Aiguro

Aiguro for Production

scrap, energy and OEE potential from your own data

Turn production output, maintenance logs and energy invoices into cost-per-unit and efficiency hypotheses - with evidence from your own plant data attached. See a real hypothesis card built on a scrap or energy finding.

Request access tell us about your team and we will get in touch to set up your workspace.

Hypothesis card: Cut Line 3 scrap with standardised changeovers (SMED) The worked hypothesis from the production sample workspace. Method LN-15 SMED, potential EUR 37,000 to 52,000 (50-70% of the excess scrap on Line 3, valued at EUR 4.20 per part.), effort 2 of 5, complexity 3 of 5. Supported by maintenance-service-agreement.md, contradicted by press-p3-warranty-terms.md on 'Setup changes possible in-house'. Hypothesis In progress Cut Line 3 scrap with standardised changeovers (SMED) METHOD LN-15 SMED POTENTIAL EFFORT COMPLEXITY EUR 37k - 52k 50-70% of the excess scrap on Line 3, valued at EUR 4.20 per part. EVIDENCE Supports maintenance-service-agreement.md Contradicts press-p3-warranty-terms.md challenges: Setup changes possible in-house From production-output-h2-2025.csv Sample workspace, fictitious data

The problem

"Our cost-per-unit target keeps slipping and we don't have an analyst to chase every lever."

"Energy prices moved and nobody has time to work out what it actually costs us."

"Every improvement idea from the shop floor dies before it becomes a tracked measure."

How it works

  1. Upload production output files, maintenance logs, energy invoices.
  2. Findings Aiguro's discovery strategies surface scrap, energy, maintenance and yield signals from your documents, news and market events.
  3. Hypotheses findings become structured hypotheses with a method, potential, effort and evidence.
  4. Pipeline a pipeline of cost and efficiency levers ready to prioritise and track through to implementation.
How Aiguro works Your business data, documents and market news feed scheduled discovery. Discovery turns them into findings, findings into hypotheses with a method, potential and evidence, and hypotheses into a prioritised pipeline of cost and efficiency levers. Underneath, a knowledge graph remembers every document, finding, decision and rejection, feeds that knowledge back into findings, hypotheses and the pipeline, and steers the next run. Your data Business data Documents News, markets Discovery Scheduled strategies analyse and cross-reference Findings Signals with evidence Hypotheses Method Potential Evidence Pipeline Prioritised Knowledge graph remembers every document, finding, decision and rejection remembered steers the next run

Proof

A worked example from the sample workspace: a production output file, a maintenance log and energy invoices turn into a scrap or energy hypothesis with the source data attached as evidence.

Every hypothesis draws on the method catalogue, covering LEAN methods, OEE, maintenance, material yield and energy. Your data stays yours: one isolated database per team, EU hosting, no training on customer data.

Why not a chatbot

A generic AI assistant starts every conversation from zero. Aiguro remembers what your team uploaded, found, decided and rejected, connecting maintenance logs, energy invoices and production data in a knowledge graph - so the same improvement idea is never proposed twice and a new invoice is automatically checked against what you already know.

Value compounds: in week one it looks like a good chatbot; by month three the graph is steering every new analysis and findings arrive without anyone asking. See the full picture on the comparison page.

A chat window forgets, the Aiguro workspace remembers Left: a generic AI assistant starts every session from zero and suggests the same idea again. Right: in an Aiguro workspace every document, finding, hypothesis and rejection is a node in a knowledge graph. One contract supports the hypothesis, another contradicts it, market news supports the finding it was derived from, and a rejected idea that is similar is not proposed again. Generic AI assistant MONDAY Have you tried renegotiating this contract? starts from zero NEXT WEEK Have you tried renegotiating this contract? starts from zero NEXT MONTH Have you tried renegotiating this contract? starts from zero Aiguro workspace Every item becomes a node; enrichment links them supports derived from derived from contradicts similar to supports Contract A Spend export Finding Market news Contract B Rejected idea Hypothesis Not proposed again - already rejected

Pricing

Small Team a flat monthly price, 3 users, 1 shared workspace. The learning plan for a plant team piloting the tool, no seat maths.

Pro priced per user, minimum 3 seats, workspaces scale with seat count, includes SSO. Built for production teams running a CI or LEAN programme across sites.

Enterprise custom, sales-led, for company-wide rollouts, SSO enforcement and negotiated contracts. Includes the Valuedesk integration: connect Valuedesk and export hypotheses to it. Talk to sales.

AI usage runs on a monthly credit budget: 5,000 credits on Small Team, 2,000 credits per user on Pro, custom on Enterprise. Chat, discovery runs and document analysis draw on it in proportion to the AI work they do - a quick question costs a few credits, a deep discovery run more. When the budget is used up, AI features pause until the next billing period or an upgrade.

FAQ: seats and workspaces every user also gets a private default workspace that never counts against your quota; shared workspaces are what the quota limits.

Security and compliance

Every team gets its own isolated database - never a shared schema. Data is hosted in the EU, and nothing you upload is used to train models. Pro and Enterprise include SSO (Entra ID); role-based access controls who can see and edit each workspace. A standard Data Processing Agreement (GDPR Art. 28) is available for download, with the sub-processor list published alongside it.

One isolated database per team, hosted in the EU Every team gets its own database, never a shared schema, inside EU hosting, with SSO and role-based access. AI models answer requests from the workspace and are not trained on customer data. EU hosting Your team SSO, role-based access Own database Another team SSO, role-based access Own database Another team SSO, role-based access Own database AI models Answer requests from your workspace No training on your data

FAQ

Do I need a data analyst on the team? No - upload what you already have (output files, maintenance logs, invoices) and Aiguro's discovery strategies do the first pass.

Does it replace our CI or LEAN programme tooling? No - hypotheses export into the programme tool you already use; Aiguro is where they are found and validated.

What happens to our data if we cancel? It stays yours and visible during the read-only period after cancellation; nothing is deleted immediately.

Can we try it before committing? Request access and we will get in touch to find the right way to start with your own documents.