AI agents for businesses
We design and launch AI agents that work on your real processes: they answer customers on your website and WhatsApp, act as a receptionist, qualify leads, read documents and update your CRM. Every agent has clear limits, says it is an AI and hands off to a person whatever it should not resolve alone.
Who it is for
An agent makes sense when there is repetitive work someone already does by hand and that can be explained step by step.
Businesses that serve customers on WhatsApp and the web
Clinics, academies or service companies that get the same questions at all hours and cannot always reply in time.
Sales teams
Leads coming in from several channels that need qualifying, scoring and moving into the pipeline without anyone doing it by hand.
Document-heavy companies
Knowledge bases, contracts or records the team checks every day and an agent can read for them.
Marketing and content teams
Repetitive production, such as editing videos, preparing posts or drafting articles, that an agent can get ready for review.
The agents we build
Website and WhatsApp support chatbot
Answers with your company's real documentation, remembers the conversation and hands off to a person when it detects a complaint or something outside its scope.
AI receptionist
Handles what your front desk would: services, opening hours and how to book. It never makes up prices or availability; if it does not know, it takes the details and alerts the team.
Lead qualification agent
Takes the form, researches the company, scores it against your ideal customer and places it in the right pipeline stage.
Prospecting agent
Finds companies by area and sector, analyzes their website and technology and builds the list of opportunities. It is the tool we use to find our own clients.
Document agent
Reads invoices, contracts or records, extracts the fields that matter and puts them in a table or in your system.
Content agents
Reel editing, carousel preparation and article drafting that wait for human approval before anything is published.
How to tell what you need
Many projects fail because someone asked for one thing and needed another. An agent, a chatbot and an automation look alike in demos and behave very differently in production.
Our rule is simple. If the process can be written as a list of fixed rules, an automation is enough, and it is cheaper and more predictable. If understanding language is needed to respond, a chatbot. And if it also has to decide and act on your systems, an agent. Once an agent can write to your CRM you are no longer testing a tool: you are putting software into production, and it gets designed that way.
We cover it in depth in AI agents for companies: what they really automate and what they don't. If you are after a broader AI plan for the company, start with AI implementation.
Connected to your CRM, WhatsApp and email
An isolated agent is of little use. The value comes from it working where your team already works: reading and writing to the CRM, replying on WhatsApp or by email and keeping a record of what it does. We integrate with GoHighLevel, HubSpot or other systems via API, and with n8n or Make flows when several tools need to be joined.
In the Big Dream Recruiting case, the chatbot answers using more than ten of the business's documents and, when it detects intent, moves the contact into the CRM sales flow with email and WhatsApp. If you do not have a well-organized CRM yet, start there: we do it in CRM implementation and migration.
Try it with your own business
For customer-facing businesses such as clinics, we can prepare an AI receptionist demo using the public information on your website: your services, your hours and how to book. You test it with your own questions before deciding anything. It only answers from that information, says it is an AI if asked and, when something is not in its knowledge base, offers to take the details so the team can call back.
And if you first want to know what AI assistants say about your business, the free AI visibility audit shows you in ChatGPT, Gemini, Perplexity and Claude.
Limits, human oversight and the EU AI Act
A well-designed agent knows what it must not do. We define what it can execute on its own, what has to go through a person and what happens when it gets something wrong: how it is detected, who reviews it and how it is fixed. Sensitive actions, such as emailing a customer or touching an invoice, get human review.
Since August 2, 2026, the EU AI Act requires telling people they are talking to an AI, and we account for it from the design stage: the chatbot on our own website says so in its first message. We also document which data each agent processes and with which model, which is a GDPR question before it is a technical one. More context in what the EU AI Act already requires.
