Process before tooling
We map the process as it really is, with exceptions and rework. Only then do we choose between workflow, integration, RPA or an AI agent.
Process automation and applied AI: how we build it
We automate processes with workflows, integrations, RPA and LLM-based AI agents. Before choosing a tool, we map the process and measure how it works today, so the result can be compared later. Critical points keep human review, data follows the LGPD, and every automation runs with monitoring, like any system in production.
Particularities
Measured baseline
We measure time, volume and errors in the current process before automating. The same metric is tracked afterwards to show the real effect.
Human in the loop
Sensitive decisions and out-of-pattern cases go through review by a person. The automation suggests and executes, and your team approves where it matters.
Data security and LGPD
Which data is used, where it lives and who can access it are defined in the architecture. Sensitive data does not leave your environment without an explicit decision from your company.
Monitored automation
Every flow has logs, alerts and an execution dashboard. Failures are detected and handled instead of silently stopping the process.
Discovery and development phases
- 01 · 15 days
Sprint Zero
Map the process, measure the baseline and validate the approach with real data.
Activities
- Mapping of the current process with the people who run it
- Baseline measurement of time, volume and errors
- Choice of approach: workflow, integration, RPA or AI agent
- Testing the approach with real data
- Definition of human review points and LGPD rules
Deliverables- Vision Document with the mapped process and baseline
- Validated automation prototype
- Roadmap with scope and estimates
- 02 · 6 to 12 weeks
MVP in production
Put the first automation into production, with human review and monitoring.
Activities
- 2-week sprints with a Sprint Review every 15 days
- Building workflows, integrations and agents
- Evaluating AI responses with real cases
- Review and approval screens for your team
- Logs, alerts and an execution dashboard
- Go-live and comparison with the baseline
Deliverables- Working automation in production
- Monitoring dashboard
- Comparison with the baseline
- 03 · Ongoing
Orbit
Keep automations reliable and extend them to new processes.
Activities
- Execution monitoring and failure handling under SLAs
- Tuning prompts, rules and models based on real use
- Tracking metrics against the baseline
- Automating new processes with the same team
Deliverables- Monitored operation with SLAs
- Living automation roadmap
Allocated crew
| Role | Sprint Zero | MVP in production | Orbit |
|---|---|---|---|
| Project managerFocus: Planning, priorities and communication with the teams involved. | Leads | Part-time | On call |
| Process analystFocus: Process mapping, baseline and metric definition. | Full-time | Part-time | Part-time |
| Integration developerFocus: Workflows, API and system integrations, and RPA bots. | Part-time | Full-time | Part-time |
| AI engineerFocus: LLM agents, prompts, response evaluation and guardrails. | Part-time | Full-time | Part-time |
| QA analystFocus: Testing with real cases, exceptions and human review flows. | Not allocated | Full-time | On call |
| Support analystFocus: Execution monitoring, alerts and SLA compliance. | Not allocated | Part-time | Leads |
Technologies
Large language models (LLMs)Workflow orchestrationRPAAPI integrationsNode.jsPostgreSQLMonitoring and observability
Frequently asked questions
How do I know whether a process is worth automating?
In Sprint Zero we map the process, measure the baseline and test the approach with real data. If a simpler solution does the job, we recommend it.
Will the AI make decisions on its own?
Only where you decide. Critical points and out-of-pattern cases go through human review, and every decision the automation makes is logged.
Is company data kept safe?
Which data is used, where it lives and who can access it are defined in the architecture, following the LGPD. Sensitive data does not leave your environment without an explicit decision from your company.
Does my company own the source code?
Yes. At the end of the project the source code belongs to the client, and we sign a non-disclosure agreement (NDA) whenever you ask.
Related service
Artificial intelligence
AI applied in production, not in a slide deck.
Software development
Custom software, from discovery to deploy.
Ready to get your mission off the ground?
Tell us what you need to build. We reply within 48 business hours.