Magni2d
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Governed knowledge for people + AI

Make your business
legible to AI.

Magni2d connects the knowledge in your documents, systems, and people, so your team and AI can work from the same governed context.

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Knowledge Context Governance Action

The missing layer

Your files contain the information. A lot of the business context still lives elsewhere.

Most companies already have much of the information AI needs. What’s missing is the context around it: which source is current, where a rule applies, how the company uses its own terms, and who owns the decision.

01

Authority

Which source is trusted? Which rule is current? Who is allowed to make the final call?

02

Relevance

Which policy matters for this product, customer, region, team, contract, or point in time?

03

Context

What does this company mean by the terms it uses, and how do its people, processes, systems, and decisions relate?

04

Ownership

Who owns the knowledge, who reviews changes, and where should an exception or unresolved question go?

Retrieval vs. governed understanding

The same question looks very different once the context is connected.

Question What does this new engineer need before their first day?
Retrieval alone Finds several onboarding checklists

But it still doesn’t know which one is current or how the person’s role, location, and employment type change the answer.

Governed context Person → role → location → current checklist → owners

Identifies the relevant steps, required access, and the people responsible for completing each one.

01 / Observe

Your business already runs on connected context.

But those connections are scattered across documents, systems, conversations, decisions, and the people who know how the work actually gets done.

02 / Structure

We make those connections explicit.

At the center is a business ontology: a map of your company-specific terms and the meaningful relationships between them. The source, relevance, and owner stay attached.

03 / Activate

Then we put that context to work.

With that context in place, AI can answer against the right sources, move routine work forward, and send exceptions back to the right person.

Built around your reality

Don’t replace your systems. Add the context between them.

01

Connect what exists

Start with the documents, systems, tools, and people your team already depends on. Magni2d works from what is already there.

02

Connect what matters

Make company-specific concepts and relationships explicit: products, customers, roles, processes, rules, systems, decisions, and exceptions.

03

Govern what becomes trusted

Keep the source, owner, relevance, review history, and changes attached to the knowledge instead of mixing everything into one pool.

04

Put the context to work

Once that context exists, you can reuse it for answers, workflows, and human decisions instead of rebuilding it for every new AI use case.

Knowledge has structure

The map carries more than facts.

The ontology carries more than facts. Each piece of knowledge keeps its business context: where it came from, where it is relevant, who owns it, what it depends on, and what happens when sources disagree.

From knowledge to action

A policy question becomes a decision with the right context.

Consider a simple request: a customer asks for an exception. The hard part is rarely finding a sentence that mentions the policy. The hard part is assembling the context that determines what should happen next.

ILLUSTRATIVE EXAMPLE
01 / Request “Can we make an exception for this customer?”

The question arrives through a person, ticket, inbox, or system your team already uses.

02 / Context Customer → contract → policy → owner → approval authority

Magni2d connects the request to the customer, contract, policy, owner, and approval path. It also shows where the answer stops and a person needs to decide.

03 / Action Prepare the answer. Cite the source. Route the exception.

AI can prepare the answer and handle the routine steps. The person with authority still makes the exception.

The context is reusable. Once the relevant customers, policies, roles, and approval paths are connected, another workflow can build on the same foundation.

Where it becomes useful

Start with one use case. Reuse the context that proves valuable.

Explore five focused applications across answers, policy guidance, automation, onboarding, and knowledge quality.

Explore use cases

Start with one real problem

Understand. Connect. Prove. Expand.

Start with one workflow or knowledge problem. Connect only the context it needs, prove that it helps, then expand when the evidence supports it.

Start with the work

Show us where context breaks down.

If your team has knowledge scattered across people and systems, or an AI use case that keeps failing for lack of company context, tell us where it breaks. We’ll start with the workflow, the knowledge it depends on, and the decision you’re trying to improve.

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