A living model of your company. Not a search box.

Identity resolution

John Smith, jsmith, john-s on GitHub and U12345 on Slack resolve to one person, with confidence scoring and kept aliases. The same goes for customers, vendors, systems and projects.

Organizational ontology

A flexible, extensible model of what the company considers meaningful (people, teams, goals, systems, incidents, decisions) and how they relate, instead of everything hard-coded around documents.

Event model

Understands events as well as entities: who did what, to what, when, why and with what result. Events are what build the organizational timeline.

Immutable organizational event log

An event-sourced log from which current state is derived, giving history, provenance and the ability to reconstruct what the company knew on any date.

Provenance graph

Ships today as per-fact provenance

Every fact records its sources, extraction method, timestamp, confidence, author and last verification, so uncertain information never passes as truth.

Fact lifecycle

Ships today as bi-temporal facts

Facts move from observed to confirmed, disputed, superseded or expired. Old facts keep their validity windows instead of being deleted: a bi-temporal knowledge system.

Confidence modeling

Each assertion carries confidence, authority, freshness and source count. Trust combines source authority, agreement between sources, freshness and extraction confidence.

Source authority hierarchy

Configurable authority from board-approved policy down to AI inference, varying by topic: CODEOWNERS can outrank Notion for ownership, Salesforce outranks Slack for pipeline.

Freshness awareness

Ships today as the freshness guardrail

Notices when a fact has aged, such as an account owner who left six months ago, downgrades its confidence and asks for verification. Knowledge decay, managed.

Automatic knowledge reconciliation

When Notion, Jira and Slack disagree about an owner, the brain reconciles them into previous owner, current owner, effective date and evidence.

Organizational semantic layer

Ships today as the Jargon Decoder

Builds a glossary of the company’s own language (“Red Account”, “Launch Ready”, “P0”), with who uses each term and what it relates to.

Acronym resolution

Ships today as the Jargon Decoder

Infers the meaning of the hundreds of acronyms every company accumulates, including the internal-only ones, from how they are used.

Concept graph

Connects documents, people, projects and decisions around concepts like Zero Trust, RBAC or SSO, which surfaces knowledge nobody knew to look for.

Organizational taxonomy management

Maps how different teams classify the same things, so Marketing’s “Enterprise” lines up with Finance’s “Strategic”.

Contextual retrieval

The same question gets a different emphasis for different askers: an engineer sees tickets and code changes, a CEO sees launch status, customer impact and risk.

User-specific context

Knows who is asking, their team, what they own and work on, their permissions and the level of detail that suits them.

Working memory

Understands the task at hand. Before a meeting with Acme it gathers the last meeting, open commitments, recent tickets, renewal status and internal risks.

Personal work brain

Every employee gets a private view: what to do today, what they promised, what they’re waiting on, meetings to prepare for and people waiting on them.

Team brain

Team views such as an Engineering Brain (initiatives, incidents, releases, architecture decisions, ownership gaps) and a Sales Brain (pipeline, objections, renewals, risk accounts).

Executive brain

Leadership sees company health, revenue, customers, execution, risks and the decisions required, and can simply ask “What needs my attention?”

Decision inbox

Pending decisions arrive with context, options, arguments, evidence, affected parties and similar past decisions. Humans still decide; the brain prepares the decision.

Organizational “open loops”

Tracks everything started but not resolved: unanswered questions, incomplete commitments, incidents without root cause, tasks without owners, decisions awaiting approval.

Question tracking

Extracts questions that would otherwise disappear in chat and tracks who asked, the likely owner, status and answer, so unanswered questions become visible.

Assumption tracking

Records project assumptions (“latency stays under 200ms”, “no extra compliance review”) with owner, evidence and dependent projects, and alerts when one stops holding.

Hypothesis tracking

Links each hypothesis to its experiment, metric and result, so the company stops re-testing the same ideas without memory of the last attempt.

Experiment memory

Answers “Have we tried this before?” with the earlier experiment, its results and the discussion around it.

Failure memory

Preserves incidents, failed experiments, cancelled projects, lost deals and architecture mistakes, and why they happened. Companies forget failures faster than successes.

Lessons learned graph

Every important project or incident leaves queryable lessons (what worked, what failed, why, what we would change) for future initiatives.

