Add intelligence to your existing processes with AI that knows your operation
You don't need to replace everything. We connect language models to your data, documents, and workflows so AI makes informed decisions, not generic ones.
Generic AI models like ChatGPT don't know your business. If you ask about your prices, your clients, or your internal processes, it knows nothing. The AI potential exists, but it's lost because it's not connected to your real information.
We build the bridge between language models and your real information. Your documents, database, historical records become the context that feeds the AI. The result: a system that responds with real knowledge of your operation.
Everything you need, nothing you didn't ask for
RAG (Retrieval-Augmented Generation)
We index your documents in a vector database. The AI only uses relevant information, it doesn't hallucinate data that doesn't exist.
Autonomous agents with tools
The agent can execute actions: query a DB, search a file, call an API, send an email. It doesn't just generate text.
Integration with your existing apps
We connect AI to your existing CRM, ERP, database, or dashboard. You don't need to migrate anything.
Document processing
PDFs, contracts, invoices, reports — AI extracts, classifies, and summarizes structured information without manual work.
Guardrails and quality control
The system includes validations to avoid out-of-context responses, inappropriate content, or unauthorized actions.
Metrics and quality evaluation
We monitor response quality, token usage, costs, and system failure points.
The process, step by step
01 · Identify the use case
We define what specific problem AI solves and what data it needs to do it well.
02 · Prepare the data
We clean, structure, and index the information. Data quality = response quality.
03 · Build the pipeline
We design the flow: embedding → vector search → prompt → LLM → structured output → action.
04 · Evaluate and adjust
We test with real cases, measure response quality, and adjust the prompt, chunking, and retrieval.
05 · Deploy with monitoring
We activate in production with logging, cost alerts, and continuous quality monitoring.
Already delivered in production
AI workflow that qualifies and serves prospects in real time
Intercultura received a steady flow of WhatsApp and Instagram messages about its Work & Travel and Au Pair programs. We implemented an integrated AI workflow that responds in real time, qualifies prospects, and connects customer-facing channels with the automation engine.
From personal WhatsApp assistant to business intelligence system
Luzmai started as a personal AI assistant on WhatsApp that created reminders and notes. Today it's evolving into an autonomous system that knows an entire business operation — the digital brain of a company.
What people ask us most
How much does it cost to use OpenAI in production?
It depends on volume. For 1,500 conversations/month like VEMSA, the API cost is less than $50 USD/month. We optimize prompts to minimize token usage.
Can the AI make mistakes?
Yes. That's why we implement guardrails: the agent only responds within the domain we trained it and transfers to a human when unsure.
Does it work with documents in Spanish?
Yes. GPT-4o has excellent Spanish support. Embeddings also work well in Spanish.
Does this solve a problem you have today?
Tell us the context. If we can help, we'll tell you exactly how and how long it would take.