You tell me the problem and we solve it. Direct, clear and working.

No endless meetings. No wasting time. No fluff.

TO PROBLEMS, SOLUTIONS.

In 48h I can return a realistic approach: priority, scope and deliverables.

If you tell me the process, I'll tell you what to automate first

How to detect automatable processes, design robust flows and avoid automating chaos.

Process automation: checklist, typical errors and how to do it right

Quick checklist (automatable)

Automate repetitive tasks, with clear rules and definable input/output. Don't automate what changes every day without criteria or what depends on human decisions without data. The useful question: can this be written as a flow with validations?

What to automate (and what not)

If the process today is confusing, automating it makes it faster… but just as confusing. First, define states, responsible parties and rules. Sometimes the greatest value is ordering the process and then automating a part.

The typical error: automating chaos

Automation without error handling is a time bomb. You need: retries, limits, logs, alerts and "plan B" when data is missing or an API fails. Robustness is part of the deliverable.

Design for exceptions (otherwise it breaks)

Without traceability there is no trust. The minimum: record executions, inputs, outputs, errors and times. For some businesses, this is more important than "doing it fast".

Traceability: knowing what happened and why

Minimal (but real) documentation: what the flow does, what it needs to work, how to restart it, what to look at if it fails. That prevents automation from becoming total dependence.

Useful delivery: minimal documentation

Python (Celery, RQ) for async tasks. Zapier/Make for quick integrations. Django/Flask for internal APIs. Databases (PostgreSQL, MySQL) for state. The stack depends on the case, but the base is usually: language + scheduler + database + alerts.

Tools and typical stack

Process Automation: Checklist and Typical Errors — RUMAZA Studio

What to automate first, what flow to design, what risks there are and what deliverables to build to have impact.

If you give me context, in 48h I'll give you clarity

Process automation for SMEs: fewer manual tasks, fewer errors, more control. Flows, integrations and bots when they add value.

Process automation

Automation — RUMAZA Studio

Python (automation, APIs, scraping), JavaScript/Node.js (webhooks, integrations).

Languages

REST APIs, webhooks, databases, business tools (CRM, ERP, ecommerce).

Integrations

Servers, schedulers (cron, task queues), logs and alerts. The minimum to work without breaking.

Infrastructure

We choose tools for reliability and maintainability. Not for fashion.

Typical stack

I understand your manual process: what you do, what data you use, what errors occur and what hurts. We define what to automate first to have impact.

1) Diagnosis

Process mapping: inputs, validations, transformations, exceptions and outputs. It's not just code: it's a system that's understood and maintained.

2) Flow design

Development with logs, error handling and testing. The goal is robustness: it works when you're not looking and alerts you if something fails.

3) Implementation

How it works, how it's maintained and what to do if something changes. If the process evolves, we propose maintenance or improvements.

4) Documentation + delivery

What it includes

Typical problems

Result

What it solves

Packaged product for automation: working flow with validations, traceability and clear deliverables.

Automate your business

Includes

Optional

Automate Your Business — RUMAZA Studio

Python (scripts, APIs), JavaScript/Node.js (web automation). We choose according to the case.

Airflow, Zapier, Make, n8n depending on complexity and budget. What matters is that it works, not the brand.

Tools

Servers, schedulers, logs and alerts. The minimum to work without breaking.

Simple and effective tools. We don't complicate if not needed.

I analyze your manual process and your tools. We define what to automate first and what flow to design. In 48h you have a clear plan.

1) Quick diagnosis

I build the flow with validations, error handling and traceability. It's not just code: it's a system that works autonomously.

I develop the automation and test it thoroughly. If there are integrations with APIs or external systems, I make sure they're stable.

3) Implementation and testing

How it works, how it's maintained and what to do if something fails. If you need continuity, we propose maintenance. The automation must work tomorrow, not just today.

Typical situations

What you get

Who it's for

In 48h: diagnosis, approach and clear deliverables. No fluff, just solutions that work.

If you tell me your problem, I'll tell you how we solve it

Honest examples of solutions built: problem → approach → deliverables. No inflated logos, just measurable results.

