Organizational memory for AI context.
FELLOW is a federated experience layer that supplies authorized, action-specific context to any model, agent, or workflow, and learns from verified outcomes. Not a chatbot. Not a vector database. The continuity and governance layer underneath them.
- The problem
- Every AI request starts from zero. The user re-explains context the organization already knows.
- The rule
- Information becomes useful experience only when its scope, source, authority, evidence, and outcome history are known.
One line in, twelve decisions out.
A user shouldn't have to manually find, reconcile, and explain everything a system already knows before an agent can act on it.
"Prepare a technical implementation plan for customer deduplication." Objective submitted by an engineering lead
- Who is asking, and with what authority
- Which organization, project, and customer apply
- What the user already knows
- What the company has already decided
- What current records actually say
- Which procedures have worked before
- Which constraints and policies apply
- What information is still missing
- What the system is permitted to do
- What requires human review
- Which tools or agents should participate
- What should be learned from the result
Three planes, never implicitly merged.
Personal, organizational, and action-time context are governed separately by design, federated rather than pooled into one unrestricted store.
Personal Experience
Private and work-scoped knowledge about an individual: preferences, responsibilities, prior decisions, recurring tasks, accepted corrections, and working patterns. Never exposed to the organization without explicit permission.
Organizational Experience
Shared knowledge owned by a team, project, customer account, product, or system: policies, decisions, procedures, evidence, exceptions, and verified lessons, promoted intentionally, never by accident.
Action Plane
The request-time layer that resolves identity and authority, retrieves applicable experience, incorporates live data, assembles an action packet, and records what actually happened.
One request, four ways of knowing.
Underneath the three planes, every request is resolved through four independent structures, so retrieval never depends on a single, fragile index.
Hierarchical structure
Every record sits inside a strict ownership tree — tenant, client, project, person. Scope is enforced there before anything is ranked or returned, not filtered after the fact.
Semantic index
Meaning-based retrieval surfaces relevant experience even when the wording never matches, ranked by relevance to the request instead of keyword luck.
Graph network
Facts link outward to what superseded them, what they were derived from, and what else shares their subject, so retrieval can follow a record's lineage instead of returning it in isolation.
Event ledger
Every raw observation is preserved exactly as it arrived, immutable and separate from what's since been verified, so nothing is silently rewritten.
Every item, cited. Every exclusion, explained.
The action packet is what FELLOW hands to a model or agent: bounded, ranked, and traceable back to the record and evidence that justified its inclusion.
"Draft a migration plan for the customer master database."
Mandatory Constraints
- WEB_SCRAPING dataset: analysis only, no production sync[1]
- Item-level approval required for destructive operations[2]
Applicable Decisions
- Target platform approved: managed Postgres, region us-east[3]
- Prior migration failed on unindexed foreign keys — checklist attached[4]
Conflicts Surfaced
- Customer contract exception overrides standard retention policy[5]
Sources & Authority
Not differentiated by storing memory.
Any vector database can store an embedding. Trustworthy retrieval requires knowing more than what a record says.
Ownership
Every record has an explicit owner and scope. Personal, team, company, and customer knowledge are never implicitly merged.
Authority
Why an assertion should be trusted, tracked separately from confidence. Explicit policy always outranks inferred preference.
Evidence
Immutable source material behind every assertion, preserved so experience can be reprocessed as extraction logic improves.
Temporal validity
Expired and superseded records are excluded by default. Nothing stale gets handed to a model silently.
Policy-controlled retrieval
Access and purpose are evaluated before results ever reach a model. Deterministic controls precede model judgment.
Outcome measurement
A memory system without verified outcomes accumulates information, not experience. FELLOW closes that loop.
Promotion across boundaries
Personal experience becomes team or company experience only through explicit policy, evidence, and approval.
Agent neutrality
No critical experience lives only inside one agent runtime, model session, or vendor-specific memory feature.
What FELLOW deliberately doesn't do.
Discipline about scope is part of the trust model.
- Replace workflow engines like n8n, Temporal, or Airflow
- Replace agent runtimes like Claude Code or custom agents
- Act as a general-purpose document management system
- Serve as the system of record for CRM, ERP, or source code
- Treat model-generated confidence as factual certainty
- Silently convert personal observations into company policy
- Autonomously perform high-impact actions without authorization
- Use a single global vector index as a security boundary
Join the waiting list.
FELLOW is rolling out to a small number of teams. Leave your details and we'll reach out as access opens up.