AI and automation in production, not a PowerPoint — LLMs, RAG and agents
We build LLM assistants and agents, RAG search over your documents, and process automation with n8n + LLM. AI that works in production, with the code and models as your property.
CLIENTS WHO TRUST OUR ENGINEERING
If your company faces these problems with AI, we can help
Lots of PowerPoint, zero production
Consultancies that sell "AI transformation" in slides and pilots that never reach production. What you need is an assistant or an automation running on your data, not another presentation.
Vendor lock-in with a single model
Solutions tied to one provider where switching models means rewriting everything. If OpenAI raises prices or Anthropic ships something better, you are stuck and you pay the bill.
Your team losing hours on repetitive tasks
People drafting the same emails, classifying tickets, copying data between systems and hunting for information across hundreds of documents. Work that a well-designed LLM agent can assist with today.
Knowledge trapped in documents
Manuals, contracts, policies and wikis where nobody can find anything. Keyword search does not understand the question. RAG over your own documents does.
"AI" that hallucinates and cannot be audited
Made-up answers, no source citation, no human oversight and no way to measure accuracy. Enterprise AI needs validation, traceability and a human in the loop.
You do not know where to start
Too many tools, too much noise and no clear impact metric. You need an honest assessment that ranks opportunities by real value and difficulty.
Applied, honest AI: assistants and agents on LLMs, knowledge retrieval with RAG, process automation and intelligent diagnostics. No hype, no computer vision, no promises we cannot keep.
The real proof: this very site runs on our AI
We do not show you made-up client cases. We show you AI you can verify yourself, right now, on this site.
ARIA — our internal AI platform
ARIA is the AI platform we run internally: it supports QA and regression on factory projects and orchestrates the agents that keep this site alive. It is not a trade-show demo — it is the tool we work with every day.
The AI assessments on this site
The assessments you see on each service (factory, SAP, AI, Salesforce…) are real applied AI: they capture your context, evaluate it with a language model and return a prioritized diagnosis in minutes. The one you are one click away from is the example.
Content, SEO and notifications with n8n + LLM
The blog, the es/en translations, draft generation and internal lead notifications are automated with n8n + LLM flows with human review. The automation we propose is the one we already run at home.
How we implement AI in 4 phases
From an honest assessment to AI running in production, with a human in the loop at every step. No endless research projects.
Assessment & prioritization
We map your processes and available data and identify AI opportunities, ranked by business impact and real implementation difficulty. Honest about what is worth doing and what is not.
Pilot in production
One end-to-end use case running on your real data: an assistant, a RAG flow or an automation. With baseline metrics to objectively compare before and after.
Scaling & integration
We connect the AI to your systems (SAP, Salesforce, Odoo, email, WhatsApp, ERP/CRM), with monitoring, alerts, human validation and traceability of every answer.
Operation & continuous improvement
Prompt and model tuning with your data, per-provider cost control, new use cases and a dashboard showing the real return of each automation.
Where you start: a pilot with fixed scope and a closed price
Custom AI is quoted per case, not off a catalog. That is why the entry point is a scoped pilot: one use case in production, with a fixed timeline and a closed price, and the metrics to decide with data whether to scale.
AI pilot in production
It is phase 2 of our methodology, offered as a product: we take one use case —an assistant, a RAG flow or an automation— and put it running on your real data, not in a demo. We measure before and after with baseline metrics so the return shows up in numbers. Scope and price are closed in the assessment and, if you move to the project, the pilot is credited.
For those who already have a use case in mind and want to see it working before committing to a large project.
- One use case running in production on your data
- Baseline metrics for before and after
- The real per-use, per-provider operating costs
- The code, prompts and configuration, as your property
Measurable RAG POC
Before indexing your whole knowledge base, we test RAG over a representative subset of your documents. We build an evaluation set with real questions and measure the hallucination rate and the quality of the cited answers. You end with a go/no-go decision based on data, not on a pretty demo.
For those with knowledge trapped in manuals, contracts or wikis who want to know, in numbers, whether RAG solves it before scaling.
- A RAG index over a representative subset
- An evaluation set with real questions from your team
- The hallucination rate measured, not assumed
- Answers that cite the source, auditable
- A go/no-go recommendation with the evidence
What you receive in an AI project
Code and prompts you own
The whole solution —code, prompts, configuration and flows— is yours from the first commit. No vendor lock-in and no dependence on us to operate it.
