About Camila

I build AI into the work teams already do.

I'm an engineering manager and the founder of AI at Work Academy. I help managers turn the pressure to use AI into one useful change their team can test.

I created the Academy for people who have access to AI but still need to decide where it belongs in the work, what a person should check, and how to know whether it helped.

See the working session

Camila Lima

Engineering manager and founder

My work

Software delivery, quality, client work, and engineering leadership

What I build

AI systems used across engineering, marketing, growth, and product work

How I help

Choose where AI can help, plan the first step, and review what worked

Selected work

Two examples from work I run.

The details change across teams. The management questions stay similar: what should AI do, who checks it, and what happens when the result is incomplete?

Engineering

Getting technical requests ready for review

The work: A new request can arrive with missing context, unclear requirements, or questions that need technical investigation before the team can plan it.
What AI does: AI reads the request and the available notes, identifies missing information, and prepares the questions the reviewer needs to answer.
What a person owns: The technical lead checks the reasoning, decides whether the request is ready, and owns the final recommendation.
What I learned: Speed only helps when the reviewer can see what information the AI used and where the answer is uncertain.

Marketing and growth

Preparing recurring research and performance reviews

The work: Research, content planning, and performance checks repeat every week. The preparation can take time before anyone makes a decision.
What AI does: AI gathers approved sources, organizes the findings, and prepares a first summary on a schedule.
What a person owns: I check the sources, choose what matters, and decide which action is worth taking.
What I learned: Recurring AI work needs a clear owner, a clear review, and a plan for incomplete results.

The systems behind the work

AgentHub and Estuddo keep me close to the full problem.

Estuddo, an independent study product built by Camila Lima

I built AgentHub to schedule recurring AI work. I also designed and built Estuddo from idea to release. This gives me direct experience with cost, missed context, failed runs, human review, and what it takes to make a product usable.

What I believe about AI adoption

Useful beats impressive.

Start with useful work

Choose a recurring task or decision that matters. Get clear on the result before choosing a tool.

Keep responsibility clear

Decide what AI can do, what a person must check, and who owns the final result.

Use evidence before expanding

Review quality, repeat use, time, and cost. Continue when the result makes sense for the team.

Work with me

Bring one real team challenge.

We will choose a useful place for AI to help, make a focused plan, and review the result together.

See the working session