Self-Hosted AI Agents for Your Business

We deploy department-specific AI agents on your own hardware, mac Mini cluster, NVIDIA DGX Spark, RTX 5090, H100 or whatever you already own. Zero cloud fees, full data privacy, one agent per department.

What Self-Hosted AI Agents Can Do for Your Business

• Works with the Hardware You Already Have: We size, install and operate the AI hardware inside your company. A Mac Mini M4 cluster for lightweight department agents, an NVIDIA DGX Spark for serious LLM inference, RTX 5090 rigs for GPU-heavy pipelines, or H100/H200 servers for enterprise scale. On top of that, we deploy department-specific agents (marketing, sales, HR, finance, support, ops) that share the same local infrastructure but keep their scopes, data and permissions strictly separated.
• One Agent per Department: Marketing, sales, HR, finance, support and ops each get their own agent, scoped data, scoped tools, scoped permissions. They share hardware, not secrets.
• Zero Per-Token Fees: Once the hardware is paid for, every inference is free. Teams running at volume typically save 70-90% vs. cloud APIs within the first year.
• Full Data Privacy: Prompts, documents, conversations and outputs never leave your network. Perfect for legal, healthcare, fintech or any sector with confidentiality constraints.
• Tuned on Your Data: We fine-tune smaller open-source models (Llama, Qwen, Mistral, Phi) on each department's historical data and tone. Smaller model + your data often beats GPT-class models on your specific tasks.
• MLOps Included: Prometheus + Grafana dashboards for GPU utilisation, inference latency, cost/inference and accuracy drift. Scheduled model updates and rollback plans baked in.
• Fits the Tools You Already Run: Our bilingual team ships English and Spanish content natively. Your AI agent speaks your customers' language without awkward translations.

How We Build Your Self-Hosted AI Agents

1. Department & Workflow Audit: We map each department in your your business company and identify the top 3 AI-automatable workflows per team.
2. Hardware Sizing (or Audit): We benchmark the models required against what you own. If hardware is missing, we recommend exact SKUs (Mac Mini M4, DGX Spark, RTX 5090, H100) with a clear cost/benefit.
3. Install & Harden: We install the runtime stack (Ollama, vLLM, TensorRT-LLM, MLX on Apple Silicon), harden the network (no egress, VLAN per agent) and set up backups.
4. Agent Build per Department: Each agent gets its own knowledge base (RAG), tool set (your APIs, not the internet) and permission scope. Marketing can't read HR data, and vice versa.
5. Fine-Tuning & Evaluation: We fine-tune on each department's historical data and benchmark against the commercial cloud alternative on your specific tasks.
6. MLOps & Ongoing Care: Dashboards, alerts, scheduled model upgrades, quarterly fine-tune refreshes. On-call support when something breaks.

AI Technologies We Use

From self-hosted open-source models to cloud APIs, we choose the right tool for each self-hosted ai agents use case.

Related services

Neighbouring problems we also solve, in case the one that brought you here is not the first to tackle.

• AI Consulting
• AI Agents & Automation
• AI Task Automation
• All our services: The full catalogue, from web and mobile to AI, data and maintenance.

Self-Hosted AI Agents: FAQ

Can you really run this on a Mac Mini?

Yes, a cluster of 4-8 Mac Mini M4 Pro runs department-level agents very comfortably for small-to-mid teams. Apple Silicon with MLX is surprisingly strong on LLM inference per watt and per euro. For heavier workloads we graduate you to NVIDIA DGX Spark (a desktop-sized AI workstation) or RTX 5090 rigs, and further up to H100/H200 servers.

What's the NVIDIA DGX Spark?

A desktop-format AI workstation from NVIDIA, ~1 petaflop of AI compute, 128GB unified memory. Enough to run Llama 70B and similar models locally. Perfect for mid-size companies that want serious on-prem AI without the cost and complexity of an H100 server.

Can agents from different departments really stay isolated?

Yes, each agent runs as a separate process, with its own VLAN, its own permission scope, its own tool manifest and its own RAG index. Marketing's agent can't read the payroll folder; HR's agent can't post to your ad platforms. Hard boundaries enforced at the OS + network layer, not just prompt-engineering.

Which open-source models do you use?

Whichever open-weight model is the best available for your task on the day we deploy, meaning one you can download and run on your own server without going through anyone else's API. The list is deliberately not fixed. This field moves every few months and pinning a name here would age badly. In practice we pick from the main open families (Meta, Alibaba, Mistral, Microsoft and the specialised speech and vision projects), sizing the model to your hardware, a large one for a department agent on a proper GPU server, a small one for a compact machine. Everything we deploy is under a permissive licence, and swapping the model later is a configuration change, not a rebuild.

When does it make financial sense vs. using OpenAI/Anthropic APIs?

Roughly, if your team uses AI for more than ~30€/day of cloud API, self-hosting pays for itself inside 12-18 months on a Mac Mini cluster or a DGX Spark. For enterprise volumes, H100 servers break even in 4-6 months. We run the math honestly. We'll tell you if cloud is cheaper for your case.

Can you combine on-prem with cloud for peak load?

Yes, hybrid is a common pattern. On-prem handles baseline load (cheap, private); cloud fallback catches peaks (expensive, but only when needed). We set up automatic routing with a circuit-breaker so your system stays responsive even if your on-prem hardware fails.

How much does a deployment cost?

It depends on the tier you need, a Mac Mini M4 Pro cluster for a handful of department agents, an NVIDIA DGX Spark setup, or RTX 5090 rigs and H100 servers once you are running many agents. We quote hardware transparently at cost, and charge for deployment and ongoing MLOps, so the number tracks the machines you actually need.

Do you work with businesses across Spain and internationally remotely?

Yes. We work fully remotely with clients across Spain, Europe, the US and LATAM. We run video calls, live demos and collaborative documentation for every milestone, and we're available in your time zone.