Aiguro

Aiguro for Finance

a board-ready pipeline of cost and efficiency measures

Turn cost-centre data into a board-ready pipeline of cost and efficiency measures - with confidence ratings, potential estimates and an audit trail your CFO can stand behind. See a real hypothesis card with its evidence and changelog attached.

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

Hypothesis card: Renegotiate indexation in service contracts before renewal The worked hypothesis from the finance sample workspace. Method CO-15 Contract management, potential EUR 56,000 to 97,000 (4-7% of EUR 1,388,700 indexed service spend per year.), effort 2 of 5, complexity 3 of 5. Supported by facility-services-agreement.md, contradicted by managed-it-services-agreement.md on 'Share of indexed spend renegotiable in 2026'. Hypothesis In progress Renegotiate indexation in service contracts before renewal METHOD CO-15 Contract management POTENTIAL EFFORT COMPLEXITY EUR 56k - 97k 4-7% of EUR 1,388,700 indexed service spend per year. EVIDENCE Supports facility-services-agreement.md Contradicts managed-it-services-agreement.md challenges: Share of indexed spend renegotiable in 2026 From cost-centre-report-2025.csv Sample workspace, fictitious data

The problem

"We need a defensible pipeline of cost-out measures, not another spreadsheet of ideas."

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

"We have a target from the restructuring plan, but no analytics function to build the case."

How it works

  1. Upload cost-centre reports, budget files, existing measure lists.
  2. Findings Aiguro's discovery strategies surface cost-out signals from your documents, news and market events.
  3. Hypotheses findings become structured hypotheses with a method, potential, effort, confidence and evidence.
  4. Pipeline a pipeline of measures ready to prioritise, present 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 cost-centre report, a budget file and a measure list turn into a board-ready hypothesis with a confidence rating and potential estimate attached.

Every hypothesis draws on the method catalogue, covering cost-out programme structure and defensible potential estimates. 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 it all in a knowledge graph - so a hypothesis carries evidence and a full, immutable changelog rather than a paragraph you have to reconstruct from memory before the board meeting.

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 finance team piloting the tool, no seat maths.

Pro priced per user, minimum 3 seats, workspaces scale with seat count, includes SSO. Built for finance teams running a company-wide cost-out programme.

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

Is this a BI tool? No - Aiguro proposes structured hypotheses for you to validate, it does not replace your reporting stack.

Can we show this to the board? Yes - every hypothesis carries potential, effort, confidence and an immutable changelog.

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.