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.

Where it hurts

Deciding blind.

01

Data scattered around.

ERP, CRM, spreadsheets and forms. The same indicator comes out differently depending on who calculates it.

02

Reports that lag.

The report lands on Wednesday with data from the previous Friday.

03

Analytics nobody uses.

Beautiful dashboards that do not answer the question the manager is actually asking.

Where we come in

Four ways in.

We start with the three questions that really get asked every week, not with the full catalogue of metrics.

  1. 01

    Source unification

    A warehouse with the data from your systems, with agreed definitions for every metric.

  2. 02

    Dashboards that answer questions

    Designed around the real decisions of the business, not around what the tool ships with.

  3. 03

    Deviation alerts

    The system warns when an indicator leaves its range. Nobody has to watch the dashboard.

  4. 04

    Natural-language queries

    Ask in text and get the figure along with the query that produced it. Verifiable.

How the process ends up
Services involved

What it is built with.

Related reading

Before you call us, read this.

  1. Method · 13 min

    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.

  2. Reflection · 7 min

    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.

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