AI agent, chatbot or automation
All three have their place. This table sums up when each one fits.
| AI agent | Chatbot | Automation | |
|---|---|---|---|
| What it does | Understands the request, decides by your rules and takes action in your systems | Converses and answers with the information you give it | Runs fixed steps when something happens |
| Example | Qualifies a lead, scores it and puts it in the pipeline | Answers questions about services, hours or requirements | When a form comes in, creates the contact and alerts the sales rep |
| Uses AI | Yes, to understand and decide | Yes, to understand and write | Not necessarily |
| When it gets it wrong | Needs limits and human review on sensitive actions | A weaker answer; escalates to a person | Fails in a predictable, easy-to-spot way |
| When it fits | Multi-step processes someone already does by hand | Lots of repeated questions on the web or WhatsApp | Clear rules that do not change |
How we work, phase by phase
Pick the process
1 to 2 weeks
We find the most repetitive, easiest-to-measure process and write it down step by step. If nobody can explain it, it is not ready to automate.
Design and proposal
3 to 5 days
What the agent will do, with which tools, what its limits are, what it will cost to run and how we will measure the result.
Build and integrate
3 to 6 weeks
We connect it to your CRM, WhatsApp, email or documents and test it on real cases before opening it up.
Launch and tune
Ongoing
It starts under supervision; we review its answers and actions and adjust the rules. Then the next process.
What you get
- The agent in production, connected to your channels and systems.
- A knowledge base with your documentation that your team can update.
- Written escalation rules and limits on what it can do.
- An estimate of the monthly running cost based on your real volume.
- Traceability of what the agent does, so it can be reviewed and corrected.
- Training for the people who supervise it.
How an agent is priced
There are two costs and it pays to separate them from the start. Implementation is a one-off cost that we set per scope once the process is chosen. Running cost is monthly and variable: it depends on the model the agent uses and how often it works.
We estimate that second cost with your real volume, not an example, and choose the model for the case, from OpenAI, Anthropic or another provider. It is the bill that surprises people most when nobody has worked it out in advance.
What it does
for you.
- Service at any hour without growing the team.
- Leads qualified and in the CRM before the sales rep opens their inbox.
- Your team drops the repetitive work and keeps the exceptions and the customer relationship.
Related case studies
Real projects where we have applied this service.
- Sales chatbot
A chatbot that qualifies leads while its owner sleeps
Trained on more than ten of the business's documents and connected to the CRM, email and WhatsApp.
View the case - Support assistant
An assistant that has read the thousand documents nobody reads
More than 1,000 vectorized files and advanced search over university databases, at any hour.
View the case - Prospecting
The tool we use to find our own clients
Finds companies, analyzes their website and technology and scores them as sales opportunities.
View the case
Further reading
AI agents for companies: what they really automate and what they don't
The four processes where they work today and the three reasons they fail.
Read the articlen8n, Make or Zapier: which one to choose
The tools that connect agents to your systems.
Read the articleThe EU AI Act: what already applies to you
Transparency, human oversight and what got pushed to 2027.
Read the article
Every project is run by the same team, with the technical side led by Carlos Dodero, lecturer in Artificial Intelligence at Universidad Alfonso X el Sabio. Meet the team or, if you want to start with something concrete, request the free AI visibility audit: we show you what ChatGPT, Gemini and Perplexity say about your business.
Frequently asked questions
A system that, besides generating text, takes action: it queries a database, decides by business rules, updates a CRM and triggers the next step. Technically it is a language model with connected tools and permission to use them, plus the rules that decide when.
An assistant that serves people on your website or WhatsApp the way your front desk would: it answers about services, hours and how to book, and collects the details of anyone who wants an appointment. It only uses the information you give it; if asked something it does not know, such as an unpublished price or a medical opinion, it says so and offers a call back from the team.
A bounded case with the process already documented takes three to six weeks. If systems that do not expose their data have to be integrated, the integration sets the timeline and it can stretch to two or three months.
CRMs such as GoHighLevel or HubSpot, WhatsApp, email, calendars and document stores, via API or with n8n and Make flows. For reasoning we use OpenAI and Anthropic models depending on the case.
It replaces tasks, not people. What goes away is the repetitive work (qualifying, extracting, summarizing); what stays is judgment, the customer relationship and the exceptions.
It is decided in the architecture: which personal data the agent handles, on what legal basis, in which model it is processed and where it is stored. Rebuilding a system later to comply costs far more than designing it right from the start.
Ready to work together?
Schedule a meeting or tell us about your project. We respond quickly, no strings attached.