Back-office document flow
Invoices, claims and contracts read, validated and routed. We built an automated IP-protection engine for a Caribbean trademark practice on exactly this shape of work.
Production-Ready Automation
We take automation past the demo and into your real operations, tested and monitored so it holds up under real load.
AI is changing how businesses work.
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Months since ChatGPT was released
0%
of companies believe AI gives them a competitive advantage
0%
of executives see AI as a strategic priority in their company
What an AI automation agency actually does
Most automation work dies in the same place: a demo that impressed everyone in the room and then met real data. An AI automation agency is only worth hiring if it owns the part after the demo, which is the messy part. That means the exceptions, the retries, the days the source system returns nothing, and the person who needs to be told when something needs a human.
We build automation that runs inside your real operations, connected to the tools your team already uses, with logging you can read and alerts that reach someone. Around 80% of AI projects fail (RAND), and in our experience the failures cluster at the handover: nobody owned the thing once it was live. So we stay through launch, monitoring and the first months of drift.
The work usually starts with a readiness audit rather than a build. If a process is not stable enough to automate, automating it just produces faster mistakes. We would rather tell you that in week one, for free, than sell you a build that will not hold.
We automate what holds up.
- 01
Map the process
We document the workflow and its exceptions in detail.
- 02
Automate step by step
We build reliable automations with clear guardrails.
- 03
Test under real load
We prove it holds up with your real volume and edge cases.
- 04
Monitor and improve
We keep watch and tune it as your operations change.
What you get
- Reliable, monitored automations
- Clear guardrails and human checkpoints
- Logging and alerting you can trust
- Documentation for every workflow
The outcome. Work that just gets done, so your people focus on what needs them.
Put repetitive work on autopilot.
Sound familiar?
Tick the ones that are true for your company.
If two or more are true, you’re paying people to do a machine’s job.
See what you getReliable before fast.
Automation that breaks silently is worse than none. These are the questions we answer first, and what skipping them usually costs.
Which process eats the most hours?What are the exceptions?What happens when a step fails?Who gets alerted, and how?
Where does a human need to approve?Which tools need to connect?Can it handle triple the volume?How do we know it's working?
What does it cost to run?How fast is the payback?What should stay manual?Who fixes it when an API changes?
You automate the mess
A broken process, automated, just produces mistakes faster and at scale.
It fails silently
Without logging and alerts, you find out from a customer, weeks later.
It breaks on the edge cases
The happy path works in a demo. Real operations are mostly exceptions.
Nobody can maintain it
Undocumented automations become black boxes the moment their builder moves on.
Is this for you?
You’ll probably get value from working with us if…
Yes, if
- Your team repeats the same steps every day across several tools.
- There's enough volume for the hours saved to matter.
- You want automations that are monitored, not just switched on.
Not yet, if
- The process changes every week and nobody owns it.
- You want a fully autonomous AI agent with no human checks.
- You want a one-off script with no support.
Where automation pays off first
The processes that most often clear their cost in the first quarter.
Reporting that builds itself
For Makam we built a six-stage pipeline that turns raw Excel into branded dashboards across 5 industry verticals, cutting production from days to hours.
Customer response
First-line answers handled automatically, with clean escalation to a person the moment the question stops being routine.
Data preparation
For a London-based AI company we built a human-in-the-loop labelling pipeline producing model-ready training sets with measurable quality.
Operations monitoring
Systems watched continuously, with alerts that name the problem instead of just firing. For one Barcelona venue this covered 55 cameras and a 14-module stack.
Internal knowledge
The answers buried in your drive, wiki and inbox made findable, with the source cited so people can check it.
Tell us what you need.
Four fields, two minutes. A senior human reads every word before your call.
Proof, not promises.
Real engagements, real systems, real results.
Entertainment
Immersive horror-experience venue, Barcelona
14-module technology stack for a haunted attraction: IT infrastructure, 55 cameras, AI character, ticketing, and visitor tracking.
14 ModulesRead the case Mining/FintechSikaAsɔm
Deep-dive research into Ghana's artisanal gold mining sector to inform platform design for price discovery and regulatory compliance.
3 CorridorsRead the case Data/AnalyticsMakam
Six-stage AI pipeline that turns raw Excel data into branded HTML dashboards across 5 industry verticals, dropping production time from days to hours.
Hours ProductionRead the caseQuestions we get about automation
Quick answers to the questions we get the most. Can’t find yours? Write to connect@sabemos.ai.
How is this different from hiring an automation freelancer?
A freelancer builds the workflow. The harder question is what happens in month four when an API changes or volume triples. We own the audit, the build, the monitoring and the training, so there is no gap where the thing quietly stops working and nobody notices.
Which tools do you build on?
Whatever fits the job and your existing stack. In practice that has meant n8n, Retool and PostgreSQL for orchestration and internal tooling, plus direct API work where an off-the-shelf connector would be too fragile. We do not resell one platform.
How long before something is live?
A first automation is usually live in weeks, not quarters. We deliberately start with one process that is small enough to prove and valuable enough to matter, then widen from there once it has held under real load.
What does it cost?
It depends on how many systems have to talk to each other and how messy the exceptions are, which is exactly what the fixed-price readiness audit (from 1,250 euros) establishes. You get the cost and the expected return in plain terms before anything is committed.
Do you work in Spanish?
Yes, natively. Audits, documentation, training and support are delivered in the language your team actually uses, and this entire site exists in English and Spanish as proof.
What if automation is the wrong answer?
Then we say so. Some processes need fixing before they need automating, and some do not have the volume to justify it. Telling you that is cheaper for both of us than a build that gets switched off.