Cases

Problem: competition with changing prices, scattered catalogs and no change alerts. Solution: robust scraper that captures prices, stock and news from 9 websites daily. Result: unified database, automatic price change alerts and internal app for real-time queries.

Shoe scraper from 9 websites → unified database + alerts

Problem: shoe brand without professional website and data in Excel, no visibility of which product rotates, which channel leaves margin or which client is profitable. Solution: corporate web design + complete control dashboard with clear KPIs (sales by product, margin by channel, top clients), automatic critical stock alerts and process automations. Result: operational website and daily decisions based on data, not intuition.

Web design + control dashboard for shoe brand → automated operations

Problem: scattered customer data, no clear segmentation and manual communication without personalization. Solution: system that obtains and unifies customer data, analysis for automatic segmentation and implementation of personalized messaging (new customers, abandoned carts, post-sale follow-up). Result: unified database, automatic segmentation and messaging campaigns that run on their own.

Customer data automation + segmentation + automated messaging

Problem: management of players, payments, training and statistics in Excel and WhatsApp. No visibility of performance or history. Solution: internal system with player management, payments, attendance and statistics dashboard. Result: organized operations, traceable payments and performance analysis per player.

Sports management system + data analysis for club

Problem: need for ecommerce website with manual processes (lead generation, order processing, stock control, shipping and SEO content). Solution: custom ecommerce website with automations: lead capture and management, automatic order processing, real-time stock control, automated shipping and SEO content generator. Result: operational ecommerce with end-to-end automated processes.

Custom ecommerce + complete automations

Problem: manual billing, bank reconciliation and error-prone accounting. Solution: automation that generates invoices from orders, reconciles bank payments and exports accounting. Result: automatic billing, daily reconciliation and error-free accounting.

Billing and accounting automation → zero manual errors

Cases: real solutions that transform businesses — RUMAZA Studio

Tell me your problem. In 48h I can give you clarity: focus, scope and next steps.

Name

Tell me your problem

To give you a closed proposal

Quick links

Contact

Sources, KPIs, priority and closed scope. No smoke.

If you give me context, in 48h I'll tell you what dashboard to build first

Practical guide to create useful dashboards: well-defined KPIs, clean data, visual hierarchy and actionable decisions.

Dashboards for SMEs: KPIs, reliable data and fast decisions

The first thing is not "Power BI or Metabase". The first thing is to decide what questions the panel should answer. Examples: Which channel leaves margin? Which product rotates? Which client is profitable? Where does our time go? If there are no questions, there are no KPIs.

Start with the business, not the tool

KPI Checklist (minimum viable)

Badly defined KPI = endless discussions. Define each KPI as if it were a contract: name, formula, frequency, source and "who uses it". Example: "sale" does it include returns? Does it include VAT? Is it counted when paid or when invoiced?

Define KPIs with precision

If the data is broken, the dashboard only masks it. Normalizing, deduplicating and validating is what gives confidence. This includes simple rules: date formats, consistent names, unique keys, valid states and reconciliation between systems.

Data quality: where almost everyone fails

A useful panel has hierarchy: at the top what matters, then explanation, then detail. It's not "more charts". It's less noise and more reading. Ideally: 3–6 key KPIs + trends + breakdown and drill-down.

Visual hierarchy: the panel must be read in 10 seconds

Many SMEs improve more with alerts than with a huge panel. Examples: conversion drop, critical stock, return peaks, expenses out of range. Less "review", more act.

Alerts and reporting: the dashboard cannot depend on "going to look at it"

Sources change, fields will be renamed, users will create "new categories" and exceptions will appear. A serious dashboard includes minimal observability: logs, validations and a reasonable maintenance plan if the business requires it.

Maintenance: the reality of data

Dashboards for SMEs: KPIs, reliable data and fast decisions — RUMAZA Studio

What to measure, what sources to use, what to automate and what dashboard to build first to have impact. No smoke, no PowerPoints, just clarity.

Dashboards and analytics for SMEs: clear KPIs, clean data and fast decisions. From Excel to a reliable panel with metrics and alerts.

