We don't use AI. We run on it.
The rest of this page is the argument. Read it before you believe us.
ScrollMost companies that claim to "work with AI" use it as a tool. Another browser tab, an assistant on the side, a pilot that never crosses into production.
We decided to do something else. We run on agents. We don't use them — they run the operation.
It sounds like a word game. It isn't. It changes who does the work, how long it takes, what it costs, and how it gets documented. When a client asks us for a proposal, no account manager sits down to write a Word doc. Four agents coordinate — research, finance, legal, copy — and a human PM reviews. What takes two days elsewhere takes two hours here. That's the math.
Three things, in order.
We saw that AI pilots die in the gap between curiosity and operation. Companies buy licenses, test models, write prompts, and six months later they have a graveyard of POCs and a higher cloud bill. The model was never the problem. The model stayed peripheral.
We saw that large consultancies sell quarters. What a mid-market team needs to solve this week, they scope at three months. The latency is structural: they bill human hours, and human hours don't scale.
We saw that AI boutiques start backwards. They build a product, then look for someone to sell it to. We started backwards from backwards: we built the agentic infrastructure to run our own company first, then turned it into what we offer.
Three pillars, one team, one fleet of agents.
Data
We work on top of the client's Looker and BigQuery — no migration, no rip-and-replace. We cut BigQuery costs by 30 to 60% in six to eight weeks, measured on the client's billing, not on ours. Then we put analytical agents on top so the data team stops grinding ad-hoc reports.
−30 to 60% BigQuery cost Data & Analytics →Agents
Custom agentic workforce for sales, billing, collections, recruiting, support. Measured cuts: 60 to 80% in time-to-hire, 40 to 70% in ticket deflection, up to 80% in manual admin load. Agents take the repetitive work. People move up to what calls for judgment, context, and relationships. We augment whole teams to do more with less effort and better quality — not to shrink them.
up to −80% manual load AI Automation →Products
notarIA for meetings, CoreWhapp for WhatsApp Business, LKMind for data teams, DataMetricX for e-commerce. Our own SaaS, live today, ready to plug into a stack in hours.
4 products, live today Products →The infrastructure we ship to clients is the same one we internally use to write proposals, invoice, recruit and report. What lives in our back office is what arrives at theirs.
This has to be said plainly, because most won't say it: when AI walks into a company, the first thing in the room isn't excitement — it's fear. The question nobody asks out loud is "is this here to replace me?"
Our answer is no, and we back it with what we do, not with a line.
Agents take the repetitive work — the kind that wears people down, the kind nobody claims as their own. People move up to judgment work, to context, to dealing with another human being. When we walk into a company, we don't empty out the team: we lift it and we walk it through the transition. That's the difference between automating and caring.
And the walk-through isn't a PDF. Every implementation includes people who train each of the client's employees, in-person training when it's needed, and a dedicated onboarding agent that answers their specific questions — at any hour.
That agent will have a name: Laura. We're building her alongside our website — she isn't live yet. When she is, Laura will be the cleanest proof of something that, to us, was never a contradiction: we use an agent to take care of people. "We run on agents" and "we take care of your people" don't compete. They're the same sentence.
We earn every promise with what we do.
Operable results in weeks, on your own data.
We promise operable results in weeks, measured on your own data, on top of the stack you already have. If it doesn't show up on your dashboard in eight weeks, we don't bill the next phase.
Your people won't be left alone with the tool.
We train them, walk them through the transition, and stay with them through the adoption curve. Consultancies implement and leave. We stay for the hard part.
We guard your data the way we guard your people.
We operate on top of your stack without extracting or exposing what's yours. Whatever we can claim about security, we claim precisely and without inflating it — the detail is written down, not promised in passing.
We don't sell what we haven't done.
Every number we put out comes from a real case. When the range moves, we update it before we sell it, not after.
We speak your language — technical when it matters, plain when it doesn't.
LookML, slot exhaustion, n+1 queries when the call is with engineering. Concrete verbs and numbers when it's with finance. And human words, not manual-speak, when the conversation is with the person who sits in front of the tool every day.
If this is you, we're talking to you.
We're talking to you — CTO or VP Engineering at a mid-market company — if your BigQuery bill doubled in twelve months and your data team is buried in ad-hoc requests.
We're talking to you — Head of Ops or CEO of a 50-to-300-person business — if your operation is growing but your admin team can't keep up, and you want to try agents on one concrete process without breaking what works or scaring your people.
We're talking to you — LATAM SaaS founder — if your team is small, you want to move metrics without hiring ten people, and you already built a Frankenstein of SaaS tools that don't talk to each other.
We're talking to the technical CTO who walked away skeptical from three consultancies and two boutiques. To the SMB CEO who got burned by a bad chatbot — and who's afraid, above all, of what AI might do to the people who work with him. To the founder who knows the next generation of service companies is built by lifting people, not replacing them.
The invitation · 06
We're not going to ask you to book a demo. We're going to ask for something simpler: read how we work. Read the manifesto, the published cases, the open glossary. Look at the repos. Look at who signs.
If, after that, a conversation makes sense, we'll have one. If it doesn't, neither of us wasted any time.
We run on agents. That's the edge. And we use those same agents to take care of the people who work alongside them. The rest is in here, open, so you can verify before you believe us.
We run on agents. That's the edge.