No model lock-in
Provider-agnostic architecture: Claude, GPT, Gemini, Llama or local models with Ollama. Switching providers does not mean rewriting the solution.
Human oversight & validation
Validation layers, escalation flows and human approval before acting. No autonomous AI without control.
Traceability & source citation
RAG answers cite the source document. Logs of every interaction to audit accuracy and fix whatever fails.
Documented integrations
Design of the connections to SAP, Salesforce, Odoo, email and your ERP/CRM, with data contracts and flow diagrams.
Pay-per-use cost control
No additional licenses: open source tools and pay-per-use APIs, with spend monitoring per provider and model.
Training & operation
Recorded sessions and manuals so your team operates the assistants and flows without deep technical knowledge.
Each capability, as an offer with scope
Once the pilot or the POC validate the case, each capability is contracted per case. This is how we charge, no fine print.
RAG-as-a-Service
Search and answers over your documents, with the source cited and hallucination under control. Operated and monitored.
- Index over your manuals, contracts and wikis
- Answers that cite the source
- Evaluation set and hallucination threshold
- Index updates as your documents change
Custom agents & copilots
Assistants that query your systems, run tasks and escalate to a human when needed. On whichever model you prefer.
- Assistant wired to your ERP/CRM, email or WhatsApp
- Escalation to a human and approval before acting
- Model-agnostic architecture (Claude, GPT, Gemini or local)
- Code and prompts you own
Automation with n8n + LLM
Flows that classify, extract and draft with a human validating. The alternative to RPA suites with expensive licenses.
- Classification, structured extraction and assisted drafting
- System-to-system wiring with no double entry
- A human validating before every action
- No recurring licenses: open source + pay-per-use
AI embedded in your product
Semantic search, summaries and copilots inside the software you already use or that our factory builds for you.
- Semantic search and summaries in your app
- Copilots embedded in your workflow
- Pay-per-use APIs, no model lock-in
- Traceability and logs of every answer
Request your AI assessment
Three fields and we schedule a session. We tell you honestly what fits and what does not.
Frequently asked questions
Can't find your question? Talk to an engineer — no sales script.
Contact us →What kind of AI do you implement exactly?
Applied, verifiable AI: assistants and agents on language models (LLMs), RAG search over your documents, process automation with n8n + LLM and intelligent diagnostics/scoring. We do not do computer vision. We prefer to do a few things and have them run in production, rather than promise capabilities we cannot keep.
Do you have anything running or just demos?
Running. The proof is on this very site: it is operated by ARIA agents, our internal AI platform, and the assessments you see on each service are real applied AI. We also automate our own content, es/en translations and lead notifications with n8n + LLM. The AI we propose is the one we already use at home.
Will I be locked into a single AI provider?
No. We build with a provider-agnostic architecture: Claude, GPT, Gemini, Llama or local models with Ollama. If tomorrow you want to switch models or a provider raises prices, you switch without rewriting the solution. All code, prompts and configuration are your property.
Do I need lots of data or a team of data scientists?
For assistants, RAG and LLM-based automation you do not need historical data: pre-trained models work from day 1 and RAG works over the documents you already have. We configure, integrate and operate the models; your team uses simple interfaces. We help you honestly assess what you have and what you need.
What happens if the AI gets it wrong or hallucinates?
Every implementation carries validation and human oversight: escalation flows, human approval before acting and RAG that cites the source so it does not make things up. We monitor accuracy with logs and adjust when the error exceeds defined thresholds. We do not ship autonomous AI without control.
Can you integrate AI with SAP, Salesforce or Odoo?
Yes, and it is one of our strengths: we have dedicated SAP, Salesforce and Odoo teams working alongside the AI team. For example, an assistant that queries inventory or orders in real time, lead scoring connected to your CRM, or flows that feed your ERP. We use native APIs, SAP CPI or n8n depending on your stack.
Does AI replace my team?
No. We automate repetitive tasks so your team can focus on high-value work. The assistant resolves the frequent and escalates the complex to a human; the draft generator produces a draft that a person reviews and approves. The team does not shrink — it becomes more productive.
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