means four artificial intelligence services in which your company checks the source and the result
Company brain, for the management that asks about the margin and wants to see the report it comes from. Expert judgement, for the work that only one person knows how to do today. Process automation, for the task that eats up the hours of many people, with the target in the contract. AI visibility, for the company its competitors overshadow in AI assistants.
How do I capture an expert partner's judgement in an artificial intelligence system?
With Expert judgement, your partner's review becomes a : what they look at, what they accept and what they send back, written so that the machine can follow it. It does the whole task, is tested against work your team has already approved and delivers nothing without the partner's sign-off.
Expert judgement · what a skill is
A skill works like a new employee's onboarding, with the manual, the checklist and the templates, and with two differences: a machine reads it, and it holds the same standard on the first piece and on the ninetieth. That way the new team works with the partners' judgement, even when the partners are not there.
What sets it apart from a downloaded template is that it recognises when the rule gives way, because it compares against your company's documents: it flags what needs correcting and stays quiet about what does not. The skill belongs to your company, written in files anyone can open.
The method was born in ten years of legal practice, reviewing files with an expert's judgement before filing them.
| The problem | What the skill does | How it is checked |
|---|---|---|
| Turning around quotes faster | Every proposal goes out with the usual commercial judgement | Against proposals that already won |
| Reviewing more documents with the same team | Every document is reviewed to the same standard | Against files the expert already approved |
| Cutting rework | Errors surface before delivery | Returned pieces, before and after |
How do I automate an administrative process with AI?
With Process automation, the first step is knowing what the process costs you today, in hours and in pesos, and writing down the the system has to move. At the end it is measured with the same method, and if the target falls short by the agreed deadline, the contract says what is refunded.
Process automation · what is worth automating
It is worth automating the process that repeats, passes through many hands and has a result that can be counted. A process that takes less than about 150 hours a month rarely pays for its build within the first year; when that is the case, TintoAI says so and proposes something smaller, or leaving it as it is.
What TintoAI builds works in batches, like the overnight match or the morning reconciliation, and a failure is fixed the next day without the end customer feeling it. Whatever must respond instantly, like confirming an online payment, stays with your technology provider, and the proposal says so from the start.
The target and its deadline are written into the outcome guarantee agreement.
| The problem | What is automated | How it is checked |
|---|---|---|
| Meeting the 42-hour workweek without growing payroll | The repeated work that today takes up hours of several people | Hours per month, before and after |
| Shortening the month-end close | The matching and reconciliations that prepare the close | Days to close, before and after |
| Automating bank reconciliation | The daily match of transactions against the books | Items left to review by hand |
| Serving more clients without hiring more agents | Answers to the usual questions, with their source | Queries resolved without reaching an agent |
How do I get AI assistants to cite my company when someone asks about my field?
With AI visibility, the first step is counting how many questions in your field your company appears in, how many another company appears in, and in what position. Then what those questions ask for is published, and the same questions are measured again every month. In the of DD9 Regulatorio, the founder's firm, Perplexity already names it in half of the questions, ahead of all its competitors.
AI visibility · the DD9 Regulatorio case
Someone looking for a provider no longer always opens ten links: they ask an assistant and get three or four names. If yours is missing, the conversation starts with your competitor, and the visit your site was expecting goes elsewhere.
The measurement uses a fixed bank of questions that do not name your company, written the way your client would ask them, and it is run the same way every month in ChatGPT, Gemini and Perplexity. It counts how many answers your company appears in, where it ranks against its competitors and how many times the assistant cites your site.
DD9 Regulatorio is the firm of TintoAI's founder and the first place this strategy was applied, the same one offered to any company. It handles sanitary registrations for medical devices before INVIMA, against firms with decades in the market, and started positioning itself in April 2026. Six months later, in the 6 October 2026 measurement, with 20 questions per platform, DD9 appeared in 10 Perplexity answers and in 4 ChatGPT answers. On both it ranked first against its competitors; on Gemini, second.
| The assistant | Questions that name it | Rank against its competitors |
|---|---|---|
| Perplexity | 10 of 20 | 1st |
| ChatGPT | 4 of 20 | 1st |
| Gemini | 3 of 20 | 2nd |
Source: Wix AI visibility tool, 6 October 2026, 20 questions per platform.
In all four, what you decide before starting is put in writing. Each service publishes its timeline and its price on its own page.
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