Data scattered around.
ERP, CRM, spreadsheets and forms. The same indicator comes out differently depending on who calculates it.
AI by process · Data and decision-making
Every department has its own number. None of them match.
Reports that arrive late, figures that do not agree and decisions made on last week’s data. We unify the sources and leave one single version, queryable in natural language.
Deciding blind.
Reports that lag.
The report lands on Wednesday with data from the previous Friday.
Analytics nobody uses.
Beautiful dashboards that do not answer the question the manager is actually asking.
Four ways in.
We start with the three questions that really get asked every week, not with the full catalogue of metrics.
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01
Source unification
A warehouse with the data from your systems, with agreed definitions for every metric.
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02
Dashboards that answer questions
Designed around the real decisions of the business, not around what the tool ships with.
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03
Deviation alerts
The system warns when an indicator leaves its range. Nobody has to watch the dashboard.
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04
Natural-language queries
Ask in text and get the figure along with the query that produced it. Verifiable.
- One agreed definition for each metric
- Live against reports that are days old
- Verifiable every figure shows how it was calculated
What we bring from what is already running.
Here there is a case of our own: ADG is in production and is exactly this — ingesting public data from heterogeneous sources, normalising it and turning it into indicators a third party can verify. Eco-Impacto does the same with telemetry from sensors on public roads. Swapping a public observatory for a company dashboard means changing the connector and the data model, not the architecture.
Highest-impact use cases
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01
A single version of the truth
ERP, CRM, spreadsheets and the shop-floor system unified into one model, with the business rule written once and not in every report.
Ends the meeting about which figure is the right one.
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02
Asking in plain language
Questions in your own words over the real data, returning the figure and the query that produced it. Whoever does not know SQL stops waiting for whoever does.
Analytics reaches the person who decides.
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03
Alerts on deviation, not reports
The system flags an indicator moving outside its expected range, with the context of what changed. Nobody has to open the dashboard to find out.
The data goes looking for the person.
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04
Every figure links to its origin
From the indicator to the record behind it in one click. Without that, no dashboard survives the first time somebody doubts a number.
Trust in the data is what makes it usable.
What it is built with.
Before you call us, read this.
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How to prioritise AI integration in your company: a quantitative model
Where do you start implementing AI? Most people answer with a hunch. But deciding where AI goes is a quantifiable problem: it has variables, formulas and a rankable result. This article is that model, with a case worked through step by step.
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First Optimizer, Second AI: why the real brake on AI is not the tool
The industry has fallen in love with "AI-first". But the real bottleneck in adoption was never technological: it is the absence of a way of thinking about processes. A reflection on the discipline that should come before touching AI.
- Administration and finance Invoices, collections and closing without typing.
- Operations The company’s core process, without human dependencies.
- Customer service Verified answers, escalation when it is warranted.
- Sales and marketing Qualified leads and proposals in hours.
- People and talent Recruitment, onboarding and employment paperwork.
Not sure where to start? Measure your organisation’s AI maturity for free in 3 minutes, or book 30 minutes of direct diagnostic.
Let's start
Let's talk about your operation.
Tell us what costs more than it should. We come back with a concrete plan, realistic timelines and a clear yes or no.
contacto@plantekia.com