01
Support that does not scale with volume
Answering repetitive questions, qualifying leads and collecting information via WhatsApp or chat consumes team time that could be spent on strategic tasks.
HomeAI & Automation
Solution 03 · Generative AI, agents and process automation
AI agents, RAG and LLM automation for operations that scale without growing the team.
We build autonomous agents, RAG systems over internal documents and automations with n8n and LLMs that eliminate manual work, qualify leads and respond to clients 24/7.
LLM
OpenAI, Claude and Gemini
We select the right model for each use case — GPT-4o, Claude 3.5, Gemini 1.5 or local models like Llama and Mistral.
RAG
Proprietary knowledge base
The agent accesses documents, contracts and internal bases via embeddings without leaking data to the external model.
Agent
Agents that execute real actions
Beyond answering, the agent executes real actions 24/7 — typical results include 3-6x ROI in 12 months and continuous operation without variable headcount cost.
Business problem
Slow support, repetitive processes and knowledge locked in documents drain the team without generating results.
Growing companies reach a point where hiring more people for repetitive tasks does not scale. Generative AI and autonomous agents solve this with precision and predictable cost.
02
Knowledge locked in documents without fast access
Contracts, manuals, policies and FAQs exist, but the team wastes time searching. RAG transforms documents into a base queryable in natural language.
03
Manual processes that could be automated with AI
Email triage, data extraction from PDFs and report generation are still done manually in many companies — and can be eliminated with LLM + n8n.
70%+
Of queries resolved automatically without human intervention
−65%
Reduction in document base search time with RAG
4-8h
Saved per employee per week on document tasks
3-6x
Average ROI on AI automation in the first 12 months
What we deliver
Agents, RAG and automations that work 24/7 with your business intelligence.
We build AI systems that integrate with the real operation: support agents, RAG pipelines over proprietary documents and agentic flows that make decisions and execute actions without human intervention.
AI agents with LLM, memory and action execution
We develop agents with access to tools (APIs, databases, email, WhatsApp), conversation memory, decision logic and the ability to escalate to humans. We use OpenAI (GPT-4o), Anthropic (Claude 3.5) or local models based on context.
RAG and process automation with n8n
We implement RAG pipelines with LangChain or LlamaIndex over internal documents, integrated with n8n flows that orchestrate LLMs, webhooks, CRMs and external systems to eliminate manual processes.
Use cases
Where generative AI and automation create immediate impact.
Intelligent support agent
LLM-powered chatbot that understands context, accesses customer history, responds accurately and executes real actions — WhatsApp, website chat or internal channel.
Agentic AI — autonomous agents
Agents that receive objectives and execute multiple steps autonomously: research, query APIs, make decisions and deliver results without human intervention at each step.
RAG over documents and internal bases
Natural language queries over contracts, manuals, policies and proprietary knowledge bases — without leaking data to the external model. Powered by LangChain and LlamaIndex.
Process automation with n8n + LLM
Flows combining triggers (email, form, CRM), LLM processing (triage, classification, extraction) and automatic actions in downstream systems.
Data extraction and analysis with AI
Structured data extraction from PDFs, invoices and contracts. Automatic generation of reports, summaries and insights with language models.
Autonomous commercial agent
Automatic lead qualification via WhatsApp or form, context-based intelligent follow-up and escalation to a human sales rep at the right moment.
Example of support agent monitoring panel — conversations, detected intents and actions executed in real time.
Project image coming soon
How we implement
From process diagnosis to production agent with measurable results.
Step 01
Process diagnosis and use case identification
We identify which problem has the highest ROI for AI automation: support, documents, internal processes or data analysis.
Step 02
Agent architecture and model selection
We define the stack: LLM (OpenAI, Claude, Gemini or local), framework (LangChain, LlamaIndex), orchestrator (n8n) and required integrations.
Step 03
Development, testing and fine-tuning
We build the agent in short cycles, test with real data, adjust prompts and tools until the expected result is achieved.
Step 04
Deploy, monitoring and evolution
We deploy to production with audit logs, response quality monitoring and an evolution plan for new capabilities.
Stack and technical scope
Models, frameworks and orchestration oriented to business outcomes.
We select each layer based on the use case: latency, cost, data privacy and integration capability with existing systems.
- LLMs: OpenAI (GPT-4o), Anthropic (Claude 3.5 Sonnet), Google (Gemini 1.5) and local models (Llama 3, Mistral)
- RAG: LangChain and LlamaIndex with vector stores — pgvector, Pinecone and Chroma
- Agentic AI: agents with tools, persistent memory and multi-step action execution
- Automation: n8n for flow orchestration, webhooks, CRMs and external systems
- Integrations: WhatsApp (official API), Slack, email, REST APIs and databases
- Security: tenant isolation, no unnecessary PII sending and audit logs
- Monitoring: conversation tracking, failure detection and continuous feedback loop
What makes the approach different
We do not deliver a chatbot. We deliver an agent that works alongside your business.
