# Process transformation with artificial intelligence > 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. - Proof of concept in 2–3 weeks - Integration with your CRM - Impact metrics defined from the start ## We don't sell AI. We remove repetitive work 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. ### Process assessment 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. ### Automation with language models Classification, extraction, drafting, and summarization applied to tasks that are done by hand today: emails, documents, tickets, meeting notes, and forms. ### Custom microservices 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. ### Integration with your CRM and your systems 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. ### Impact measurement 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. ### Data security and governance 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. ## What separates a pilot that lasts from one that gets abandoned 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. ### First choose the process, then the technology 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. - The criteria: it repeats many times a month, it consumes people's hours today, and the result is easy to verify. - A case where correctness cannot be checked is a case where nobody will know whether it works. - We have a public calculator that estimates what that manual work costs you today, area by area. ### One service per task, with a person at the point of risk 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. - They are built in weeks and can be replaced without rebuilding the rest of the operation. - Everything that runs is logged, so an unusual output can be audited later. - We hand over the code and the documentation: if you decide to run it with your own team tomorrow, you can. ### Quality is measured after launch, not only before 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. - The metric is agreed with you before the first line of code, not picked at the end so the result looks good. - Cost per operation is monitored alongside quality: an expensive automation is also an automation that needs review. - Retiring something that stopped being useful is part of the agreement, and that is why it is written here. ## Processes that already run on AI 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 ## Use cases by area Concrete examples of processes that consume hours today and can be solved with intelligent automation. ### Sales - Automatic lead qualification and routing based on what the prospect wrote, not only on the form they filled out. - Automatic summaries of calls and meetings, saved to the matching deal in the CRM. - Draft proposals and quotes generated from the sales rep's notes and your product catalog. - Alerts when a deal has gone too long without movement or shows signs of risk. ### Marketing - Copy variants for campaigns, ads, and emails, generated from your brand tone. - Classification and enrichment of your contact database, so you can segment without depending on empty fields. - Analysis of the questions your prospects actually ask, to decide what content to write. - Translation and localization of content to expand into other markets in Latin America. ### Service and support - Automatic ticket classification by topic, urgency, and owning team the moment they arrive. - Suggested replies for the agent, drawn from your knowledge base, with human review before sending. - Detection of unhappy customers from the tone of their messages, before the case escalates. - Summaries of long cases, so whoever picks up the ticket does not have to read twenty emails. ### Operations and administration - Data extraction from invoices, contracts, and purchase orders into your systems, with no manual entry. - Reconciliation of information between systems that do not talk to each other. - Recurring reports generated and distributed automatically, in the format each area needs. - Validation of supplier and customer documentation against rules your team defines. ## We start small, measure, and scale Five stages designed to prove the value before you commit a large budget. ### Discovery · 1–2 weeks 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. ### Proof of concept · 2–3 weeks 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. ### Build · 4–10 weeks 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. ### Adoption · 2–3 weeks 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. ### Operation and improvement · Ongoing We monitor quality and cost per operation, adjust what degrades, and extend to the next processes on the priority list. ## A pilot that works is worth more than an 80-page strategy ### Narrow scope One process at a time, with the success criterion defined before we start. If it is not met, the lesson is a cheap one. ### Results in weeks The proof of concept is delivered in 2 or 3 weeks with your real data, not with a demo example. ### No vendor lock-in 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. ## Frequently asked questions about AI transformation ### What is AI process transformation? 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. ### What is a microservice and why does it help me? 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. ### Do I need HubSpot or a specific CRM? 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. ### What happens with my company's confidential information? 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. ### How do I know if the AI is getting things wrong? 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. ### Where should I start? 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. ## Which process is costing you the most hours? 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. --- Source: https://c2suite.com/en/services/ai-transformation You can cite and summarize this content if you credit C2Suite and link to the source URL.