Control from the first line: which information comes in, what gets automated, which decisions stay human and how the return is measured. We put the operation in order so AI works inside your systems. We work with companies in Venezuela, the Dominican Republic, Mexico, Colombia and the US Hispanic market.
We work with Anthropic’s deployment standard.
FROM USING TO OPERATING
A company that operates with AI has decided which problems it solves, which information comes in, which systems it works on, what gets automated, which decisions stay human and how it is measured. That is what we put in order and build: the end-to-end process, with written rules and a rollback plan.
THE THREE STEPS
Someone opens an assistant and solves their task. It is useful and it is individual: the result depends on who uses it and on what they paste into it.
The process moves without anyone copying data around. AI reads, decides within a cap and acts with the system’s tools. Whatever commits money waits for a person.
Usage policy per domain, use-case inventory, information classification, evaluations before publishing and a rollback plan. You govern in order to build.
You govern in order to build. We diagnose in order to build.
IN ONE MINUTE
Gabriel explains it straight, no theatre: what we are good at and why that changes your operation.
HOW IT DECIDES, STEP BY STEP
Three situations from a demo company’s operation. In each one the AI exposes the signals it read, the cap that applies and who had to decide.
Sample lead
Raw lead comes in
10:02 · “Hi, I need a price for three units like last year’s.” Number +1 809 ··· 41. Nobody has read it yet.
Verifies who is writing
Looks the number up in the account master: Distribuidora del Este (DR), two previous purchases paid on time.
Checks the datum at its source
Availability of the model bought in 2025: five units in DR. The price comes from the Quote builder, not from the chat.
Decides within its cap
Replying with availability and handing off to the assigned salesperson is level 2: it executes and notifies.
Hands off with context
Lucía Farías receives the conversation with history, summary and a link to the account.
What got recorded
Opportunity created without manual entry. Price pending from the Quote builder. Full circuit in Traces.
Raw lead comes in
The salesperson asks for 12 % to close. The cap without approval is 8 %. The customer cited a competitor in the conversation.
Reads the current list and the cap
Price list RD-2026, country tax, discount policy v3: up to 8 % without asking.
Computes margin with the requested discount
At 12 % margin lands at 22 %; the policy minimum is 18 %. Within policy, but it commits margin.
Prepares both variants
Generates quote v1 at 8 % and leaves v2 at 12 % ready with its evidence.
Stops: a person decides
12 % is level 1: it opens an approval for the commercial director and waits. It sends nothing.
What got recorded
Quote v1 issued. The 12 % waits for Tomás Herrera; his decision will sit in the audit trail with name and time.
Raw lead comes in
Issuance request for invoice MX-00931. The order’s tax ID does not match the customer master.
Extracts the document data
Tax ID, receipt usage and total, read from the order before issuing.
Cross-checks against the master
The order’s tax ID ends in XA4; the master’s ends in XB7. No match.
Reasons with the rule
A document with a wrong tax ID gets cancelled and reissued later. Blocking before costs less than fixing after.
Blocks and explains
Level 2: blocks issuance and sends finance the explanation with the mismatching datum.
What got recorded
Issuance blocked until the tax ID is fixed; the document will go out with the correct datum. Logged as a critical exception in the tower.
WHAT WE BUILD
Each pillar is a screen inside a demo company’s system. Open it and walk through it: it is the same thing we build on your operation.
AI is a machine: the raw material is your information and the product is the answer. Where each datum is born, where it travels, which decision it triggers and what happens when it fails.
What AI may do alone, how far, what it may never do even with approval and how long data is kept. Written to be read and audited every hour.
Training ten people out of four hundred is a pilot. A program per role — use, operate, govern — measured against real platform usage.
Custom systems built with Claude: agents with tools and autonomy levels, integrated with what you already have, with evaluations before publishing.
INDUSTRIES
Eight with systems in production; oil and gas and clinics, with a demo system.
CAPABILITIES
HOW WE WORK
Four phases with verifiable closure. No generic timelines: scope and model are defined in the assessment, on your real process.
Phase 1
We map the real process: which information comes in, who decides, which systems touch the data and where AI already came in without permission.
What is delivered
Use-case inventory, systems map and risk matrix.
Who decides
Leadership, with us at the table.
Scope and model are defined in the assessment.
Phase 2
We write the usage policy per domain, order the single source of truth and design the architecture: agents, tools, autonomy levels and rollback.
What is delivered
Policy v1, data governance, architecture and a transition plan with owner and date.
Who decides
Each domain owner signs their policy.
Scope and model are defined in the assessment.
Phase 3
We build the custom system with Claude. Every agent enters in shadow until it passes its evaluations; whatever commits money waits for a person.
What is delivered
System in production with traceability, audit trail and a circuit per run.
Who decides
A person approves every agent before it goes live.
Scope and model are defined in the assessment.
Phase 4
Training per role against real usage, adoption indicators and before-and-after measurement. What is not measured becomes technological enthusiasm.
What is delivered
Adoption indicators, program per level and an improvement plan with an owner.
Who decides
Leadership reviews the indicators; we build them.
Scope and model are defined in the assessment.
PARTNER ECOSYSTEM
REAL CASES
WHO IS BEHIND THIS
“I help companies move from using artificial intelligence to operating with it — and to governing that operation.”
In almost every company I visit, AI is already in: through people, on their own, with no shared rules. It is like having a Ferrari in the garage and nobody who has learned to drive it. What is missing is the frame that orders its use: which information comes in, what gets automated, which decisions stay human and how the return is measured.
We diagnose in order to build: AI prepares and executes within written rules, and whatever commits money is decided by a person. If you want to see what that looks like on your operation, schedule 30 minutes with me and we look at it on one of your own processes.
Gabriel Montiel
CEO · Boosty Digital
FOR LEADERSHIP
A CEO’s question is no longer whether to allow AI, but how to govern a technology already in use across the organization. Schedule a 30-minute assessment with Gabriel: we look at your real process and tell you what we would order first, what we would build and what we would measure.
