Your company already uses AI. But is it paying off?
If the answer is "I don't know", the problem isn't your team. It's that no one has organized the way.
Sfida gets your company ready for AI: from one agent for one area to the whole operation, with teams who know how to use it and information AI can understand.
AI is available. Is your team getting the most out of it?
Everyone uses it their own way. No one really knows what is being done, or whether the output can be trusted. And when leadership needs a number, they still wait for someone to build the spreadsheet.
of Brazilian executives surveyed say their company uses AI only on an ad hoc basis.
Panorama Liderança 2026, by Amcham Brasil and Humanizadas, presented in June 2026. 629 executives from different sectors in Brazil. 84% say their company uses AI only on an ad hoc basis.
of companies surveyed worldwide can point to any effect of AI on profit.
The State of AI in 2026. Fielded May to June 2026, with 1,719 respondents in 97 countries.
of work hours is what AI saves when each person uses it on their own. When the company encourages its use, that gain can be up to 40% larger.
Humlum and Vestergaard, University of Chicago, May 2025. About 25,000 workers in Denmark, self-directed use between 2023 and 2024. Gains are 10% to 40% larger when the company encourages its use.
The difference is in how the team uses it
When AI goes into the right tasks, work gets done 25% faster. Sfida organizes that use, area by area.
Dell'Acqua et al., Harvard, MIT, Wharton and BCG, Sep 2023. Controlled experiment with 758 BCG consultants.
Where to start: one agent or the whole company
We don't build a new AI. We teach the AI your company already uses to work with your rules, your documents and your numbers.
An agent for one area
Sales, customer service, finance, marketing.
You choose the function. We build an agent that knows how that area works and answers from its sources, inside the tool your team already uses. It is a short project, and it can be the first step.
The whole company
Teams that know how to use AI, organized information and an executive agent for leadership.
This is the full implementation, described below.
On either path, the agent goes through the same test before it reaches your team.
Assess my companyAgents your company can rely on
This holds for every agent we build: the promise is not that it never makes mistakes. It is that every error can be checked.
- Every answer cites its source. You check it.
- When it has no basis to answer, it says it doesn't know.
- When two sources disagree, it shows the difference instead of picking one.
- Before it is released, it is tested with questions whose answers you already know.
Testing matters: used on the wrong tasks, AI cut consultants' accuracy by 19 percentage points.
Dell'Acqua et al., Harvard, MIT, Wharton and BCG, Sep 2023. Controlled experiment with 758 BCG consultants.
The full implementation
It takes 4 to 6 months, in stages. The exact timeline comes out of the diagnostic and depends on the company's pace.
-
Stage 1
Diagnostic
We talk with leadership and with each area to understand how you work, where information lives and which questions leadership can't answer today. This produces the implementation plan.
-
Stage 2
Teams
We set up each area's projects inside the AI you already use, with precise instructions, automated routines and skills for recurring work. And we train the team to keep creating its own routines after we leave.
-
Stage 3
Data
We define the official source of each number, where it lives and who maintains it.
-
Stage 4
Executive agent
We build the agent and test it with leadership questions you already know how to answer. It is released after validation.
The agent reads the company's sources, and the teams are the ones who keep those sources alive. Without routines in the teams, information goes stale and answers get worse.
The system is yours and keeps running without us. If you want, we keep working together through Ongoing support, which is optional. Claude and ChatGPT change all the time: we track those changes, adjust your system, keep the company context up to date and create new routines when the need arises.
Agents that talk directly to your customers, such as customer service on WhatsApp, are quoted separately.
When the work requires access to company data, it is covered by a non-disclosure agreement (NDA).
What your company gets
With the full implementation, your company ends up with four things.
A team that knows how to use AI in its own work
Each area comes away with AI set up for its work and with ready routines for what repeats every week. People learn to ask and to check what they get back. The least experienced gain the most: among novice support agents, productivity rose 34%.
Brynjolfsson, Li and Raymond, Quarterly Journal of Economics, 2025. 5,179 support agents at a US software company, with real operating data.
Your company's context, ready for AI
Today, every conversation with AI starts from zero. It doesn't know who you are, how you work or what each metric means. We organize that context into instructions, glossaries and maps any AI can understand. It's yours, and it works in any tool.
Data traceability
Every key number gets an official source and a person responsible for keeping it current. You can tell where each answer came from, and no one has to ask which spreadsheet is right anymore.
An executive agent for leadership
You ask about the company and get the answer with its source, so you can check it. It runs analyses and projections from official data and, when the basis is missing, says it doesn't know.
Lívia Troise
Lívia spent four years as CEO, accountable for the results of an entire company. She took over a financially fragile business, restructured its operations, led up to 35 people across product, technology, CRM, marketing and operations, and took the company through a merger.
Since the merger, she has been Chief Innovation Officer: she built the company's data architecture and AI governance rules from scratch and brought AI into every area's routine. She led resistant teams to the point where people began creating their own routines. Sfida was born from that work: a real implementation that she planned, led and carried out.
The discipline comes from management: before deciding, make sure the rules of the operation are correctly reflected in the numbers. It is this management perspective, not a technology one, that she brings to every implementation.
At Sfida, that management perspective comes with applied AI engineering: context engineering, to organize what AI needs to know about the company; agent building, with instructions, skills and connected sources; and evals, tests against an answer key before every release and every platform change.
Let's start with your company
Before the first conversation, we ask for some information about your company, your teams and how you already use AI. That way, the conversation starts with your case.
From a consultancy or a technology team? We also build agents in partnership.