Discovery
Map the process, risk and point where automation can genuinely reduce friction.
- Current process
- Available data
- Risk
- Success criteria
AI & Automation
Useful automation starts with clear boundaries.
I design AI-assisted experiments and systems for concrete operational problems, with evaluation, privacy and human oversight.
Controlled prototype
Build the smallest experience that can validate utility and limits.
- Limited scope
- Anonymised data
- Guardrails
- Observability
Evaluate before scaling
Test critical intents and keep human intervention where errors carry meaningful cost.
- Critical cases
- Human review
- Failure log
- Explicit decision
Before the prototype
Automation is a decision about process, risk and responsibility.
The question is not where to add AI. It is where repetitive work, recoverable context and errors that can be detected, contained or escalated exist.
Process fit
Map inputs, decisions, exceptions, available data and error cost before choosing model, interface or integration.
Knowledge architecture
RAG, hybrid search and knowledge bases only help when sources, updates, permissions and absence of an answer are well defined.
Risk-oriented evaluation
Evaluation separates average accuracy from reliability in critical intents through test sets, adversarial cases, logs and stop criteria.
Human oversight
Guardrails, fallback, review and escalation define where the system may act and where it returns control to a person.
Operation and improvement
Observability, feedback and periodic review help correct sources, prompts, workflows and boundaries without claiming full autonomy.
When not to automate
A good strategy also knows when to say no.
Automation is not appropriate when reliable data is missing, the process changes constantly, errors carry high impact without possible control or no owner exists to operate and evaluate.
- Defined goal and user
- Authorised, minimised and maintainable data
- Success and failure criteria
- Human fallback and owner
- Decision to scale, limit, pause or archive
Questions about AI and automation
What is RAG and when is it useful?
Retrieval-Augmented Generation combines information retrieval with response generation. It is useful when answers must rely on controlled sources, but requires governance, evaluation and explicit uncertainty handling.
Is an assistant with 90% accuracy ready?
Not necessarily. The average can hide failures in critical questions. The decision must consider error severity, coverage, fallback and oversight, as the archived Berlenguinha experiment showed.
Does automation replace teams?
The focus is reducing friction and expanding capacity in specific tasks. Responsibility, judgement, exceptions and risk decisions remain assigned to people.
What can a prototype deliver?
A process map, hypothesis, knowledge architecture, controlled prototype, evaluation set, guardrails, logs, decision criteria and operating plan.
Connect AI to the system
LabExperiments, states and documented learning.
Web GrowthWhere automation meets experience and measurement.
ServicesDiscovery, prototyping and implementation.