Aiguro

Why not just ChatGPT or Copilot

Every buyer already has a generic AI assistant. The honest question is "why would I pay for another one". The answer is not "better chat" - it is that Aiguro is a system that accumulates your team's knowledge, and a chat window is only one way into it.

A generic AI assistant starts every conversation from zero and forgets when the tab closes. Aiguro remembers what your team uploaded, found, decided and rejected - per workspace, for as long as the workspace exists and within your own retention settings - and every new answer, finding and hypothesis is built on that.

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

What sits under the hood

Layer What it is What you see Why a chatbot cannot do it
Workspaces as the unit of knowledge A workspace carries its business area, focus, language and currency, its members with roles, and everything that belongs to it: documents, findings, hypotheses, data fields, memory. Every agent turn runs inside that context A shared team space instead of private chats; a colleague picks up where another stopped Chat tools are personal and conversation-scoped; "context" is whatever is pasted into the prompt
The knowledge graph as the system's memory Every document, finding, hypothesis, method, data field, table column, extracted entity, tag, web source and rejection becomes a node. Enrichment links them: supports, contradicts, challenges, similar to, duplicate of, same cost domain, uses method, derived from, temporal successor. Clusters group related items into themes Evidence and contradiction badges on a hypothesis, related hypotheses, possible-duplicate warnings, a signal timeline, the workspace map A chat log has no structure; nothing connects last month's contract to this week's news
Decisions that survive When a user dismisses a finding or deletes a hypothesis, the graph keeps a self-contained rejection record. Discovery generation checks new proposals against known items and clusters before saving The same idea is not proposed again; "already looked at this" instead of noise A chatbot happily suggests the same thing every session
Learned data knowledge Verified analysis work is recorded: which columns in two spreadsheets correspond, detected formats and units, which fields map to which real entities (suppliers, materials, plants). Per-document analysis notes steer the next analysis toward new angles Field suggestions appear on the next upload; re-analysing a document yields fresh findings, not repeats Every spreadsheet is a new file to a generic assistant
Agent memory with consent Durable facts about the user and the team, saved only after the user confirms the offer, reviewable and deletable "Should I remember that you report in GBP and exclude intercompany costs?" Consumer memory features are per person, opaque, and not team-scoped
Continuous, not prompted Discovery strategies run on schedules: news, world events, periodic cross-referencing of documents and hypotheses, document analysis Findings waiting in the inbox on Monday morning without anyone asking A chatbot only works while someone types
Structured outcomes Hypotheses with a method from a curated lever catalogue, potential, effort, confidence, evidence, and an immutable changelog; linkable to real supplier and material lists; exportable to the programme tool Something a CFO accepts, not a paragraph to copy into a slide Free text with no schema, no history, no audit trail
Governance built in One isolated database per team, EU hosting, no training on customer data, retention controls, SSO, role-based workspace access The security page IT and legal actually read Shared model endpoints, unclear data handling, personal accounts

The compounding curve

In week one, the value is roughly equal to a good chatbot: first uploads, first findings. By month one, the graph is linking documents, findings and hypotheses, and duplicates and contradictions start to surface. By month three, rejections, field mappings and memory steer every new analysis, and findings arrive without anyone prompting. By month six and beyond, the workspace is your team's institutional knowledge - the switching cost is the graph itself.

The compounding curve Illustration: in week one Aiguro is worth about as much as a good chatbot. By month one the graph links documents, findings and hypotheses; by month three rejections and field mappings steer every analysis; by month six the workspace is the team's institutional knowledge. A generic assistant stays flat. Value to your team Week 1 Month 1 Month 3 Month 6+ Generic AI assistant Aiguro workspace First uploads, first findings Graph links documents, findings, hypotheses Rejections and mappings steer every analysis Your team's institutional knowledge

See the difference on your own data

The compounding only shows once your own documents are in the workspace. Once your team is set up, the sample workspace already has a graph, evidence badges and a related-hypotheses drawer waiting on the first screen.

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