AI-Automation
AI process automation: workflows and agents for companies
Automate the work. Stay in control.
AI workflows and agents that do real work, with a person in charge.
When it makes sense
From scattered tasks to connected processes.
We use AI where it improves the result. And only there.
- The team repeats similar tasks at a growing volume.
- Data is copied by hand between emails, spreadsheets and systems.
- You need to cut response times and errors without losing visibility.
Types of solutions
Workflows or agents, depending on the process.
AI Workflows
Step-by-step automations
Triggered by an event: a lead, an order, a ticket. For stable, repetitive processes.
AI Agents
Digital employees
They understand context and decide within limits, escalating to a person when needed.
AI in action
See a system from the inside.
Pick an area and watch it work.
Simulation with fictitious data from a real system.
A thousand CVs. One morning.
- Today
- Reading, summarising and entering each CV into the system. One by one.
- With AI Mate
- AI reads and compares all thousand. You decide who to interview.
- Input
CVs received
Email and portal
Open process: 1,000 applications received.
- AI
Reading and summary
Common format
Every CV, summarised in the same format.
- Rule
Fit with the role
Six requirements
Compared against the role's six requirements.
- System
Record in the ATS
Synced
Records created in the recruitment system.
- Person
Decision
Who to interview
24 interviews proposed. The consultancy decides.
Based on a real case: Euromanager
One receipt. Fifty insurers.
- Today
- Looking up the policy, checking the bill and choosing among fifty insurers.
- With AI Mate
- Policy identified, amount validated and sent to the right insurer.
- Input
Receipt by email
Dozens a day
New receipt: bank transfer of €412.60.
- AI
Reading
Payer, amount and policy
Policy HG-7781 identified in the reference.
- Rule
Validation
Matches the bill
Matches the outstanding bill. No double charges.
- System
Sent to the insurer
Payment reconciled
Sent to the right insurer. Payment reconciled.
- Person
Unclear cases
The team decides
Anything that doesn't add up goes to the team with a note.
Based on a real case: Alea Broker
The meeting ends. The CRM is already up to date.
- Today
- Remembering what was said and filling in the CRM. Or leaving it for later.
- With AI Mate
- The meeting summarises itself into the CRM. The salesperson just reviews.
- Input
Sales meeting
Video call
Meeting ended: Example Energy, 32 minutes.
- AI
Transcription and extraction
Need, amount, next step
Key data extracted from the conversation.
- Rule
Validation
Complete data and permissions
Fields complete. Access permissions correct.
- System
CRM
Record updated
Record updated in the CRM.
- Person
Sales
Reviews and decides
Salesperson review. Next step scheduled.
Based on a real case: One Hub Energy
The machine alerts before the customer does.
- Today
- The customer calls, waits and someone searches the manual.
- With AI Mate
- The machine sends an alert and the assistant writes to the customer with the steps.
- Input
Machine alert
IoT, at any hour
Machine 0231: error E4 at 9:42.
- AI
Assistant
Understands the alert
E4: water tank empty.
- System
Data and manuals
Real status
Tank empty since 9:40. Manual: refill and restart.
- AI
Message to the customer
With the steps
Message sent to the customer with the steps to follow.
- Person
Technician
If it needs escalating
Solved without escalating. The technician, for what matters.
Based on a real case: Cafès Cornellà
The roads inspect themselves.
- Today
- Driving the road, taking notes and typing each incident into a report.
- With AI Mate
- Cameras detect and prioritise. The team heads out with the report done.
- Input
On-road images
Vehicle cameras
Route 12: 1,840 images captured.
- AI
Computer vision
Detects and classifies
Three incidents detected and classified.
- Rule
Priority
By severity
Priority assigned by severity.
- System
Incident map
Geolocated
Incidents placed on the map.
- Person
Maintenance
Acts with judgement
Maintenance report ready. The team decides where to start.
Based on a real case: AMSA
Ask your data like you'd ask a colleague.
- Today
- Finding the report, filtering, cross-referencing tables. For one question.
- With AI Mate
- You ask as you would a colleague and the answer comes from your data.
- Person
Question
In natural language
“How is the fresh line doing this month compared to last year?”
- AI
Assistant
Understands the question
Understood: fresh line sales, September, year-on-year comparison.
- System
Secure connector
The usual permissions
Access with the user's permissions.
- System
Dashboard
Real data
Data retrieved: 1,240 orders.
- AI
Answer
With the figure and the context
Answer with the figure, the comparison and the reason.
Based on a real case: Delicat Aliment
What the project looks like
From business goal to real operations.
We align the goal
What we solve and how we measure impact.
We design the system
Rules, exceptions and checkpoints.
We build the solution
AI, automation and business logic.
We integrate it into operations
With metrics and traceability.
We keep improving
New cases prioritised by ROI.
What you get
A process that runs on its own. And a team in control of it.
Automated process
With validations and exception handling
Monitoring dashboard
Hours, times, costs and errors
Documentation
How it works and how to adjust it
Trained team
To run it without depending on anyone
AI-Automation case studies
Automations and agents in production.
70%
faster CV screening
Euromanager
AI-Automation
CV screening 70% faster
50
insurers in a single collections flow
Alea Broker
AI-Automation
One receipt, fifty insurers, zero double charges
24/7
customer support, at any hour
Cafès Cornellà
AI-Automation
24/7 support connected to the coffee machines
7
AI SDRs running, from tourism to investment
AI SDR
AI Agents
Seven sales teams with an AI SDR working for them
Ask AI Mate
Is AI-Automation right for your company?
FAQ
What people ask us sobre AI-Automation.
What kinds of tasks can we automate?
Repetitive tasks such as emails, reporting or updating systems, but also parts of the analysis. We also build systems that help the team work with more context and less effort.
Do we need our own data to get started?
Not always. In the assessment phase we evaluate what data you have available and design the strategy around it. In many cases it's possible to start with limited data.
What size of company do you work with?
We work with medium-sized and large companies that already have established processes and want to strengthen them with AI. The recommended minimum is a team of 20 people.
Which AI models do you use?
We use the best models available at any given time, including open source and proprietary models when the case calls for it. We are technology-agnostic: we choose what works best for each process.
Can it integrate with our current systems?
Yes. We design solutions to integrate with your tech stack: ERPs, CRMs, databases, internal APIs and communication tools.
How much impact can we expect?
A significant reduction in operational workload, but also teams with greater capacity to deliver and better access to information.
What happens if something fails in the automation?
We design controlled, traceable systems. There is always visibility and the ability to step in.
How long does it take to be up and running?
It depends on the project. We aim for quick impact from the start.