Aiguro

Aiguro for Logistics

freight and warehousing savings cross-checked against contracts

Turn shipment data, carrier contracts and tariff tables into freight, warehousing and inventory savings hypotheses - cross-checked against the contracts you actually hold. See a real hypothesis card built on a freight consolidation finding.

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

Hypothesis card: Consolidate Leeds part loads into scheduled full truckloads The worked hypothesis from the logistics sample workspace. Method PC-5 Optimization of capacity utilization for logistics purchases, potential EUR 145,000 to 211,000 (55-80% of the modelled annual saving of EUR 263,252 from moving Leeds LTL to scheduled FTL.), effort 2 of 5, complexity 2 of 5. Supported by ftl-agreement-brennholt.md, contradicted by ltl-framework-transvia.md on 'LTL volume movable to FTL'. Hypothesis In progress Consolidate Leeds part loads into scheduled full truckloads METHOD PC-5 Optimization of capacity utilization for logistics purchases POTENTIAL EFFORT COMPLEXITY EUR 145k - 211k 55-80% of the modelled annual saving of EUR 263,252 from moving Leeds LTL to scheduled FTL. EVIDENCE Supports ftl-agreement-brennholt.md Contradicts ltl-framework-transvia.md challenges: LTL volume movable to FTL From shipments-q4-2025.csv Sample workspace, fictitious data

The problem

"Freight rates keep moving and nobody has time to check whether our contracts still make sense."

"Every network redesign idea stalls because pulling the numbers together takes weeks."

"Tariff and service-level changes land on our desk with no time to model the impact."

How it works

  1. Upload shipment data, carrier contracts, tariff tables.
  2. Findings Aiguro's discovery strategies surface freight, warehousing, inventory and packaging 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 freight, warehousing and inventory levers ready to prioritise ahead of the next contract renegotiation.
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: shipment data, a carrier contract and a tariff table turn into a freight consolidation hypothesis cross-checked against the carrier contract as evidence.

Every hypothesis draws on the method catalogue, covering freight, warehousing, inventory and packaging 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, connecting shipment data, carrier contracts and tariff tables in a knowledge graph - so a rate change is automatically checked against what you already know, and the same idea is never proposed twice.

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 logistics team piloting the tool, no seat maths.

Pro priced per user, minimum 3 seats, workspaces scale with seat count, includes SSO. Built for logistics teams managing multiple carriers and lanes.

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 on the team? No - upload what you already have (shipment data, contracts, tariffs) and Aiguro's discovery strategies do the first pass.

Does it replace our TMS? 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.