We find where your team's hours go, redesign that process with AI, and build the microservices that keep it running. We start with one concrete case and measurable results.
Six capabilities that run from assessment to daily operation. Each one exists so the automation holds up in everyday use instead of stalling as a pilot.
We map how your team works today and measure where the hours go. Before automating, you need to know what is worth automating: not every slow process deserves a model behind it.
Classification, extraction, drafting, and summarization applied to tasks that are done by hand today: emails, documents, tickets, meeting notes, and forms.
Small, deployable services that solve one specific task and connect by API to what you already use. They are built in weeks, not quarters, and can be replaced without touching the rest.
Everything we build reads from and writes to your CRM, your ERP, or your database. AI without access to your data produces polished text; connected to your operation, it produces results.
We define the metric before we start—hours saved, response time, error rate—and measure it afterward. If an automation does not move the number, it gets adjusted or retired.
We define what information can leave your organization and what cannot, with access control, usage logging, and human review at the points where a mistake has a cost.
Almost no AI project fails because of the model. It fails because the wrong process was chosen, because no person was placed where a mistake has a cost, or because it was never measured again.
Criterion 1 of 3
Not every slow process deserves a model behind it. Before we build anything, we map the tasks, measure how many hours they consume each month, and rank them by impact against effort. The first case comes from that quadrant, and it is rarely the one that looked most appealing in the meeting.
Criterion 2 of 3
We build small pieces that do one thing and connect by API to what you already use. Where a mistake has a cost—a quote, a tax figure, a message that goes out to a customer—the flow stops and waits for human approval. Where that checkpoint sits is decided by the cost of the error, not by how much anyone trusts the model.
Criterion 3 of 3
An automation that worked in March may have stopped working by August because the input data, the catalog, or the model changed. We define the metric and the threshold before building, and we keep measuring them in production. If the number falls below the threshold, it gets adjusted; if it cannot be sustained, it gets retired.
Concrete examples of processes that consume hours today and can be solved with intelligent automation.
What gets automated lives inside each company's operation and cannot be shown in a screenshot, so here is the name instead.
Grupo Upax
upax.com.mx
Five stages designed to prove the value before you commit a large budget.
Interviews with the people who run the process, and observation of how they actually work. We come out with a map of tasks, times, and friction points, ranked by impact and by difficulty.
We pick one narrow case and solve it end to end with real data. The goal is not to impress; it is to confirm whether the solution holds the quality your operation needs.
We develop the microservices and the automations, integrate them with your CRM and your systems, and define the points where a person must approve before anything runs.
We train the team, document what each piece does, and make clear what to do when something fails. An automation nobody understands gets abandoned at the first error.
We monitor quality and cost per operation, adjust what degrades, and extend to the next processes on the priority list.
One process at a time, with the success criterion defined before we start. If it is not met, the lesson is a cheap one.
The proof of concept is delivered in 2 or 3 weeks with your real data, not with a demo example.
We hand over the code and the documentation for everything we build. If you decide to run it with your own team tomorrow, you can.
It means redesigning how your company runs a process, using artificial intelligence models for the parts that consume human time without adding judgment: reading, classifying, extracting, drafting, and moving information between systems. The point is not to replace your team, but to take off their plate the work that does not require their judgment.
A microservice is a small, independent component that solves a single task and talks to the rest of your systems through an API. It helps because it is built and tested in weeks, it can be replaced without rebuilding everything, and it lets you start with one specific process instead of committing to a year-long project.
No. We work with what you already have: HubSpot, another CRM, an ERP, or even databases and spreadsheets. What we do need is for the information to be reachable through an API or exportable in a consistent way; if it is not, organizing it becomes the first stage of the project.
During the assessment we define which data can be processed outside your organization and which cannot. Depending on the case, we use models with no-training agreements on your data, anonymization of sensitive fields, or deployments where the information never leaves your infrastructure. The rules are put in writing before anything is built.
Every automation is delivered with a quality metric measured on real cases, and with mandatory human review at the steps where a mistake has a cost. We also log every operation so it can be audited, and we monitor quality over time, because models and data change.
With a process that repeats many times a month, consumes people's hours today, and where the result is easy to verify. Those three criteria together make the return obvious within weeks and give the team confidence for the next project.
Ready to grow?
Tell us about it in a 30-minute call. We come out of it with a prioritized list of what to automate first and why.
Or email us directly at contacto@c2suite.com