Data & Dashboards

Data & Dashboards — RUMAZA Studio

Python, SQL, validations, light ETL. What's necessary for data to be reliable, not just pretty.

Data

APIs, CSV/Excel, MySQL/PostgreSQL, business tools (CRM, ERP, ecommerce). We connect what you already use.

Sources

Clear dashboards + reporting. Tool according to case: from Metabase to custom solutions. What matters is that it works, not the brand.

Visualization

I understand your business and your data sources (Excel, ERP, CRM, ecommerce, APIs). We define metrics with clear names: what is "sale", what is "margin", what is "lead", etc. No ambiguities, no interpretations.

Extraction, transformation and basic validations. If the data is broken, the dashboard only masks the problem. Here we fix the base: clean, consistent and traceable data from the source.

2) Data pipeline

Visualization with hierarchy: first what's important, then the detail. Less "color chart", more actionable reading. A panel that tells you what to do, not just what happened.

3) Dashboard

What each KPI measures, where the data comes from and how it's maintained. If you want continuity, we propose maintenance. The goal is for it to work tomorrow, not just today.

What KPIs to define, what sources to use, what dashboard to build and what recommendations to give you to have impact.

Packaged product: dashboard + analysis + actionable recommendations. To make decisions with data without complications.

Data Boost

Does not include

Data Boost — RUMAZA Studio

Python, SQL to clean and connect. What's necessary for data to be reliable.

Clear dashboard (Metabase, Superset or custom depending on case). What matters is that it works, not the brand.

Written recommendations based on real data. It's not complex data science, it's practical clarity.

Analysis

I analyze your data and your business. We define what KPIs matter and what sources to use. In 48h you have a clear plan.

I connect your sources (Excel, CSV, APIs, databases) and clean the data. The goal is for them to be reliable, not just pretty.

2) Connection and cleaning

I build a clear panel with your KPIs. It's not just visualization: it's that you understand what's happening and what to do.

I give you a written analysis with actionable recommendations: what I would do with that data. It's not posturing, it's clarity.

4) Analysis + recommendations

In 48h: honest diagnosis, alternatives and the most efficient path (no fluff).

If you want, I'll tell you if your case needs data science or not

Honest guide on data science: real use cases, requirements, risks and how to avoid fluff projects.

Data science for business: when it adds value and when it's fluff

Requirements checklist

For data science to add value, you need sufficient and quality data, a clear decision to improve (not "do AI for AI"), success measure (baseline and objective) and ability to put the result in production.

Minimum requirements (otherwise it fails)

Demand prediction, lead scoring, anomaly detection, segmentation, inventory optimization or fraud. Always with one condition: that the output is used to act.

Use cases where it usually adds value

When there's no data, when the business process is broken or when the real problem is operational (and is fixed with automation/internal system). "AI" doesn't fix the lack of process.

Cases where it's usually fluff

If you can't explain the problem without saying "AI", you probably don't need AI yet. First order the process, clean the data and define clear metrics.

Practical rule

1) Clear metrics (what is success), 2) Clean and traceable data, 3) Dashboard + alerts, 4) Flow automation, 5) Models (only if they add value). Don't start with models.

Recommended order

Python (Pandas, Scikit-learn) for analysis and models. Jupyter for exploration. Databases for storage. APIs for integration. The stack depends on the case, but the base is usually: Python + ML libraries + database + integration API.

Data science for business: when it adds value and when it's fluff — RUMAZA Studio

Tell me what you'd like to see more clearly.

Want to understand your team better?

Structure and simple visuals from what exists with quality.

Match / competition data

Readable dashboards

Pipelines and alerts only when the case needs them.

Integration & upkeep

What it can include

Useful data to train and decide: performance and context.

Sports data for real decisions

Outcome

Many calls are still made by gut feel

Data that serves coaching staff

Sports data analysis — RUMAZA Studio

Problem & outcome

Limits

What you gain

How we work

Site that organizes teams, calendar, content and sponsors.

Webs for sports clubs

Players, teams, attendance and roles in one place.