Shared meaning
Objects, concepts, relationships, models, taxonomies, rules, and Use Cases stay linked instead of being flattened into unrelated chunks.
The work substrate for business AI
AI has repositories for code. Legra provides a living graph for every business process.
Coding agents enter a durable, versioned, testable corpus that a community improves one reviewed change at a time. Most business agents enter scattered documents, temporary context windows, and disconnected applications. Legra gives them the missing corpus of work.
The next productivity revolution is a corpus revolution.
Models can already read, write, reason, retrieve, and call tools. Their business impact compounds only when useful work improves a shared body of knowledge that later sessions can trust and extend.
From software to every domain
Generalize the repository without reducing business knowledge to files.
Code repositoryLegra workspace
Source, schemas, docs, and testsGraph knowledge, models, documents, media, policies, and tests
Issues and project contextUse Cases, Stories, tasks, conversations, decisions, and outcomes
Branches and working treesIsolated proposals, experiments, agent work, and what-if analysis
Builds and continuous validationSemantic constraints, rules, inference, tests, and validation
Review and mergeHuman and agent review before accepted knowledge joins the corpus
Commit history and attributionVersion history, provenance, task trees, and activity evidence
Beyond retrieval
RAG can find a passage. It cannot be the system of work.
Retrieval and model inference remain useful components. The substrate must also preserve meaning, authority, change, and evidence across people, agents, applications, pipelines, and organizations.
Enforceable authority
Identity, workspace boundaries, permissions, budgets, policies, and review paths constrain what an agent may read, spend, or change.
Reviewable change
Agents can work on branches, validate proposed knowledge, and submit bounded improvements without silently rewriting the shared state.
Durable evidence
Inputs, tools, actors, authority, decisions, outputs, costs, failures, and accepted results remain connected to the work that produced them.
One connected corpus
The graph is connective tissue, not a media restriction.
Structured knowledge and arbitrary content stay organized around the objects, relationships, people, agreements, and Use Cases they concern. Communication becomes reusable context instead of another isolated inbox or transcript.
Enterprise dataOntologies and taxonomiesRules and policiesUse Cases and StoriesTasks and decisionsDocuments and datasetsImages, audio, and videoMessages and emailHuman-agent conversationsTests and validationProvenance and historyRights and economic evidence
Perpetual learning machine
Every accepted contribution improves the starting point.
01
Inherit
Begin from the current governed corpus instead of rebuilding organizational context from a prompt.
02
Understand
Resolve relevant graph objects, files, conversations, policies, Use Cases, and prior evidence.
03
Work
Perform bounded human or agent tasks with explicit authority, tools, inputs, and budgets.
04
Validate and review
Check semantics, rules, tests, provenance, and the proposed change before acceptance.
05
Compound
Merge accepted knowledge and evidence so every later participant begins from a better corpus.
Durable learning does not require continuous model retraining.
The learning accrues in the governed graph, content, history, tests, and evidence. Models and agent frameworks remain replaceable consumers and contributors.
Put the corpus to work
Give the next human or agent session something worth inheriting.
Start with one bounded workspace and one valuable Use Case. Connect the knowledge, authority, behavior, and evidence it needs; then let each completed cycle improve the shared corpus.