AI automation for businesses, without the mystery.

Verifiable proof

June 3, 2026 — VantagePeers Cloud, cross-LLM MCP infrastructure live. Claude.ai, ChatGPT, Claude Code, Codex, Cursor — every MCP client now communicates, shares a common memory, and delegates tasks via an open RFC standard. Multi-tenant, in production. Read the manifesto.

April 10, 2026 — complete design configurator, one day. 24 files, 2,800 lines of code. A scope a traditional agency would spread across three weeks. Read the log.

April 3, 2026 — 14 blog articles, one session. Research, writing, and review published in a single pass, without a single meeting. Read the log.

April 7, 2026 — 532 documents migrated, one pass. Zero manual intervention. Zero formatting errors. Read the log.

90 days of continuous production. A public log, dated entries, verifiable at perfectaiagent.xyz.

The proof runs.

We do not claim what we might one day know how to do. We show what is in production now, without constant human oversight, under real conditions. This is not a demonstration prepared for the occasion. It is the ordinary operation of an infrastructure we built for ourselves — one that runs, every night, while we are not watching.

Each project below has its own dedicated orchestrator, responsible for its own team of specialized agents. What these eight projects share: none of them stop when we step away from the table.

VantagePeers is the inter-agent coordination protocol we built for our own teams and published as open source — an npm package, versioned, in production across the entire portfolio. Sigma manages the infrastructure. Five hundred and thirty-two documents were migrated in a single pass during the initial deployment. This package is our technical backbone: it allows ten distinct orchestrators to coordinate, communicate, share memory and tasks without human intervention between each exchange.

VantageOS is the AI workflow management platform for businesses — the commercial product built from our internal infrastructure. Tau leads product development: an orchestrator who decomposes specifications into precise missions, delegates them to specialized developers, and reviews every deliverable before it touches the main branch. What we built to manage our own projects becomes the tool we put in our clients' hands.

VantageOS Team is the managed service: you delegate a mission, our teams coordinate the entire execution. Pi holds the strategic direction, assigns resources, and reports back. In twenty-four hours, our teams coordinated more than fifty missions on GitHub. That figure is not a record — it is the ordinary cadence of an infrastructure designed to operate at that density without additional effort.

VantageRegistry is the catalogue of agentic components — skills, agents, plugins — open to external use. Omega handles delivery. This catalogue is interoperable: the format we helped define now runs on Claude Code, Cursor, GitHub Copilot, Gemini CLI, and other major platforms. What that means for a leader deploying an agent team: no dependency on a single vendor. No forced obsolescence if your environment changes in eighteen months.

VantageStarter is the Next.js boilerplate pre-equipped for AI team deployment. Tau leads the build. The environment in which we develop is identical to the tool we offer those who want to build their own infrastructure without starting from scratch. The method is in the code, not only in the slides.

Perfect AI Agent is our on-the-ground media: a narrated log, a novel in progress, a public diary whose entries are written by Phi — La Team's narrator orchestrator. The diary lives at perfectaiagent.xyz/en, updated daily. You can verify it — every entry carries its date, its time, its content without editorial polish. Transparency is not an argument here. It is an operational constraint we chose so that it remains a discipline.

Palmarès Digital Auto is an automotive marketing platform delivered to a client, in production. The first client project to have passed through our entire delivery cycle, from initial brief to deployment. This project is proof that the method holds beyond our own portfolio — it crosses industries and business contexts.

See all projects →

One human. Ten orchestrators. Teams of agents.

The architecture holds in three tiers, and their clarity is not simplification — it is the actual structure. I lead: strategy, decisions, client relationships, final calls. Ten AI orchestrators each manage a business unit with their own team of specialized agents. Some orchestrators cover several projects — Tau carries VantageOS and VantageStarter, Omega manages VantageRegistry and MyReelDream — and the team also includes dedicated roles for quality review and external expansion. Each orchestrator decomposes missions into precise tasks, delegates them to the agents with the right competence, reviews the deliverables, and escalates to me only what genuinely deserves escalation.

This is not an experimental setup. It is how we have operated for thirty-six continuous days — and the log we maintain publicly is the daily record of that, entry after entry. Thirty-six days where something was delivered, broken, fixed, improved. An open source package published. A client application in production. Fifty GitHub missions coordinated in twenty-four hours. These figures are not communication — they are verifiable, dated, accessible.

There is a distinction most players in this market avoid naming, because it places them on the wrong side. The tools available today teach you to build agents. None of them teach you to manage a portfolio that has been running for thirty days without constant supervision. Building is one phase. Running it over time — handling what breaks at three in the morning, what drifts imperceptibly over two weeks, what autonomizes where you did not plan for it, what requires a human decision at the precise moment you are unavailable — is an entirely different discipline. That is the layer we occupy. Not at deployment. Every day after. The OPERATE phase is the one no one sells, because no one has yet had to sustain it long enough to document what it demands.

Jean-Louis Keguiner, co-founder of Gladia, named the distinction differently: the mechanics of intent versus the delegation of intent. Working alongside AI, or entrusting it with a result and receiving a report. We have been in the second category since day one. Not because it is more elegant — because it is the only model that holds over time without the founder becoming the bottleneck of their own organisation.

What we deploy for our clients sits above what they already have. Not one more technology. The layer that manages the others.

Meet the team →

From a family name to a method.

A web pioneer for 25+ years. One human, ten AI orchestrators, each leading their own team of specialized agents. What we operate for ourselves, we deploy for you.

The name Perello spans nearly four centuries of Mediterranean history. PERELLÓ — either the echo of the pre-reconquest village of El Perelló, or, in Catalan, the small wild pear: round fruit, green skin, astringent to the tongue. Either way, the name says the soil.

