Zumaquero DevAI process automation for businesses
Are there repetitive tasks your team still does by hand? I work out where AI makes sense and build a solution that is useful and maintainable. Where it does not make sense, I tell you.
The problem: manual work that eats the team's time
Almost every company has processes that run on copy and paste, reviewing documents one by one or answering the same thing over and over. Nobody has automated them because they seemed too specific or because the usual tools did not reach that far. Generative AI has moved that boundary, but it has also brought a lot of noise: not everything sold as AI solves a real problem.
- Classifying incoming emails, invoices or documents and routing them by hand.
- Extracting data from PDFs, contracts or forms to move it into a sheet or a system.
- Writing the same type of document (quotes, reports, replies) again and again.
- Answering frequent customer or colleague questions with the same information.
- Digging through old folders and emails for something someone wrote months ago.
- Chaining steps between several tools that do not talk to each other.
How I approach it: from feasibility to maintenance
Four phases to move from "I think AI could help us" to a solution in production that the team uses every day. You can stop after any of them.
- Feasibility analysis: I review the process, the volume, the available data and the current cost, and tell you whether AI adds value, with what approach and what limits.
- Scoped proof of concept: I build a minimal version on a real case with your data, to measure accuracy and savings before investing in the full solution.
- Integration: if the proof of concept convinces, I connect it to your systems (email, document manager, ERP, CRM) and leave the process running end to end.
- Maintenance: monitoring the quality of the answers, tuning the prompts and the model, and controlling costs as the data and the usage change.
What the proof of concept includes
The proof of concept is the entry point and does not commit you to the next phase. When it is done you have concrete data to decide, whether you build it with me or not.
- Definition of the use case and the success criteria (minimum accuracy, time saved, cost per operation).
- Working prototype on a real sample of your documents or queries.
- Measurement of results: what it gets right, what it gets wrong and when a person should decide.
- Estimate of the monthly cost in production (model, infrastructure and maintenance).
- Written recommendation: continue, adjust the approach or rule out AI for this process.
Timeline and price
The proof of concept starts from 900 € and is delivered in 2-4 weeks, depending on the complexity of the process and the state of the data. That amount is deducted if we then continue with the integration. The prior feasibility analysis can be hired on its own if you prefer to validate the approach before committing to the full proof of concept. Integration and maintenance are quoted with a fixed scope once there are results.
Keep reading
- If the process you want to improve does not need AI, but an internal tool that does not exist yet: custom software development.
- If you already have an application built by another company and want to add AI capabilities without rebuilding it: legacy software rescue.
Frequently asked questions
What if AI is not the solution for my process?
I tell you in the feasibility analysis, before you spend on a proof of concept. Sometimes the process is better solved with rules, a normal integration or a change in the workflow. I would rather lose that job than sell you a solution that will not be used.
Does my data end up in a third-party model?
It is decided with you based on how sensitive the information is. You can work with providers that do not train on your data and with data-processing agreements, or with models that run on controlled infrastructure. The choice is justified in the feasibility analysis, not assumed.
How much does it cost to keep this in production?
It depends on the volume and the model, which is why the proof of concept includes a monthly cost estimate. It is usually a cost per operation (per document processed or per query answered) plus maintenance. Without that figure on the table, it makes no sense to move to integration.
Do I need a lot of data to get started?
For most cases with generative AI there is no need to train your own model, so you do not need a huge history. A representative sample does help (a few dozen real documents or queries) to measure accuracy in the proof of concept.
Who is responsible if the AI gets it wrong?
The solution is designed so that a person reviews the doubtful cases, not to replace human judgement where there are consequences. The design defines what the AI decides on its own, what is flagged for review and what always stays out of its scope.
Have a manual process you think can be automated?
Tell me what your team does by hand, at what volume and with which tools. I will come back with an honest first assessment of whether AI fits, with no obligation.