What can I automate with AI agents?
Autonomous LangGraph agents that reason, make decisions and adapt to context
An AI agent is not a chatbot with canned answers. It is software that reasons about your problem, queries your data in real time through RAG, runs actions in your systems (CRM, ERP, email, calendar) and decides the next step without human intervention. We build each agent with Python and LangGraph, tailored to the specific logic of your business — not generic templates.
Why AI agents, not Zapier/N8N
The difference is the ability to reason and adapt
Real reasoning
Agents analyse context and make decisions. They don't just run if/else rules.
Dynamic adaptation
They adjust to the context of each interaction. They don't follow rigid scripts.
Smart escalation
They know when they need a human and pass on the full context.
3 main use cases
Real examples with metrics and the full tech stack
24/7 customer service
An agent that queries your knowledge base with RAG in real time, interprets the customer's intent and resolves level 1 and level 2 queries without human intervention. Connected to the WhatsApp Business API, email and web chat. When it detects frustration or an out-of-policy case, it hands over to the operator with full context.
Key features:
Sales & qualification
A conversational agent that qualifies leads in real time using your scoring methodology (BANT, MEDDIC or custom). It talks naturally, picks up key information without feeling like a form, and syncs every qualified lead to your CRM — HubSpot, Salesforce or Odoo. It books meetings straight into the assigned salesperson's calendar.
Key features:
Internal operations
An agent that orchestrates complex workflows by connecting your existing systems — ERP, CRM, databases, internal APIs. It makes decisions based on business rules and real-time data. It processes documents, extracts structured information and runs chained actions without anyone pressing a button.
Key features:
Tech stack in every case
Professional tools, real Python code
LangGraph
Workflow orchestration with decisions
State machines for complex agents
RAG
Contextual knowledge retrieval
Vector databases + semantic search
Python + FastAPI
Robust, scalable backend
REST APIs + asynchronous processing
Full stack:
A shared process across all cases
4 phases from analysis to production
What all the cases have in common
Whatever the sector or process, every agent we build shares the same principles: real Python code (not low-code platforms), LangGraph decision graphs you can audit and modify, and access to your data through RAG with vector databases. There are no black boxes. You have access to the source code, the logs of every decision and real-time performance metrics.
The result is an agent that works like one more member of your team: always available, consistent in its decisions, and improving with every interaction thanks to the feedback you gather in production. If a process in your business has clear rules, repetitive volume and needs speed, an AI agent will probably do it better and more cheaply than doing it by hand.
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