Aiguro

Aiguro for Procurement

AI-found savings from your own spend data

Turn your spend data and supplier contracts into a defensible savings pipeline - with an AI expert that finds, challenges and documents every hypothesis. See a real hypothesis card, built on a supplier consolidation opportunity with contract evidence attached.

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

Hypothesis card: Consolidate fastener spend onto two framework suppliers The worked hypothesis from the procurement sample workspace. Method QU-2 Consolidation of suppliers, potential EUR 147,000 to 221,000 (8-12% of the EUR 1,839,900 addressable spend: rebate tier plus price harmonisation.), effort 2 of 5, complexity 2 of 5. Supported by framework-agreement-brightbolt.md, contradicted by supply-agreement-kessel.md on 'Volume movable within 12 months'. Hypothesis In progress Consolidate fastener spend onto two framework suppliers METHOD QU-2 Consolidation of suppliers POTENTIAL EFFORT COMPLEXITY EUR 147k - 221k 8-12% of the EUR 1,839,900 addressable spend: rebate tier plus price harmonisation. EVIDENCE Supports framework-agreement-brightbolt.md Contradicts supply-agreement-kessel.md challenges: Volume movable within 12 months From spend-cube-fasteners-2025.csv Sample workspace, fictitious data

The problem

"Our spend data is sitting in Excel exports, and nobody has time to turn it into anything."

"Every savings idea dies in a slide deck before it reaches the CFO."

"We have a savings target, but no dedicated analytics function to hit it with."

How it works

  1. Upload spend cubes, supplier contracts, supplier lists.
  2. Findings Aiguro's discovery strategies surface price, volume, specification and supplier consolidation signals from your documents, news and market events.
  3. Hypotheses findings become structured hypotheses with a method from the lever catalogue, potential, effort and evidence.
  4. Pipeline a savings pipeline ready to review, prioritise and export into the programme tool you already use.
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 spend cube, two supplier contracts and a supplier list turn into a supplier consolidation hypothesis with contract evidence attached - the kind of finding that used to take a consultant a week.

Every hypothesis draws on the method catalogue - price, volume, specification and supplier levers. 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, and connects it in a knowledge graph - so the same supplier idea is never proposed twice, and a new contract 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 team piloting the tool, no seat maths.

Pro priced per user, minimum 3 seats, workspaces scale with seat count, includes SSO. Built for procurement teams that outgrow a single workspace.

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 an analyst to use this? No - upload what you already have (spend exports, contracts) and Aiguro's discovery strategies do the first pass.

Does it replace our procurement tool? 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.