Our DNA
Our team is composed by Senior engineers, UX and Managers with 10+ years in the industry. That collective experience is what makes AI safe to lean on: the machine writes a lot of the code, the seniors still own the architecture.
AI in every process — starting with the one that builds your software
Long-track tech experience applied to your success
Who we are
We are a young startup located in Uruguay, a hub for IT companies in Latam.
Our main mission is to provide high quality technology services, from consulting to product design, development, deployment and support/maintenance — with AI built into every one of those steps, ours and yours.
Our Approach
We are a MVP-first company, always proposing ways to reduce market time, maximize resources and think in a iterative way. AI shortens every one of those iterations — from idea to product in weeks, not quarters.
Our Methodology
We apply Agile principles to all our internal process and to every process when developing, maintaining or scaling software products — and we instrument those processes so we can tell you what AI actually gave back.
Three ways AI lands in a company.
We do all three.
We don’t sell “digital transformation”. We build your software with AI in the loop, put agents inside the cycles that slow your team down, and leave your people able to run both without us.
Software development, AI-native
Every stage of how we build for you has AI in it — discovery, architecture, implementation, tests, review, documentation, release. The result is a cycle time that used to be impossible at this quality.
- Specs and domain models drafted against your real business, not a template
- Seniors decide the architecture; the boilerplate writes itself
- Test suites, runbooks and docs generated and then reviewed, never skipped
- Large refactors and migrations at a scale hand-work can’t reach
- An AI first pass on every review — and a human last word
Agents across your internal cycles
Your team spends hours routing tickets, drafting follow-ups, reconciling the same record in three systems and answering the same question again. We put agents inside those loops — reading your systems, taking the safe actions, escalating only when judgement is genuinely needed.
- Sales handoffs and CRM hygiene
- Hiring pipeline triage and candidate drafts
- Finance close and reconciliation
- Support triage and first-pass replies
- Engineering review, status and release notes
AI adoption for your team
The tools are the easy part. The hard part is changing how a team works without breaking what already works. We map the processes, pick the ones worth changing, put the tooling in your people’s hands and write the guardrails around it.
- Process map: where the hours actually go, by role
- Tool selection and rollout, with the boring integration work done
- Guardrails: data boundaries, review gates, audit trails
- Enablement sessions per role, not one generic workshop
- Measurement: hours back, cycle time, defect rate
AI accelerates solid engineering.
It doesn’t replace it.
Ten-plus years of architecture work sit behind every engineer here. AI compresses the build cycle — it doesn’t excuse skipping the parts that matter once the system takes real traffic, gets audited, or has to be handed to someone else.
Right-sized architecture
Calibrated to the stage your company is actually at — not over-engineered for scale you don’t have, not under-built for the load you do. The seams you’ll want next year are already there.
Production-grade foundations
Observability, test coverage, deployment hygiene and the operational runbook ship with the product — not as line items you get quoted for later.
A human last word
Nothing reaches production without an engineer who can explain it. AI drafts, proposes and checks; the person whose name is on the merge is still accountable for it.
Cycle time as a feature
Shorter loops change what’s worth trying. When a change takes days instead of a quarter, you get to be wrong cheaply — which is how good products actually get found.
Ownership
Your code, your data, your deployment, your roadmap. We hand off repositories, infrastructure-as-code and runbooks. Nothing we build requires us to keep running it.
Agent-powered support
Optional: we run maintenance and support on the same agent tool chain we built into your system. Routine tickets get resolved autonomously; the real ones reach the people who should see them.
Map. Pilot. Scale. Hand-off.
No lock-in.
Every engagement starts grounded in your real workflows, not a generic framework. We prove one concrete win before scope expands — and you can stop after any step with something that works.
Map
Two weeks with the people doing the work. We catalogue your processes, your tooling and where the hours really go, and hand you a ranked list: automate, augment, rebuild, leave alone.
Pilot
One process, one agent, or one build — shipped in roughly four to six weeks on your real data, with the metric to beat agreed before we start.
Scale
We work down the list. Integrations with what you’re keeping, migrations off what you’re not, and the change management that makes your team actually use it.
Hand-off
You own the code, the deployment configuration, the runbooks and the operational knowledge. We stay on retainer if you want us — but nothing requires it.
The questions we keep getting asked
Still not sure where AI fits in what you run today? Tell us how your team works and we’ll come back with the loops worth automating and the ones worth leaving alone.
Is “AI-assisted development” just faster typing?
No — the leverage is in the parts around the typing. Specs written against your real domain, exhaustive test suites, migrations that touch four hundred files, code review that reads every line before a human does, documentation that stays current. Those were the expensive parts, and they are the ones that compress the most.
What it buys you is cycle time. Work that was a quarter becomes a few weeks, which changes what is worth building at all.
How do you keep quality up when the machine writes the code?
The same way we always did — a senior engineer owns the architecture and has the last word on every merge. AI writes a lot of the code; it does not decide the boundaries, the data model, the failure modes, or what “done” means.
Concretely: tests and observability go in from day one, every change is reviewed, and nothing reaches production without a human who can explain it. Our team has 10+ years each in the industry; that judgement is exactly what does not get automated away.
Where does AI actually pay off first in a company like mine?
Almost always in the loops nobody owns: routing, drafting, chasing, reconciling the same record across three tools, answering the question that gets asked forty times a month.
We start by mapping where the hours really go — talking to the people doing the work, not just the people who bought the tools — and then pick the one loop with the clearest measurable win.
What happens to our data?
Data boundaries are part of the design, not an afterthought. We define up front what leaves your perimeter, what stays inside it, and which model providers are in scope — including running against enterprise endpoints with no training retention, or fully self-hosted models where the compliance case demands it.
Every agent action worth auditing is logged, so you can answer “why did it do that?” months later.
Do we have to replace our tools to get value from this?
No. Most engagements start with agents working on top of the stack you already run — reading your systems, taking the safe actions, escalating the rest.
Rebuilding a tool is something we only propose when the tool is genuinely the bottleneck, and we say so with the numbers in front of us.
How do agents fit alongside the people on my team?
Agents take the repetitive middle. Your people keep the judgement calls, the relationships, and the decisions that carry risk.
We do not pitch head-count cuts. We pitch your senior people spending their time on senior problems instead of copying fields between tabs.
How long before we see something real?
Two weeks to a map of your processes with a ranked list of what to automate, augment, rebuild or leave alone. Four to six weeks after that, one pilot running on your real data.
Scope stays deliberately tight until the first thing works. Then we expand.
Let’s find your first loop.
Two weeks from now you could have a map of your processes and a ranked list of what AI should touch first. That’s the whole commitment.