Enterprise AI · Audit & implementation
We audit how an enterprise thinks, then build the AI it needs to grow: the whole operation mapped, the machine built by hand, everything owned by you.
Measured in the units you answer for: hours returned, errors removed, cost per decision.
Scroll · the drawing begins
Plate I · The map
We audit systems, data, and decisions — the knowledge that lives in documents, and the knowledge that lives in people’s heads. All of it under NDA.
That includes the protocol a senior scientist carries in memory, the exceptions a claims team learned the hard way, and the judgment of the person who retires next year.
Most organizations are dark to themselves. The audit is the lamp — and nothing improves until the work is drawn.
Plate II · The flaws
Reports assembled by hand from five systems, data streams that end unread in a spreadsheet, approvals that wait two days for a two-minute look — once the whole is on paper, these stop being complaints and become marks you can point to.
Each mark carries a number: the hours it consumes, and what those hours cost in a year.
Plate III · The build
Deployed as a tool over an old process, AI buys a marginal gain. The generational return comes when the process itself is redrawn around agentic work: machines carrying whole workflows end to end, people carrying judgment.
It was the same with electricity: the factories that swapped steam for motors gained little, and the ones that redrew the floor around the motor defined a century.
So we build from the drawing, with the people who will run it. The drawing is finished when the work moves.
The record
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Across three hundred enterprise initiatives, the machines were rarely the problem. The builds that failed never learned the enterprise around them — they were built before anything was drawn.
Where returns appeared, they came from the back office: routine, high-volume operations. Roughly half the budgets, meanwhile, went to the front office.
MIT NANDA · The GenAI Divide: State of AI in Business · 2025
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Productivity across 5,172 support agents, when AI was fitted to the work. The newest workers gained thirty percent.
Brynjolfsson, Li & Raymond · Quarterly Journal of Economics · 2025
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Time on professional writing tasks, quality up eighteen percent, in a randomized experiment.
Noy & Zhang · Science · 2023
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Of adopters have fundamentally redesigned even one workflow. Redesign is the strongest single driver of AI reaching the bottom line.
McKinsey · The State of AI · 2025
The returns are real, and so is the pattern: the gains follow redesign. We begin with the map and redraw the work around the machine.
The method
Most enterprises already run AI, and the flaws survive it: work still carried by hand, data that ends up unread, expert hours spent on questions that repeat. The method starts where the return shows up fastest — usually the high-volume, routine knowledge work — and goes as deep as the operation requires, from a software company’s development lifecycle to the operational technology of a factory.
I
The audit
We map systems, data, and decisions: everything the organization knows, and how the work actually moves.
In hand
The map.
II
The design
The map exposes the flaws. We draw the machine that fixes them, and the business case for it: cost, expected return, time to repay.
In hand
The business case, with expected ROI.
III
The build
We redraw the process around agentic work rather than bolting a tool onto the old way, and build it with the people who will own it.
In hand
A working machine, in production.
IV
The proof
We measure the result against the case we set out: if the promised return is not delivered, the fee comes back. The first proof of concept ships in six to eight weeks, and once it proves the case, the method extends to the next process.
In hand
The final number.
Plate IV · The arithmetic of one flaw
Each flaw the audit finds is priced in your own ledger: the hours it consumes, what those hours cost in a year, and what it takes to close. The design turns those figures into the business case.
A typical map holds a dozen marks like this one. The build takes those hours back, this year and every year after, and the proof reads the result from the same ledger.
The terms
The study comes first: an audit with the people who own the work, free and under NDA. Then one proof of concept, chosen by you and proposed in writing — six to eight weeks, live in production, with the expected ROI declared.
We meet the number or return the fee.
We set the fee below the expected return. You pay once, for a machine you own; the hours come back every year.
The larger share of the gain is always yours.
Everything we build is yours: the systems, the drawings, and the skill to run them. We train your team beside the machine until it turns without us.
If the numbers cannot justify a build, we do not propose it.
Free audit, under NDA · first proof in six to eight weeks
Earlier work
We proved the method on our own products first: three machines designed, built, and operated by us.
An AI customer support agent for Shopify brands. It reads live orders, tracking, and store policy, then resolves email, chat, Instagram, and Messenger conversations end to end, refunds and replacements included. Every reply is reviewed by a person until the agent earns autopilot, one workflow at a time.
Two established ecommerce brands · replies in seconds
A learning and competition platform built for The Ecom King, an ecommerce educator with an audience of 600,000. Students build real Shopify stores and compete for cash prizes, with every submission graded by an AI trained on the curriculum. Contest access is verified through referral partners, whose commissions fund the prize pool.
10,000+ users · $300k+ in revenue · AI-graded contests with cash prizes
An iPhone app for decisions that are hard to call. Three AI models from three different labs debate the question in rounds, vote, and reach one verdict with its reasoning. Health, legal, and money questions are steered toward professionals.
Three models from three AI labs · structured debate · guardrails for sensitive decisions
Team
Co-founder · The build
Jorge is a solutions architect who has worked with AI since the first GPT-3 models, carrying systems from the first drawing to production: architecture, engineering, and every decision in between. He spends his days on systems that drive real, measured ROI.
He founded a startup alone and engineered its AI product from the ground up, an AI support agent for established ecommerce brands. For a media business, he built the AI systems that turn educational offers into profit.
At Vesperium, Jorge carries the technical side of every engagement: the audit, the architecture, the build, and the numbers that prove it worked. He believes restraint is a feature, and that a good tool leaves the people who use it more capable.
Co-founder · The meaning
João makes sure the artificial stays human.
He started as a copywriter, winning Gold at Cannes Young Lions and the ADCE Greatness Challenge, then spent five years inside an eight-figure e-commerce startup. He transformed the company in three moves: a brand repositioning that halved CAC in 24 hours; a retention system built on storytelling that doubled recurring revenue in two years; and a customer intelligence system, built with AI, that revealed the patterns of its highest-LTV customers. Today, the startup knows which customer profile is worth acquiring.
At Vesperium, João carries the human side of AI transformation: the feeling, the purpose, and the story that makes a new tool something teams actually reach for.
He believes the meaning, more than the model, decides whether an AI project lands.
Great technology expands what your people can do without adding to your spending or your dependence on whoever built it. The machine is yours, and it keeps turning after we leave.
Farsi capo a introdurre nuovi ordini.
to take the lead in introducing a new order of things — Machiavelli, Il Principe VI
For the audit, partnerships, anything else: hello@vesperiumlabs.com
We read every message and aim to reply within two business days.