The difference between an AI agent that works and one that creates frustration lies in tool architecture, prompt quality, context management and alignment with the real process.
Application example
Support agent with RAG over product base + CRM integration
A B2B company deployed an agent that answers technical questions via RAG, qualifies customer interest and automatically creates opportunities in the CRM — handling 400+ contacts per day without an SDR team, with a 68% reduction in support costs and response time dropping from 8 hours to 30 seconds.
- Agent with access to real company data via RAG and integrated tools
- Model selection by use case: cost, latency and privacy considered
- n8n orchestration to connect AI to the systems the company already uses
- Logs, monitoring and feedback loop for continuous quality improvement
Frequently asked questions
Common questions before implementing generative AI and agents
What is an AI agent and how does it differ from a simple chatbot?
A simple chatbot follows a fixed flow of questions and answers. An LLM-powered AI agent understands natural language, accesses external tools (APIs, databases, email) and can execute multi-step actions autonomously. An agentic system goes further: it receives an objective and decides on its own which tools to use and in what sequence to achieve it.
What is RAG and when should I use it?
RAG (Retrieval-Augmented Generation) connects an LLM to a proprietary document base so it answers accurately about your content — without retraining the model. Use RAG when your company has documents, manuals, contracts or knowledge bases that the model does not know by default. This ensures precise answers without hallucinations about your specific context.
What is the difference between n8n automation and generative AI?
n8n is a flow orchestration tool that connects systems, APIs and triggers. Generative AI (LLMs) adds reasoning, natural language and semantic decision-making. The two work together: n8n orchestrates the flow (when to trigger, what to connect), the LLM processes and decides (classify, summarize, generate, respond). The combination eliminates brittle automations based on fixed rules.
Does my company need GPT-4o, Claude or can I use smaller models?
It depends on the use case. For simple tasks (classification, extraction, summarization), smaller models like GPT-4o mini, Claude Haiku or Llama 3 have much lower cost with equivalent results. For complex reasoning or legal analysis, GPT-4o or Claude 3.5 Sonnet are more appropriate. We always evaluate cost, latency and privacy to recommend the right model.
Is company data secure when using LLMs?
Yes, with the correct architecture. We use RAG so proprietary data stays in your infrastructure and only relevant excerpts are sent to the model. We configure with no training data storage (OpenAI Enterprise, Claude API with zero data retention). For high sensitivity, we deploy local models (Llama, Mistral) that never leave your environment.
How long does it take to implement an AI agent?
A basic support agent with RAG can be implemented in 2 to 4 weeks. n8n + LLM automations for internal processes generally take 1 to 3 weeks per flow. More complex agentic systems with multiple tools and integrations take 6 to 12 weeks depending on scope.
What is Agentic AI and why is it different from a common assistant?
Agentic AI is an architecture where the LLM acts as an autonomous orchestrator: it receives an objective, plans the necessary steps, calls tools, evaluates intermediate results and adjusts the plan until the task is complete. Unlike an assistant that answers questions, an agentic system can search the web, query a database, send an email and update a CRM — all autonomously.
Is it possible to integrate the agent with WhatsApp, internal systems and CRM?
Yes. We integrate via WhatsApp Business API (official), REST APIs from CRMs (Salesforce, HubSpot, Pipedrive), ERPs, databases and any system with an available API. n8n acts as integration orchestrator, connecting the agent to the tool ecosystem the company already uses without replacing what works.
What is the real ROI of implementing AI in my company?
The ROI depends on the use case and operation volume, but AI automation projects in support, qualification and document processes typically return 3 to 6 times the investment in 12 months. The main factors: 30-70% reduction in support costs, 4-8 hours saved per employee per week on document tasks and the ability to handle more demand without growing the team. Our free technical diagnosis maps the processes with the highest return potential before any investment.
How quickly does AI automation start paying for itself?
For support and lead qualification automations, the typical payback is 60 to 120 days. n8n + LLM flows for triage and data extraction generally pay back in 30 to 60 days. RAG systems over internal documents usually show visible return in under 30 days from the immediate reduction in time the team spends searching for information — before any other optimization.
Next step
If your operation can gain scale, speed or quality with AI, the diagnosis shows where to start.
We map the process with the highest ROI for AI automation, define the architecture and deliver a proposal with stack, timeline and investment.