Process mapping
Find where time, context or consistency is being lost.
Approach
Applied AI for real work, not just demos.
Automation matters when it removes friction, improves output quality and keeps control points visible.
Workflow design
From agent and tool orchestration to exact result validation.
Usable delivery
Documentation, handoff and rules the team can keep working with.
What changes
What usually happens when automation is done well
Less repetitive work
The team gets time back for analysis, creativity and decisions.
More consistent outputs
Reporting, documentation or drafts follow a reliable structure.
Better traceability
It becomes clear what goes in, what comes out and where to intervene when something fails.
The best first step is usually a small workflow with a clear impact.
If you already have a repetitive process, we can turn it into a measurable prototype before scaling it.
Use cases
Automation work where AI creates real value
I work with existing processes where repetition, context loss or control requirements are already visible.
Automated reporting
Collect, normalize and explain recurring data for marketing, sales or operations teams.
Research and documentation
Systems to summarize sources, extract learnings and keep internal documentation up to date.
Briefs and content
Workflows for briefs, drafts, editorial checks and consistent deliverables.
Internal operations
Automation for admin tasks, handoffs, alerts and recurring materials.
Data validation
Checks to detect errors, duplicates, deviations or incomplete information before it is used.
Team handoff
Clear structures so strategy, content, paid media, SEO and operations work from the same context.
Deliverables
From prototypes to documented systems
The scope can stay small and pragmatic or grow if the workflow deserves it.
Current flow map
Diagnosis of steps, friction and dependencies.
Automation prototype
A working version to validate value before scaling.
Prompt and rule library
Instructions, checks and editorial or operational guardrails.
Usage manual
Documentation to operate, review and evolve the system.
Process
How I turn a real use case into useful automation
I start with concrete use cases and real friction. The automation comes after that.
01
Choose the case
Pick a workflow with repetition, cost or accumulated error.
02
Design and validate
Prototype the system and review quality, controls and limits.
03
Document and hand off
Leave it ready for internal use or further iteration.
Technical stack
Agents, workflows, RAG and evaluation for maintainable systems
I combine models, frameworks and automation tools according to the level of control, integration and scalability each use case needs.
Agent frameworks
I design agents with instructions, tools, limited memory and clear stopping criteria.
Orchestration and workflows
I connect steps, approvals, APIs and automations so the flow is predictable and auditable.
RAG and vectorization
I use retrieval when the system needs to work with documentation, history or internal knowledge.
Observability and evaluation
I define logs, metrics, output tests and reviews to detect degradation or recurring errors.
Enterprise tooling
I consider permissions, security, maintenance and vendor dependency before putting a workflow into production.
Common questions
Before jumping into automation
Do we need a complex stack?
Not always. Many improvements come from connecting existing processes properly before adding more tools.
Can this apply to both marketing and operations?
Yes. Reporting, research, documentation, briefs, content and admin tasks are all strong candidates.
How long does a first prototype take?
It depends on integrations and data access, but I usually start with a small scope that validates usefulness before investing more.
What about security, permissions and sensitive data?
Limits are defined from the start: what data goes in, where it is processed, who reviews the output and which parts should not be automated.
When should AI automation be avoided?
When the process is not clear yet, the volume does not justify the effort or the risk of a wrong output is higher than the expected saving.