Your Organization's Wisdom —
Captured, Structured, and Inherited
By Every AI Agent
Mimir builds digital twins of how organizations think, decide, and operate. Every AI agent that connects inherits the full institutional context automatically — risk appetite, approval chains, regulatory obligations, and the lessons your team learned the hard way.
The well remembers what the organization forgets.
The Problem: Agents Start From Zero
Every AI agent begins every interaction with no knowledge of how your organization works.
Three categories of knowledge. All invisible to agents.
How you decide — risk appetite, approval chains, escalation paths. Lives in people's heads.
Why you decided — regulatory constraints, compliance obligations, lessons learned. Lost when people leave.
How things work — business logic, state machines, architectural invariants. Scattered across documents nobody reads.
When agents act without this knowledge, they make decisions your organization would never make.
Today, institutional knowledge lives in three places: people's heads, scattered documents, and tribal memory. When people leave, the knowledge leaves. When agents act without it, they operate blind.
The agent revolution needs a knowledge layer. Not a knowledge base. A living twin.
Six Layers of Institutional Wisdom
Each layer inherits from above and adds specificity as it descends. Authority lives at the top. Specificity lives at the bottom.
Industry
Regulatory standards, compliance frameworks, sector requirements. NIST, FedRAMP, DORA, EU AI Act, HIPAA, PCI-DSS. Curated by Mimir, updated continuously.
Organization
Your company's identity as executable knowledge. ISMS policies, risk appetite, brand voice, ethical standards, architectural principles. "How we do things here."
Division / Team
How this part of the company operates. Approval chains, escalation paths, communication norms, SLAs, decision authority. Different teams, different rhythms.
Product / Service
How this specific thing works. State machines, business logic invariants, architectural decisions, API contracts. The accumulated knowledge about what the team builds.
Repository / Asset
The most specific layer. Code standards, naming conventions, testing patterns, maintenance history, configuration decisions.
Session
Ephemeral context. The current task, recent corrections, active thread. Dies when the session ends — but corrections promote upward through the maturity lifecycle.
Inheritance Flows Down
A session at any layer inherits from every layer above it. An agent writing code inherits the repo's patterns, the product's architecture, the team's norms, the org's policies, and the industry's regulations — all at once.
Corrections Promote Up
When an expert corrects an agent, the correction enters a maturity pipeline. Same pattern across sessions? Product-level invariant. Across products? Organizational standard. The twin improves from the bottom up.
Lower Never Contradicts Higher
If the organization says "all PII must be encrypted at rest," no repo-level standard can relax that. The hierarchy is a constraint cascade. Authority at the top, specificity at the bottom.
From Raw Knowledge to Living Twin
Three paths in. One living, breathing digital twin out.
Ingest
Feed conversation transcripts, meeting recordings, policy documents, runbooks. Mimir extracts decisions, rules, constraints, and edge cases automatically.
Correct
When experts correct an agent's output, the correction enters the maturity pipeline. Pattern detection across corrections generates new standards automatically.
Import
Structured import for ISMS documents, compliance frameworks, architectural decision records. Mapped to the hierarchy automatically.
The Maturity Lifecycle
Knowledge isn't declared — it's earned. Every piece follows a lifecycle. Unused knowledge decays. Corrected knowledge sharpens.
Provisional
Pattern detected. Soft suggestion.
Solidified
Validated across sessions. Strong recommendation.
Reinforced
Battle-tested invariant. Violations flagged.
Enforced
Institutional law. Cannot be overridden.
Intelligent Injection
When any agent starts a session, Mimir selects the most relevant knowledge from the full hierarchy stack. Weighted by layer priority, task relevance, and maturity level.
400 tokens injected from a corpus of 550,000+
1,375x efficiency. The agent doesn't search the twin. The twin finds the agent.
Not Just Organizations. Any Long-Lived Entity.
The hierarchy works for anything that accumulates knowledge over time.
A House
Building codes, permits, structural plans, HVAC specs, every repair, every contractor, every decision. An agent managing this home knows the deck was permitted for 40 psf, the panel is at capacity, and the northeast corner leaks in high wind.
A Vehicle
Emissions standards, factory specs, TSBs, service history, modifications, OBD codes. An agent scheduling service knows your engine variant's timing chain fails at 90K and you're at 87K.
A Person / Family
Tax status, medical history, genetic context, career trajectory, insurance formulary. A medical agent knows your family cardiac history and current medications before it speaks.
A Project
Industry standards, organizational methodology, team norms, project-specific decisions. Every agent that touches the project inherits the accumulated wisdom of everyone who worked on it before.
The architecture is identical in every case. Six layers. Inheritance down. Corrections up. Authority at the top.
Knowledge Base vs. Digital Twin
A knowledge base is searched. A digital twin is inherited.
| Dimension | Knowledge Base | Mimir Digital Twin |
|---|---|---|
| How agents access knowledge | Search and hope for relevance | Injected automatically at session start |
| Knowledge structure | Flat documents, no hierarchy | Six-layer hierarchy with inheritance |
| What happens to stale content | It stays forever, misleading agents | Auto-demoted based on usage data |
| Learns from corrections | Never — manual updates only | Every correction sharpens the twin |
| Authority model | None — all content is equal | Constraint cascade — higher layers override lower |
| Context efficiency | Dumps everything into context | 400 tokens from 550K+ corpus |
| Survives turnover | Knowledge leaves when people leave | Twin outlives any individual contributor |
Not a Concept. In Production.
The engine powering Mimir has been running since December 2025.
Standards in corpus
178 code · 1,508 business · 229 regulatory
Token efficiency
400 injected from 550K corpus
Maturity stages proven
Provisional → Solidified → Reinforced → Enforced
Three ingestion paths operational. Industry layer seeded with NIST 800-53 (229 standards). Organization layer proven with full ISMS suites. Product layer active — 96 invariants committed in a single day.
Connects via MCP. Any MCP-compatible agent receives injected knowledge automatically. No integration code required.
6
Knowledge hierarchy layers
1,782
Standards in corpus
400
Tokens per injection
<2s
Relevance scoring + injection
Build Your Digital Twin
Start with a single repository. Scale to your entire organization.
Explorer
For individuals evaluating
- Industry layer (regulatory standards)
- 1 repository twin
- MCP integration
- Community support
Professional
$999/yr — save 16%
- Industry + Organization layers
- Unlimited repositories
- Maturity lifecycle
- Intelligent injection
- Correction pipeline
Business
$4,999/yr — save 16%
- Full six-layer hierarchy
- Team / division scoping
- Twin management dashboard
- Multi-team analytics
- All Professional features
Enterprise
$25K – $100K/yr
- Full hierarchy + marketplace
- API access + SLA
- SSO + onboarding
- Dedicated support
- Custom integrations
The buyer is a business leader, not an individual developer. You're not buying coding assistance — you're buying institutional memory preservation and agent enablement.
The agent revolution is here.
Every company deploying AI agents faces the same problem: the agents don't know how you work. They don't know your risk appetite, your approval chains, your regulatory obligations, or the lessons your team learned the hard way. They start from zero every time.
Mimir solves this by building a digital twin of your organization's knowledge — not a static knowledge base, but a living system that accumulates wisdom over time. Every correction makes it sharper. Every decision makes it deeper. Every agent that connects inherits the full institutional context automatically.
The question is whether your agents operate with your organization's wisdom — or without it.