MIA LABS · AI IN CHILE · 2026
We build AI systems inside your operation in 2–8 weeks. Your team inherits them. Discovery call, free.
Why now
Today there are systems that read documents, respond with context, execute tasks, and leave evidence of what they did.
The problem usually isn’t the model. It’s connecting it to your data, your rules, your tools, and your metrics.
Companies between 20 and 200 people are stuck in no-man’s-land: too big to keep operating on patches, too small to build their own AI team, and too specific for generic software.
MIA Labs exists for that space: building useful, measurable, and maintainable systems without asking you to build a new team.
Where we put AI to work
Product, operations, and teams. Three different surfaces, one demand: the system gets used.
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AI features that improve the customer experience and make your product more useful. Smart search, conversational interfaces, in-app copilots, recommendations, and content generation.
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Agents that take repetitive work and move it between systems. Invoice processing, ticket triage, lead enrichment, reports, contract review, and data updates.
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We help engineering, marketing, sales, admin, and support use modern AI tools as a natural part of the day. We set up the stack, train the team, and leave playbooks to repeat what works.
Where it pays back
We start from the work that today consumes hours, depends on manual judgment, or crosses too many tools.
| Team | Processes we can take off your plate |
|---|---|
| Sales | Lead qualification · initial proposals · CRM updates · follow-up |
| Customer support | WhatsApp agents · ticket classification · knowledge-base responses |
| Operations | Procedure Q&A · data extraction · automation of handoffs between systems |
| Finance | Invoice reading · reconciliation · expense classification · internal reporting |
| People | Policy Q&A · onboarding · initial candidate screening |
| Legal and compliance | Contract review · clause extraction · checks against internal policies |
How we work together
A short process to decide what to build, how to measure it, and when not to use AI.
A working conversation, not a sales pitch. We learn where the operation gets stuck, what data exists, and what outcome would justify the effort.
We come back with a concrete scope: what to build, what to integrate, how to measure quality, how long it takes, and what it costs. You leave with enough information to decide.
End-to-end delivery: prototype, automated evaluations, production deployment, and handoff. Every system ships with observability, documentation, and clear operating criteria.
If the system becomes important to the operation, we stay with you. Monitoring, adjustments, new integrations, and new systems under a cancellable monthly model.
What we build with
We choose the technique by the problem. The priority is that the system can be measured, run, and improved.
Agents that follow steps, use tools, and escalate to human review when the task needs it.
Search and answers over documents, policies, contracts, code, and ticket history.
Extraction, classification, and summarization of invoices, contracts, forms, and reports.
Chat and voice for customers or internal teams, in Spanish and English from day one.
AI connected to the tools you already use: CRM, ERP, email, Slack, WhatsApp, and databases.
Evals, traces, and metrics to know when the system is responding well and when it needs adjustment.
It’s not enough for AI to respond well in a demo. It has to hold up in production — with your data, your rules, your operation.
Why us
US engineering and local execution in Chile. One team to discover, build, and partner.
6 years building production software, including 4 years on mission-critical systems for the U.S. defense sector. Environments where uptime, security, and correctness aren’t optional.
We work daily with agents, RAG, evals, voice, observability, and non-deterministic systems. These aren’t presentation words. They’re the tools we use to build.
US engineering by MIA Labs and execution in Chile with GAP Tecnología. Bilingual by default, with deployment to AWS, Azure, GCP, on-premise, or inside your app.
Discovery call, free. You leave with a map: where AI fits, what to build first, and the risks we see.