Pattern detection

With enough history, spots patterns such as custom-SSO enterprise projects slipping 2–4 weeks because security review starts too late.

Cross-functional dependency graph

Maps how a marketing launch depends on a release, which depends on a feature, a security approval and a legal review, so “What could block the launch?” has an answer.

Resource graph

Connects people, budget, cloud resources, vendors, licenses and capacity to projects, which enables real capacity reasoning.

Organizational load

Understands workload structurally, such as where projects and responsibilities concentrate, without becoming employee surveillance.

Dependency risk

Flags critical flows with a single point of failure, such as a payment system that relies on one vendor API with no fallback.

Bus-factor intelligence

Goes beyond who knows a system to calculate continuity: how many people could run it, how current its documentation is, and what breaks if one person leaves.

Employee onboarding

Instead of 50 documents, a new hire asks “Teach me how this company works” and gets the product, architecture, processes, key people and current priorities.

Role transition

Knows what the previous person in a role owned, which meetings matter and which systems and processes they ran, so stepping into a role starts with context.

Employee offboarding

Before someone leaves, generates a transition plan covering systems and projects owned, open commitments, undocumented knowledge and external relationships.

Succession knowledge

Answers “If our Head of Infrastructure left tomorrow, what would become risky?” by finding the systems, vendors and decisions concentrated with one person.

Meeting preparation

Ships today as Meeting Prep

Before every meeting: the objective, participants, last discussion, outstanding actions, recent changes, relevant decisions and open questions.

Meeting follow-through

After the meeting, captures decisions, owners, deadlines, commitments and risks, and updates company state automatically.

Relationship intelligence

Understands each external relationship: executive sponsor, champion, technical contact, support history, renewal, open promises and product usage.

Stakeholder maps

For any initiative, infers the decision maker, owner, contributors, reviewers, affected teams and executive sponsor.

Communication network

Shows how work actually flows between teams, where links are strong, weak or a bottleneck, for organizational efficiency rather than surveillance.

Information flow analysis

Finds where information stalls, such as customer feedback that reaches Product but rarely Engineering, or incident lessons that never reach support.

Duplicate-work detection

Notices when two teams are independently building the same thing, and answers “Is someone already doing this?”

Duplicate-idea detection

Recognizes when an idea was discussed before and shows the earlier proposal, who was involved, why it stopped and what’s different now.

Redundant tool detection

Spots when several teams pay for different tools that perform the same workflow, and surfaces consolidation opportunities.

Policy intelligence

Treats policies as executable knowledge, so a question like “Can I expense a $1,500 monitor?” gets a direct, cited answer.

Compliance memory

Tracks policies, controls, evidence, audits, exceptions, owners and reviews across security, finance, HR and legal.

Policy-vs-reality detection

Detects when what actually happens deviates from what the policy says.

Process mining

Reconstructs how processes really run from event streams, such as lead to demo to security review to procurement to closed won.

Process deviations

Detects when a step is skipped, such as a deployment that went from review straight to production without approval.

Automation discovery

Finds workflows that repeat dozens of times a month with the same steps, the strongest candidates for automation.

Agent action layer

Agents do real work on top of the brain: create a ticket from a customer issue, draft a project brief, prepare a launch checklist, send meeting follow-ups.

Action authorization

Every action carries a policy level (read, suggest, draft, request approval, execute), so autonomy is granted deliberately.

Action audit trail

Every AI action records the agent, action, reason, evidence used, permissions, timestamp, approval and result.

Simulation

Answers “What happens if we postpone Project Atlas by a quarter?” by following the effects through launches, commitments, pipeline, revenue and hiring.

Scenario planning

Combines organizational dependencies with metrics to explore scenarios like slower revenue growth or a vendor shutting down.

Strategic memory

Keeps why each strategy was chosen (objective, alternatives, assumptions, expected and actual outcomes), so leadership can revisit decisions with real history.

Strategy-to-execution traceability

Links mission to strategy, goals, initiatives, projects, tasks and metrics, and finds the projects not connected to any company priority.

Metrics knowledge graph

Treats metrics as entities with a definition, formula, source, owner, dashboard and change history, so “Why does Finance ARR differ from Sales ARR?” has an answer.

