AI that works while you sleep.

We use GPT-4o and o1 (OpenAI), Claude 3.5 Sonnet and Opus (Anthropic), Gemini 1.5 Pro (Google), and Llama 3.1 or Mistral Large for open-source needs. We build AI agents with persistent memory, tool-use capabilities, function calling, and custom business context via RAG. We do not sell generic chatbots that simply repeat FAQ answers.

n8n is an automation platform that can connect APIs and business tools in deterministic or AI-assisted workflows. Hosting, execution limits, credentials, retries, errors, approvals, and ongoing ownership depend on the chosen deployment and scope.

Price depends on the task, data, tools, integrations, security, evaluation, review path, volume, hosting, documentation, and support. A written proposal confirms commercial terms after those inputs are reviewed; ROI and payback are measured rather than promised.

Not always. The required skills depend on the workflow and risk. Training, documentation, operating instructions, and the review interface are defined in the delivery scope.

Security and compliance cannot be guaranteed by a model name. The scope must identify data, providers, regions, retention, training use, subprocessors, access, encryption, logging, deletion, contractual requirements, and the organization's legal basis before architecture is selected.

Yes. Start with a bounded, measurable task and expand only after quality, review effort, operating cost, failure modes, and business value are observed.

A chatbot answers questions (input → output). An AI agent makes decisions and executes actions (plans, uses tools, self-corrects). An n8n workflow is a deterministic flow of steps (if-this-then-that). We combine all three based on your needs: workflows for repetitive tasks, agents for complex decisions, and chatbots as the user interface. We advise you on what you actually need before proposing a solution.

RAG (Retrieval Augmented Generation) is the pattern used to let an AI access your specific information without retraining the model. We index your knowledge base (catalogs, manuals, policies, customer data) in a vector database (Qdrant, Pinecone, pgvector), and the AI queries it in real-time before responding. This is the correct solution when you need the AI to know your business without "hallucinating," avoiding the high cost of fine-tuning.

Yes. We implement AI agents within the WhatsApp Business Platform (Meta Cloud API) that handle inquiries, qualify leads, schedule appointments, and escalate to a human when necessary. These include conversation memory, CRM integration, and personalized responses based on contact history, fully compliant with Meta policies.

Before starting, define a baseline and specific measures such as accepted output, review time, error and escalation rates, cycle time, operating cost, or attributable business outcomes. The review cadence and stop or expand decision are part of the scope.