OIDA is an organisational knowledge management platform. It turns unstructured company knowledge — decisions, documents, meetings, evidence, hypotheses, tacit expertise — into a computable, queryable graph that both humans and AI systems can reason over. Built by Kakashi Ventures.
Every organisation runs on knowledge — decisions, evidence, hypotheses, dependencies, tacit expertise — but this knowledge is scattered across documents, chats, decks, tickets and people's heads. Teams cannot answer basic questions: Is this decision still active? Has anything contradicted it? How much should we trust this? Who knows about this? What blocks this?
Traditional tools — wikis, knowledge bases, search — treat knowledge as documents to find, not as structured objects to reason over. As a result, organisations keep rediscovering the same things, executing plans based on outdated assumptions, and losing expertise when people leave.
OIDA treats organisational knowledge as first-class computable objects. Every decision, piece of evidence, hypothesis, question or dependency is stored as a typed node — an epistemic object — with explicit relationships (supports, contradicts, implements, supersedes, blocks, derives from) and computed properties (importance, confidence, freshness, urgency, controversy).
This turns knowledge into something queryable: humans can ask structured questions; AI agents can ground their reasoning in verified organisational context instead of hallucinating; contradictions are flagged the moment they enter the system; decisions carry temporal state and provenance.
AngelicaDB is the substrate layer of OIDA. It is a purpose-built knowledge graph database for storing organisational knowledge as computable epistemic objects. Each object has a typed class (DECISION, EVIDENCE, HYPOTHESIS, QUESTION, and more), a seven-axis coordinate that places it in the organisation's epistemic space, typed directed relationships to other objects, provenance metadata (source, author, timestamp, revision), and computed scores that evolve over time.
AngelicaDB supports temporal reasoning (decisions expire, supersede, get reinforced), contradiction detection (new evidence that conflicts with active decisions is flagged automatically), and confidence propagation (support and contradiction flow through the graph, updating trust scores). It is the source of truth that Veltha and Menrva build on.
Learn more about AngelicaDB →
Veltha is the ontology editor of OIDA. It defines the schema of your organisational knowledge: what classes of objects exist, what relationships can hold between them, what properties each class carries, and which constraints apply. A well-designed ontology is what makes knowledge computable rather than just textual — it lets the system enforce consistency, infer missing connections, and validate that new knowledge fits the model.
Veltha is built for knowledge engineers and domain experts to collaborate. It reads as a structured feed of the organisation's epistemic life — what is being decided, what is being contradicted, what is gaining importance — letting humans shape the ontology in response to what actually happens.
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Menrva is the reasoning engine of OIDA. It queries the AngelicaDB graph to answer organisational questions, surface hidden dependencies, and support decision-making. Menrva reasons over the typed relationships: if a decision SUPPORTS another decision that has been SUPERSEDED by new evidence, Menrva escalates this contradiction. If a question BLOCKS an execution path, Menrva raises its urgency.
Menrva is the layer that makes OIDA useful at the point of decision. It can be queried directly by people or integrated as the grounded-context backend for AI agents — turning an LLM from a stateless chatbot into an agent that actually knows the organisation.
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Modern AI agents fail inside organisations not because the models are weak, but because the agents have no memory of the organisation — no grounding in decisions, no awareness of contradictions, no provenance for the facts they assert. OIDA fixes this by giving agents a computable, queryable, verified knowledge graph to reason over.
An agent built on OIDA can cite the specific decision, evidence and provenance behind every claim; detect when its answer contradicts an active organisational stance; surface the people who own the knowledge it is using; and learn continuously as the graph grows. See how OIDA powers AI agents →
OIDA ingests organisational signal from existing sources — docs, meeting notes, tickets, chats, CRM, code — and extracts epistemic objects (decisions, evidence, hypotheses, questions) with their relationships. These objects are written to AngelicaDB under the ontology you defined in Veltha. Menrva then runs continuous reasoning over the graph, updating importance, confidence, freshness and urgency as new signal arrives.
The outputs are available through the Veltha feed for humans, through structured queries for analysts, and through the Menrva API for AI agents. The system is designed to be the shared epistemic substrate that aligns people and models inside the same organisation. Read the deep-dive →
It means turning the implicit knowledge that runs a company — decisions, rationale, evidence, dependencies, tacit expertise — into explicit, typed, queryable objects with relationships that machines can reason over. Not just search: structured inference.
No. Wikis store documents; OIDA stores epistemic objects. A document is a file you read; an epistemic object is a typed node with relationships, computed scores and temporal state. OIDA can ingest documents as sources of evidence, but it models the knowledge inside them, not just the text.
LLMs are pattern-based; OIDA is ground-truth-based. OIDA provides the organisational memory that AI agents need to reason correctly inside a specific company. Agents query OIDA for verified context and cite it in their answers, turning hallucination-prone chat into grounded decision support.
OIDA is built by Kakashi Ventures. The technical foundations are described in the research paper.
Visit the contact page or write to hello@projectoida.com for product enquiries, research@projectoida.com for academic collaboration, or press@projectoida.com for media.