Enterprise AI · Audit & implementation
Kinetic imagination.
We audit how an enterprise thinks, then build the AI it needs. Mapped in full. Implemented by human hands. Owned by yours.
Measured in the units you answer for: hours returned, errors removed, cost per decision.
Everything your organization knows, drawn as one map.
Systems, data, decisions. The knowledge that lives in documents and the knowledge that lives in people's heads. We survey all of it.
The protocol a senior scientist carries in memory. The exceptions a claims team learned the hard way. The judgment of the person who retires next year.
Most organizations are dark to themselves. The survey is a lamp. Nothing improves until it is drawn.
The map shows where the work goes wrong.
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. Experts spending their hours on questions that repeat. Once the whole is on paper, these stop being complaints. They are marks you can point to.
Each mark carries a number: the hours it consumes, and what those hours cost in a year.
flaws marked in sanguine
A · work carried by hand B · data nobody reads C · experts on routine questions
Then we build, by human hands.
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.
Electricity taught the same lesson: the factories that swapped steam for motors gained little. 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.
gear and pinion: one turns the other
95%
of organizations investing in generative AI report zero return.
Three hundred enterprise initiatives, reviewed. The machines were not the flaw. 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.
MIT NANDA · The GenAI Divide: State of AI in Business · 2025Productivity 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 · 2025Time on professional writing tasks, quality up eighteen percent, in a randomized experiment.
Noy & Zhang · Science · 2023Of adopters have fundamentally redesigned even one workflow. Redesign is the strongest single driver of AI reaching the bottom line.
McKinsey · The State of AI · 2025The returns are attested. The failures were bolted on. We begin with the map, and redraw the work around the machine.
Most enterprises already run AI. The flaws survive it: work carried by hand that a machine should carry, data streams that run loose and end unread, expert hours spent on questions that repeat. The deepest returns are rarely the spectacular ones. They come from high-volume, routine knowledge work: reading, drafting, checking, moving information, taken over end to end. The method aims there first.
We map systems, data, and decisions: everything the organization knows, and how the work actually moves.
In hand: the map.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.Not a tool bolted onto the old way. The process redrawn around agentic work, built with the people who will own it.
In hand: a working machine, in production.We measure what returned against the case we declared. What cannot be measured is redrawn.
In hand: the ROI, measured.Every flaw on the map carries a number.
AI is mostly sold as spectacle. We sell arithmetic. Each flaw the survey finds is priced in your own ledger: the hours it consumes, what those hours cost in a year, what it takes to close. The design delivers the case. The proof reads it back.
- One team, re-answering what the organization already knows
- 40 people
- Hours lost to it, each person, each week
- 5
- Hours gone, in a year
- 10,400
- Each year, at €60 an hour
- €624,000
Ostinato rigore. Obstinate rigor, Leonardo's motto.
A typical map holds a dozen marks like this one. The survey finds them. The build takes the hours back, this year and every year after. The proof is the same ledger, read again.
The method runs twice. First as the study: eight weeks, a fixed fee, one workflow, one business case with the expected ROI in writing, one measured return, live in your operation by week eight. Then as the fresco: the same four movements, across the enterprise, once the numbers hold.
We price against the return, and beneath it: the larger share of the gain is always yours. A machine you own keeps earning it. The hours return every year; the fee is paid once.
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. A build the numbers cannot justify is a build we do not propose.
We prove the method on our own products first. Three machines, designed, built, and run in production.
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Squire
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 · 30%+ of support resolved end to end · replies in seconds
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The Ecom King Vault
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
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Council
An iPhone app for decisions that are hard to call. Three AI models from three different labs argue the question in rounds, vote, and land on 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
Jorge Vieira
Co-founderJorge is a solutions architect. He has worked with AI since the first GPT-3 models, and carries systems from the first drawing to production: architecture, engineering, and every decision 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 large media business, he built the AI systems that turn educational offers into profit.
At Vesperium, Jorge carries the technical side of every engagement: the survey, 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.
João Elias
Co-founderJoão makes sure the artificial stays human.
He started as a creative writer, winning Gold at Cannes Young Lions and the ADCE Greatness Challenge, then spent four years inside an eight-figure DTC startup. He transformed the company in three moves. First, a brand repositioning that halved CAC in 24 hours. Second, a retention system built on storytelling that doubled recurring revenue in two years. And third, he used AI to build a customer intelligence system that revealed the patterns of its highest LTV customers. Today, the entire company knows who's worth acquiring and who isn't.
At Vesperium, João carries the human side of AI transformation: the feeling, the purpose, and the story that makes a new tool something a team actually reaches for.
He believes most AI projects fail not on the model but on the meaning.
For the survey, partnerships, & anything else: hello@vesperiumlabs.com.
Built to run without us
Farsi capo a introdurre nuovi ordini. to take the lead in introducing a new order of things. Machiavelli, Il Principe VI