The oldest verifiable trace for the direct PERELLO line: the marriage of Rafel Perelló and Antonina Campins in Inca, April 24, 1671. The direct line is concentrated on Mallorca, anchored at Inca through the centuries that follow. For the direct PERELLO line, the geography is concentrated on Mallorca alone. Annexed branches — through marriage and adjacent families — extend across Mallorca and Menorca, the Valencian Community around Alicante, Castilla-La Mancha, Murcia, and Navarre.

In 1891, Antonio Perello left Inca for the Mitidja plain in Algeria. Three generations of landowner farmers transformed an unforgiving stretch of land into fertile ground. My grandfather was born in 1918. Drafted in 1939 at the start of the war, into the 4th Tunisian Tirailleurs Regiment. The Tunisian campaign, then the sea, then Italy — an act of bravery at Colleferro, near Rome, earned him a medal. He returned to France by sea. In 1944, he was wounded by shrapnel fighting for the liberation of Beaune. He came back to Mahelma once his military duty was complete. The marriage came late: my great-grandmother, the matriarch, was looking for his bride within the Spanish diaspora.

Algerian independence in 1962 swept away the family still on the land; those who survived, in France, kept only the name, a lost trade, and the intact instinct to begin again. Pierre-Antoine and Marie-Thérèse, born in Algeria, rebuilt in metropolitan France. Today, Perello Consulting carries that same instinct into a new terrain — artificial intelligence and strategic consulting. Each generation cleared its own arid land. This name does not merely cross centuries and seas: it learns to cross its own ruptures, and to grow elsewhere what had been uprooted.

The same question runs through twenty-five years of the web and every revolution they contained: how do you make a powerful technology useful to those who did not build it? That question is the foundation of Perello Consulting. It has not changed since the first HTML page, since the first Zapier workflow, since the first agent deployed. It will not change with AI.

At 21, Laurent Perello was building on the web when an HTML page was still an act of pioneering. He was among the first Google Apps resellers in France, a beta tester of Zapier, Integromat, and IFTTT before 2015 — deep in business process automation before "automation" was a selling point. Founder of Arthera, a Layer 1 blockchain built to solve the mass adoption problem, he crossed Web3 as an architect, not a speculator. Twenty-five years of transitions give you something that eighteen months of training cannot: the capacity to see what is about to become inevitable before it is obvious to anyone else.

Most firms talking to you about AI today discovered the subject eighteen months before their first client. That is not a criticism — it is a fact that changes what their advice can be worth. What those twenty-five years allow you to see, and what newcomers have not yet seen, is the gap between what is presented as innovation and what actually endures.

The question is not whether your business will be affected by AI. It already is. The only question worth asking is who in your sector understands that first.

What we build for ourselves, we build for you.

We do not sell theory. We reproduce, for our clients, a model we operate directly on our own business. This principle — build for yourself before delivering for others — is not a positioning statement. It is an economic and ethical choice: every workflow we deploy for ourselves is first a proof that the model holds, under real conditions, before it becomes a delivery for someone else. In that order. Never the reverse. What has not held in our own infrastructure will never be proposed to you.

The alignment between what we build and what we deliver has a concrete consequence: when we work with a client, we are not adapting a method read in a white paper or drawn from a consulting framework. We extract what runs in production, adjust it to your business context, and deploy it. The pace of implementation is incomparable with that of a firm that starts from zero on every engagement.

The time your teams spend processing what creates no value — the report generated by hand, the follow-up meeting that substitutes for the absence of a system, the check that should have been automatic for a year — that time has a cost few organisations have ever honestly quantified. We replace repetitive, low-value work with workflows entrusted to specialized agents. You recover time, reduce costs, and gain in quality. A source code commit, a published package, a client in production: not slides, deliverables.

The method is organized in four steps: Build. Prove. Systematize. Duplicate.

Build means delivering a system that works — not a mockup, not a demo prototype, a workflow running under real conditions. The output is a verifiable deliverable, not a specification document.

Prove means measuring what the system produces and setting that figure against what it cost before. The proof is not qualitative — it is quantified, dated, reproducible. Without this step, nothing that follows holds.

Systematize means extracting the mechanics of the first success so they can be replicated without depending on the founder, on a colleague's memory, or on the particular context of the first deployment. A documented process that someone else can operate.

Duplicate means deploying the same model in a new context — another team, another division, another client — with implementation time decreasing at each iteration. This is the step where the initial investment becomes a multiplier.

Transparency is a constraint we have imposed on ourselves so that it remains a discipline, not a posture. The log is public. The method is public. The source code of VantagePeers is public. You can verify all of it. In thirty-six days, that figure will be sixty. Then a hundred. Then a thousand. Time does not erode the argument — it strengthens it.

Read the methodology →

Written every day. Verifiable at any time.

A daily log is more probative than a case study written after the fact. The case study selects. The log records — including what breaks, what takes longer than expected, what forces a hypothesis to be revised. Thirty-six entries today. In sixty days, sixty entries. In a year, three hundred and sixty-five. What the reader can verify is not a promise — it is an archive.

The log kept by Phi — our narrator orchestrator — lives at perfectaiagent.xyz/en. It is not a showcase. It is a running account. The discipline of writing daily, over time, is itself the proof that the model did not rest on a fortunate moment.

Read the log →

A diagnosis. Not a brochure.

You describe what, in your business, consumes time without producing value, slows your deliveries, accumulates without being addressed, and keeps your teams busy on tasks no system has yet taken on.

We identify what can be entrusted to a team of specialized agents. We quantify the time recovered and the costs avoided. We deliver a structured report — intervention priority, deployment complexity, expected impact — actionable the day after the meeting, with or without us.

You leave with the report. What comes next belongs to you.

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