AI Automation & Agents
Processes that run end-to-end, freeing your team's time for higher-value tasks
What we automate
Processes that involve data or decisions. These are the three criteria we use to decide whether a process qualifies.
CriteriaWhat a process must meet
Frequency
That someone does it today with a frequency that justifies it.Available data
That the necessary information exists somewhere, even if scattered across systems that don't communicate.Definition of done
That it is possible to describe what would need to happen to consider it done well, even if there is no single correct answer.Three ways to automate
Process automation
The path is known in advance and does not change, even if within it there are steps that no rule can resolve. Reading an invoice and extracting the relevant fields, classifying a ticket by its actual content rather than keywords, drafting the summary of a report, enriching a record with information from another document. The sequence is fixed; what AI contributes is the task that previously required someone to read. These are the shortest projects with the most verifiable results, because for each input there is an identifiable correct output.
Read more →RAG. AI-powered search on your own documentation
Everything your company knows is written somewhere, spread across manuals, contracts, reports and emails, and finding it costs more than having written it. A retrieval system searches that material, cross-references what it finds, and returns an answer with the source alongside. The difference from a search engine is that it does not return documents for someone to read: it returns the answer and where it came from.
Read more →Multi-agent systems
In every decision-making process there are complex analysis variables that require the intervention of multiple actors or criteria. This makes the process dynamic and the path depends on what is found along the way. An operational decision that combines multiple signals and changes case by case. A task with multiple steps that the team executes with specialised judgement. That is where a system that breaks down the problem, decides which path to take based on what it finds, and reviews its own work before declaring something done enters the picture.
Read more →The first step of the diagnostic is locating your case. Many processes have segments of more than one type, and the sensible approach is to resolve with fixed paths everything that can be and reserve agents for what cannot be anticipated. An agent system costs more and takes longer, so using it where a fixed flow would have sufficed wastes money. Using it where it belongs, however, allows automating what was previously out of reach — precisely the work that consumes your most expensive profiles. And there, moving data between systems is not enough: it takes engineering over what that data says.
What is delivered
When the process is critical or changes frequently, the proposal also includes continuous monitoring: performance review, adjustment when the process changes, and response when something breaks. Scoped from the start, with its own price, not as an invoice that appears later.
How far it goes on its own
It executes the strategy you define, at a scale your team cannot support. The rules of the game are still set by your business; execution is autonomous.
- Internal processes with no external consequences
- Repeated decisions with known criteria
- Volume your team cannot handle
- Output to customers or authorities
- Sectors requiring an audit trail
- Initial phase until evidence accumulates
When there is review, the system leaves the work done and explained, with the reasoning for each step, so that approval takes minutes, not hours.
How it is verified to be working
This is the question almost no one answers, and it is the one that underpins everything else.
A system that executes on its own with no way to verify it is executing correctly is not autonomy, it is a problem waiting to happen.
Before anything goes live, we define with your team what a correct output looks like. Outputs from the system are reviewed during development and marked as right or wrong, until the system's criteria align with the business criteria. That work is what prevents delivering something technically correct and operationally fragile. From then on, every new correction that comes up is material for refining it, and that refinement continues throughout the life of the system — partly because the system reviews its own work, and partly through feedback your team provides when something does not come out as expected.
- Cases that pass without intervention
- Cases that need correction
- Type of case where it makes mistakes
- Whether what was executed had the expected effect
- Takes longer to see
- Decides whether it is worth maintaining
From model to action
A predictive model returns a probability or a value. Someone has to decide what to do with that, and when there are several models running, that decision becomes work in itself.
There are two ways to solve it, and they are not the same. One is to integrate the outputs to decide: combining the churn probability, the expected value, and the propensity of each customer with your business rules, so that the right action for each one emerges. The other is to execute the strategy: when the model already tells you who, but the team cannot keep up building and launching what follows, a system that prepares the campaigns, justifies them, and leaves them ready for review.
Both are multi-agent systems. The difference is whether the bottleneck is deciding or executing. See the full case →
Not sure where to start or which process makes sense to automate first?
Strategic Advisory →Frequently asked questions
Our information is spread across systems that don't talk to each other. Is that a problem?
It's normal and it's part of what gets resolved. Almost no manual process survives because of a lack of technology, but because someone has to go and fetch the information from three different places. What blocks is not that they are separate but that something is not recorded.
How do we know it won't make mistakes?
It will make mistakes sometimes, just as whoever does that work today makes mistakes. The useful question is how often, in what type of case, and whether you find out. That is measured before anything goes live, benchmarked against cases your team has already resolved, and continues to be measured in production.
Does someone need to supervise it every day?
No, and that is precisely how automation saves nothing. Where a person intervenes is decided when designing the system, based on what happens if it makes a mistake at that point and whether your sector requires a record. An internal process with no external consequences runs on its own.