Metric lineage

Traces any number from dashboard to definition, transformation, warehouse table and source system.

Metric anomaly understanding

When churn rises, searches releases, incidents, complaints, pricing and sales changes for correlated evidence, and keeps correlation distinct from cause.

Organizational pulse

A continuous Company Pulse for each role: wins, risks, decisions, deadlines, escalations, incidents, blockers and metrics moving unexpectedly.

Change intelligence

Answers “What changed since yesterday?” with only the meaningful changes: statuses, decisions, escalations, releases, new dependencies.

“What did I miss?”

Back after two weeks, an employee asks one question and gets the decisions, project changes, deadlines and people waiting on them, instead of reading Slack.

Attention management

Scores urgency, impact, relevance, dependency, authority and deadline, and surfaces what actually matters.

Intelligent notification suppression

Combines 17 related changes into one meaningful update instead of 17 alerts, so proactive AI stays bearable.

Company inbox

One inbox for what needs your decision, review, response or approval, plus detected risks, commitments coming due and knowledge needing verification.

Knowledge requests

When unsure, the brain asks instead of guessing: it suggests a likely owner, that person confirms, and the brain gets smarter.

Human-in-the-loop knowledge curation

People can confirm, correct, merge, reject, supersede or mark any piece of knowledge authoritative, making the knowledge base self-improving.

Knowledge reputation

Learns over time which sources are reliable, which improves answer quality.

Knowledge ownership

Every important knowledge object has an owner, which prevents knowledge rot.

Semantic access control

Permissions apply to entities, relations, properties, sources and time, not just documents: you may see a project’s name but not its budget.

Inference permission safety

The brain never infers restricted facts indirectly, such as hinting at a promotion from compensation data the asker can’t see.

Knowledge firewall

Executive, HR, legal, security, general and customer-specific knowledge stay in separate contexts, so restricted information never blends into an answer.

Multi-company boundaries

Tenant boundaries are absolute: no embeddings, caches, training artifacts or agent memory ever cross from one company’s brain to another.

External knowledge

Connects market news, competitor releases, regulations and public filings to internal knowledge, such as which active deals need a feature a competitor just shipped.

Competitive intelligence

Maintains competitors, products, pricing, positioning, customer mentions and win/loss reasons, so “Why do we lose against Competitor X?” has evidence.

Market memory

Tracks how the company’s assumptions about its market changed over time, and why.

Customer voice aggregation

Unifies feedback from calls, tickets, NPS, communities, email, reviews and feature requests, and clusters the recurring needs.

Idea graph

Recognizes that a Slack question, a meeting comment and a ticket are all the same idea, such as offline mode.

Innovation memory

Preserves brainstormed ideas without polluting authoritative company state, keeping facts, decisions, proposals, ideas and hypotheses distinct.

Argument graph

For important decisions, captures the reasoning on each side and connects it to evidence and participants.

Counterfactual reasoning

Explores questions like “If we had chosen Kubernetes, what would have changed?”, always labeled as hypothetical, never as fact.

Organizational causal graph

Links causes to effects across the company, each link carrying its evidence strength, enabling deeper analysis over time.

Root-cause memory

For incidents, failures, churn and missed deadlines, records the immediate cause, contributing factors, root cause, and corrective and preventative actions.

Organizational anti-pattern detection

Finds systemic problems: projects that keep launching without security review, requirements that keep reaching engineering late, the same incident recurring.

“Why are we doing this?”

Click any task and traverse upward through feature, project and initiative to the company goal it serves.

“What does this affect?”

Traverse downward from any decision to the projects, features, customers and revenue it touches. Impact analysis is core.

Work graph

One unified representation of how the company actually works: people, goals, projects, tasks, decisions, systems and customers, connected.

Digital twin of the company

A continuously updated model of structure, people, knowledge, work, systems, customers, decisions, processes, dependencies, metrics, risk and history.

Company API

Beyond chat, an API lets other internal applications query the brain for entities, decisions, timelines and context.

Company Brain as MCP/tool layer

Agents call the brain as a tool to find owners, decisions, commitments, impact and context, with permissions enforced.

Context compiler

Instead of dumping 500 documents into a model, it builds the smallest, highest-quality set of facts for the task: intent, entities, permissions, graph, time and authority.

