The problem: repetitive work absorbs your best people's time
Almost every company has processes nobody designed to last: a spreadsheet that started as a temporary fix, an email forwarded manually every week, a data validation done by the same person for three years because "that's how it's always been done." None of these processes require senior talent, but all of them consume the time of someone who has it.
The real cost isn't just time spent — it's the lost opportunity to use that time on something a system can't do: negotiate, decide, handle a difficult client.
How we solve it
- Mapping the real process: we document how work actually happens today, not how the manual says it happens — exceptions and informal shortcuts are the most important part of the mapping.
- Classifying task type: we separate what's purely mechanical (moving data from one place to another) from what requires interpretation (reading a document and deciding what type it is). Each type needs a different technique.
- AI automation where variability exists: when the process handles free text, images or inconsistent formats, we use AI models capable of interpreting that variability — not rigid rules that break on the first different case.
- Supervision and confidence threshold: the system resolves what it can resolve with high confidence and escalates what it can't to a person — it never decides blindly on ambiguous cases.
Expected result
The actual savings depend on the process and volume — we quantify it during the initial assessment. As a directional reference (an estimate, not verified data): document classification and validation processes typically free up 20% to 40% of the operational time spent on that task. We do have a real process automation case in production: Polis, which cuts vehicle insurance quoting in Colombia down to about 50 seconds, with zero data-entry errors, by connecting RUNT, Fasecolda and insurers into a single flow.
Technology stack we use
Python
The base language for nearly any automation pipeline and AI model integration.
Language models (LLMs)
For interpreting free text, classifying documents and extracting data from unstructured formats.
scikit-learn
Explainable classification models when a large neural network isn't justified.
AWS / Azure / Google Cloud
Orchestration and deployment of automated flows on the cloud your company already uses.
Methodology
Assessment, scope, development, deployment and monitoring — the same five phases across all our projects. See full methodology →
Where it applies today
AI process automation is cross-industry: we apply it alongside computer vision in agriculture, and evaluate cases in manufacturing, logistics, retail, security and health depending on each company's specific process.
Frequently asked questions
How is this different from traditional RPA?
Traditional RPA follows fixed rules and breaks when something changes on screen or in a document. Our systems use AI to interpret variable content (free text, images, different formats), so they tolerate far more real-world variability.
Which processes are good candidates for AI automation?
Ones that are repetitive, high-volume and follow a decision criterion that can be explained, even if it isn't written down. Document classification, data validation and report generation are usually the first candidates.
Will AI replace my team?
The goal is to remove the repetitive part of the work, not the people. In practice, teams get reassigned to exceptions, judgment calls and client relationships — the work that actually requires human judgment.