought reasoning, and guardrails that guide AI behavior for production applications.
Good prompt engineering is the difference between an AI demo and a production system. It includes techniques like system prompts that define personality and constraints, few-shot examples that demonstrate desired output format, chain-of-thought prompting that improves reasoning, and output validation that ensures responses meet your requirements. In production, prompts become complex engineering artifacts with version control, A/B testing, and monitoring. We build prompt systems that are robust, testable, and maintainable — not fragile one-liners that break with unexpected input.
Agentic AI
Agentic AI refers to AI systems that operate with a degree of autonomy — they can set goals, create plans, use tools, and execute multi-step workflows without constant human supervision. In 2026, agentic AI is moving from experimental to production, with Gartner predicting 40% of enterprise apps will embed AI agents.
Agentic AI is the paradigm shift from 'AI as a tool' to 'AI as a worker.' These systems combine LLMs with planning algorithms, memory systems, and tool access to complete complex workflows autonomously. A marketing agentic system might research competitors, draft content, generate images, schedule posts, and analyze performance — all from a single high-level instruction. The key challenge is reliability and safety — ensuring agents handle edge cases gracefully, escalate when uncertain, and never take irreversible actions without approval. We build agentic systems with proper guardrails and observability.
Supabase
Supabase is an open-source backend-as-a-service platform that provides a PostgreSQL database, authentication, real-time subscriptions, edge functions, and file storage. It's the most popular backend for AI MVPs and vibe-coded applications built with tools like Lovable and Cursor.
Supabase has become the default backend for modern AI applications because it combines everything you need in one platform: a powerful PostgreSQL database with pgvector for AI embeddings, built-in auth with social logins, real-time subscriptions for live updates, edge functions for serverless logic, and storage for files. Its generous free tier and developer-friendly API make it perfect for MVPs, while its PostgreSQL foundation ensures it scales to production. We use Supabase extensively in our AI projects and know how to architect it for performance, security, and scalability.