Data and analytics automation, built on the numbers you already have
Most companies are not short on data. They are short on time to shape it into something they can act on. The month-end report gets rebuilt by hand, the dashboard is a screenshot in a slide, the forecast is a gut feeling wearing a spreadsheet. Roiwerk automates the plumbing between your systems and your decisions: reports that build and send themselves, dashboards fed from live data, pipelines that move and clean numbers on schedule, and forecasts grounded in your real history. It runs in your accounts, you own it, and we are honest about where a human still belongs in the loop.
The problem is rarely the data, it is the manual middle
Almost every reporting headache lives in the gap between where data is created and where a decision gets made. A number is born in your CRM, your billing system, your ad accounts, or a machine on the factory floor, and then a person spends hours each week exporting it, pasting it, reconciling it, and reformatting it before anyone can look at it. That manual middle is slow, error-prone, and quietly expensive, and it is exactly what software should be doing instead.
We automate that middle. Data moves from source to destination on a schedule, gets cleaned and standardised along the way, lands in a report or dashboard that updates itself, and triggers an alert when a number crosses a line you care about. Your analysts stop being copy-paste operators and go back to actually analysing. The work that used to eat a morning happens overnight, on its own, and you see the result when you open your inbox.
Where AI helps, and where it stays out of the way
The unglamorous majority of analytics automation is deterministic, and it should be: moving rows, joining tables, applying rules, drawing charts. That is code and pipelines, not AI, and we do not sprinkle a language model on it to sound modern. AI earns its place at the edges, where judgement or language is involved: writing the plain-English summary that sits on top of a dashboard, categorising messy free-text so it can be counted, flagging an anomaly that a fixed threshold would miss, or explaining why a KPI moved.
We are deliberate about the split because it matters for trust. The numbers themselves come from your systems through logic you can audit, not from a model that might hallucinate a figure. Where an LLM does touch the output, it works from data we have already validated, and a person reviews anything that drives a real decision. That is the human-in-the-loop line, and in analytics we hold it firmly: a confident, wrong number is worse than no number at all.
Built on your stack, owned by you
We build on n8n, Make, and Zapier for orchestration, plain code where transformation gets heavy, and Claude or GPT for the language and judgement steps. It all runs in your accounts, against your data warehouse, your spreadsheets, your BI tool, and your source systems. We lean toward self-hosted n8n so your data stays on infrastructure you control, which matters when the pipeline is carrying customer and financial information.
When we hand over, you own the whole thing: the pipelines, the report templates, the dashboard definitions, the code, and the documentation. There is no Roiwerk platform in the middle holding your analytics hostage, and no per-row surcharge that turns a growing dataset into a growing bill. If you ever want to bring it in-house or move to another partner, everything comes with you. That is the difference between an asset we ship you and a subscription you can never leave.
Deep dives on automated reporting, live dashboards, data cleaning, KPI monitoring, data pipelines, and sales forecasting, so you can see exactly what we would build for you.
We already have a BI tool. Do we still need this?+
Often yes, because a BI tool visualises data, it does not get the data there or keep it clean. The hard part is usually the pipeline underneath: pulling from your source systems, standardising it, and refreshing it on schedule. We build that layer and feed your existing tool, or replace a manual export-and-paste habit with something that runs itself. We won't sell you a new dashboard you don't need.
Is the AI going to make up numbers?+
No, because the numbers do not come from the AI. Calculations, joins, and aggregations run in code and pipelines you can audit. A language model only touches language and judgement tasks, like writing a summary or categorising free text, and it works from data we have already validated. Anything that drives a real decision gets a human review. We are strict about this on purpose.
How messy can our data be before this is worth it?+
Messy data is usually the reason to start, not a reason to wait. Deduping, standardising, and validating is a core part of what we automate, and clean data is the foundation everything else sits on. We assess the real state of your data first and tell you honestly whether to fix it once, automate the cleaning, or, occasionally, hold off until an upstream system is sorted.
Do you replace our systems or work with what we have?+
We work with what you have. The whole point is to connect your existing CRM, billing, ads, warehouse, and spreadsheets, not to rip them out. Automation runs on top of your stack through its APIs, and where a clean connector doesn't exist we find another way in. You keep the tools your team already knows.
Not sure which applies to you?
Book a free assessment and we'll map the highest-ROI automation opportunities for your business, honestly, including when it's not worth starting yet.
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