What's broken today
Manual processes that don't scale
The team grows, spreadsheets multiply and everything still runs through the same people. The company hires to keep things flowing, not to grow.
Rigid automations that keep breaking
You have no-code flows that worked at first, but now every change in an upstream system knocks something over. Maintenance kills the ROI.
Decisions that need context
The last step of the process takes judgment: approve, reject, escalate, prioritize. Today people do it by hand because the rules don't fit in if-statements.
How we build AI automation
Diagnostic
We map the current process step by step and identify which steps can be automated and which need human judgment.
Design
We define the architecture: which AI model handles each decision, which tools get connected, where it escalates to a person and what gets measured.
Build
We implement with guardrails, an audit log and observability from day one.
More
We implement with guardrails, an audit log and observability from day one. We test with real data before production.
Operation
We release the agent into the real process, monitor business metrics and tune it during stabilization.
What it's built on
Models from several providers, chosen for each task.
Workflows with explicit rules, connected tools and human decision points.
ERPs, CRMs, WhatsApp Business, payment gateways, SAT (Mexico's tax authority), Drive, custom APIs.
Audit log, real-time metrics and alerts to your phone.
Who AI automation is for — and who it isn't
✓ Who it's for
- Companies with repetitive, high-value processes (sales, ops, finance)
- Teams buried in tasks they could hand off to an agent
- Companies that already tried no-code tools and hit the ceiling
- Leadership that needs traceability and an audit log for audit or the board
✕ Who it's NOT for
- Low-volume processes — the build cost doesn't pay off
- Rigid processes with no variation: a classic automation is enough
- Companies that just want a basic chatbot that doesn't take action
- Decisions with irreversible consequences that can't keep a human in the loop
FAQ · AI automation
How much does AI automation with Studio cost?
It depends on scope and on the integrations with your current stack (AI models, Orchestration, Integrations, Observability). We don't publish list prices: implementation and monthly operation are quoted after the Studio diagnostic, which is free and gives you a range tailored to your case.
How long does it take to get AI automation into production?
It depends on the number of integrations and on operational complexity. We work in stages — design, build, testing with real data and team training, then a controlled production launch — and we agree on the timeline with you once scope is locked.
How is Studio different from a traditional consultancy?
Studio builds, hands it over running and keeps it alive. We don't sell hours or never-ending pilots: we operate the outcome. Every project ships with guardrails, an audit log and observability dashboards already live. The stabilization phase is included.
Do I have to replace my current stack (AI models)?
No. AI automation plugs into what you already have through official APIs or native webhooks. If we find a tool that caps the project's upside, we'll tell you straight, but we never require a migration as a prerequisite.
What does this mean in business metrics?
Every Studio implementation ships with operational metrics on a dashboard: time saved per process, error rate, unit cost and equivalent FTE capacity freed up. They're calibrated against your own baseline at kickoff and reported weekly during stabilization.
Is it locked into a single AI vendor?
No. We work with models from several providers and pick the right one for each task.
Which process in your company should AI be running?
Studio diagnostic: we identify the highest-return processes, estimated savings and an implementation plan. No cost.
No cost · No commitment