Memory compiler

The write side of the brain: every message, meeting and document passes through entity, event, decision, commitment and fact extraction, then reconciliation into company state.

Episodic versus canonical memory

Keeps what was said (episodic) separate from what is true (canonical), and derives canonical state from episodic history.

Evidence-preserving summarization

Summaries never destroy detail. A CEO reads one sentence; an engineer drills down to the exact Slack message behind it.

Knowledge compression

Progressively compresses years of history (10,000 messages into events, decisions and major developments) while raw evidence stays recoverable.

Memory retention policies

Data carries retention periods, legal holds, deletion and archival policies, and the brain honors the source system’s requirements.

Right-to-delete propagation

When something is deleted at the source, lineage carries the deletion through to extracted facts, summaries and derived relationships.

Knowledge versioning

Documents have versions and entities have states over time, so time travel works naturally.

Query time travel

First-class questions like “What did we know before the outage?” or “Who owned the system in March?”

Organizational replay

Replays the sequence of events leading up to an outage or decision, for learning and audits.

“Explain this company to me”

A new leader asks once and gets what we sell, who buys it, how we make money, key teams and systems, strategy, current problems and recent decisions.

“How does X work here?”

Shows the documented process next to the observed one, for releases, sales handoffs, promotions or product development.

Company-wide memory search

Search by keyword, meaning, entity, relationship, time, person, decision, event or concept, such as decisions involving AWS during 2025.

Exploration UI

You don’t need to know what to ask: click through relationships and traverse the company graph.

Company map

Visualizes the organization as a strategy map or a dependency map, from goals through teams and systems to customers.

Knowledge heatmap

Shows where the company’s knowledge is strong, weak, outdated, held by one person, or conflicting.

Organizational health scorecards

Separate, evidence-backed scores for knowledge coverage, decision clarity, ownership, documentation freshness, dependency resilience and commitment health, not one meaningless number.

Brain skills marketplace

Installable skills (Incident, Sales, Hiring, Architecture, Customer and Compliance Intelligence) that all run over the same brain.

Custom agent builder

Build agents like “Every Monday, find customer commitments engineering may miss” that use the brain instead of querying dozens of systems.

Brain triggers

Events invoke agents: a new commitment, a conflicting status or an incident can start work automatically.

Self-healing knowledge

The brain repairs itself: it detects an unknown owner, suggests a likely one, asks a human to verify, and updates the graph.

Self-improving ontology

Notices new concepts that keep appearing, such as “Design Partner”, and proposes adding them to the ontology.

Self-improving extraction

When someone corrects it (“that was a proposal, not a decision”), it learns the pattern for this organization.

Organizational language model

Learns how this specific company communicates through its ontology, glossary, entity graph, authority rules and history, not necessarily model training.

Organizational identity

Understands mission, values and cultural norms, and separates written policy, historical precedent and common practice when asked how things are normally done.

Precedent search

Answers “Have we faced this before?” with similar escalations, incidents, pricing exceptions or migrations, and what happened.

Exception memory

Tracks each rule’s exceptions (who approved them, why, for how long and how broadly) so exceptions don’t become invisible precedent.

Organizational promises

Models internal, customer, partner, public, regulatory and contractual promises separately, because each carries different risk.

Obligation graph

The general form of every commitment: obligation, owner, beneficiary, due date, source, status and penalty or risk.

Attention graph

Shows where the company’s attention is going, such as a quarter of leadership discussion on the AI platform. Trend analysis, not employee monitoring.

Emerging-theme detection

Sees hundreds of small signals and names the theme, like security review delays appearing across four projects at once.

Weak-signal detection

Catches issues before they land: complaints creeping up, objections repeating, scaling worries in engineering threads. Individually small, together meaningful.

Organizational anomaly detection

Spots anomalies (no release in six weeks, a customer’s cases jumping from 2 to 17, cloud cost up 80% without usage) and connects them to company context.

Decision-quality feedback

Compares each decision’s expected result with the actual one, so the company learns which kinds of decisions it gets right.

Organizational learning loops

Closes the loop from decision to action, outcome, learning and an updated policy or process. Most companies stop at action.

Company Brain as institutional memory

People leave, teams change, tools change, Slack scrolls away and decisions are forgotten. The brain is the memory that stays.