The context layer for enterprise AI
What your agents are allowed to know, in one governed place.
Powerful models are available to everyone. Vickee turns what your organization uniquely knows into governed, up-to-date context that people and agents can use, without exposing your systems of record.
Your people know the business and your agents scale the work. Vickee gives them shared context.
Your people understand the customers, policies, history, and nuance behind the work. Agents can turn that understanding into action at a speed and reach people cannot match. But people and agents cannot work together effectively when they operate from different, or incomplete, versions of what the enterprise knows.
- Context scattered across systems, documents, teams, and users
- Direct access to every source creates unacceptable risk
- A separate pipeline for every agent creates competing versions of the truth
- Approved knowledge in one shared, governed context layer
- Each person and agent gets exactly what they are authorized to know
- Every answer is grounded in identifiable sources
Your systems stay protected.
Your people remain accountable.
Your agents act from context the enterprise can inspect and govern.
Data in. Answers out.
Upload a file and Vickee takes it from there: storing, indexing, and analyzing it. Agents then search by meaning, match exact terms when precision matters, or ask a question and get a cited answer. Storage, indexing, and retrieval in one API, built for agents from the first line.
Every agent and every person works from the same live view of what your organization knows.
The whole integration, in five steps
- 01CreateA tenant and namespace. Nothing lands by accident.
- 02UploadOne call. Extraction, chunking, and embedding are automatic.
- 03IndexStatus is queryable, so agents know the moment content is searchable.
- 04SearchBy meaning, by exact term, by similarity, or as a question with a cited answer.
- 05RetrieveFull content for many files in one batch call, across namespaces.
Why teams put Vickee between their data and their agents.
Seven pillars, and where each one lives in the product.
Each pillar pairs the benefit you get with where it lives in the product. Pick one to see both.
Seven pillars
Every pillar is in the product today. Human-gated writeback is a setting your team turns on.
Organized by design
The benefit
Every piece of knowledge lands at an address. Tenants isolate customers or business units, namespaces separate domains, and directories and tags organize within them. The card catalog rolls it all up so agents and admins can see what’s known at a glance.
Where it lives in the product
In the product
- Multi-tenant with per-tenant namespaces, created explicitly so nothing lands by accident
- Full filesystem semantics: directories, move and rename, metadata sidecars, tags, file links
- Card catalog rollups at the namespace, tenant, and platform level
Every library needs a librarian.
CMS and CDP, CRM and marketing automation, commerce, ERP and finance, analytics: systems of record were built for controlled transactions. Autonomous software running open-ended queries is a different kind of load, and you don’t hand a patron the keys to the archive. The librarian retrieves what’s appropriate and keeps the stacks intact.
That is Vickee’s job between agents and sensitive systems. Publish curated extracts into Vickee and agents work against the copy. The source is never touched.
The seven parts of the diagram
Point at any part of the diagram.
Curated extracts flow in on a schedule. Scoped answers flow out. Approved writes go back under a human signature. Agents never reach past the middle column.
Data in
Extracts arrive from your systems of record through deterministic connectors, on a schedule you configure.
Answers out
Agents search by meaning, match exact terms, or ask a question and get an answer with cited sources.
Your systems of record
Agents never touch the system of record.
CMS and CDP, CRM and marketing automation, commerce, ERP and finance, analytics: these systems were built for controlled transactions. Autonomous software running open-ended queries is a different kind of load, and you don’t hand a patron the keys to the archive.
Scalability
Retrieval load hits Vickee. Your ERP never fields a thousand exploratory queries at 2 a.m.
Stability
SOR schemas change on their own release cycles. Vickee decouples agents from those changes, so an upstream migration doesn’t break every agent overnight.
Inbound · SOR to Vickee
Connectors are plain code.
The acquisitions desk. Deterministic connectors pull from source systems on your schedule and shape data on the way in, so everything arrives cataloged and shelved. No LLM in the sync path means no inference cost per run and no behavior that drifts with a model.
Deterministic
Plain code with transformation and validation built in: testable, versioned, and the same result every run.
Freshness, on your terms
Connector schedules are configurable, so the copy updates on your publication cadence. Replace a file in place and the index follows.
The card catalog
One source of truth, every agent.
Multi-agent systems drift when each agent carries its own context: every private copy is a fork of reality. The catalog is the shared map of what is known.
Update once
Change a document and every consumer sees it. No per-agent copies, no reconciliation, no version archaeology.
Shared map
The catalog shows every agent what is known, including what it never ingested itself.
Vickee · the librarian
Every library needs a librarian.
Publish curated extracts into Vickee and agents work against the copy. The source is never touched. The librarian retrieves what’s appropriate and keeps the stacks intact.
Governed
A read-optimized copy, scoped by tenant and namespace, indexed automatically and answerable.
Readable by both
A task-oriented agent guide for machines and a wiki-style admin console for people, over the same data.
Scoped retrieval
Agents get answers, never the keys.
Every request is bounded by the tenant and namespace it was published into.
Security
No source-system credentials in agent context windows, prompts, or logs. The SOR attack surface never grows with agent count.
Least privilege
Agents see only what was deliberately published into their tenant and namespace, scoped and tagged.
Auditability
What agents can reach is an explicit, reviewable publication decision, never a side effect of a service account’s permissions.
Your agents
Orchestrators, sub-agents, and people.
Every consumer reads from the same catalog: semantic search for meaning, hybrid search for exact terms, ask for a synthesized answer with cited sources.
People included
The wiki-style admin console reads the same shelf, with browse, search, and cited answers built in.
No drift
One platform serving orchestrators, sub-agents, and people from the same store, so no agent carries a private fork of reality.
Outbound · Vickee to SOR
Agents propose. A person approves. Code executes.
Circulation with a signature. When agent work should flow back to a source system, updates travel as deterministic code, and nothing ships without the librarian’s stamp.
Proposed, never written
Agents propose changes in Vickee. The SOR is never written to directly.
Gated
Human review and signoff before anything reaches a source system.
Repeatable
Approved updates execute as plain, repeatable code.
Why not just…?
One API in place of the database, the pipeline, and the glue code around them.
| Alternative | Where it falls short | Vickee’s answer |
|---|---|---|
| Object storage | Stores bytes, answers nothing. | Storage plus automatic indexing plus retrieval, one API. |
| Standalone vector DB | You still build ingestion, chunking, storage, and ops around it. | The pipeline is built in. Upload is the integration. |
| RAG framework code | Glue code your team owns forever. | A running service with an admin plane. Nothing for your team to maintain. |
| Wiki or drive | Organized for people, opaque to agents. | Readable by both: agent guide for machines, admin UI for people. |
| Direct SOR access | Credentials in agent context, unbounded load on production, one schema change breaks every agent. | A governed, read-optimized copy. Agents get answers and the keys stay with you. |
| Per-agent context | Each agent drifts toward its own private truth. | One centralized catalog every agent reads and trusts. |
| LLM-driven pipelines | Inference cost on every sync, behavior that drifts with the model. | Deterministic code connectors: testable, versioned, same result every run. |
Bring a real corpus.
Put it in Vickee, ask your agents a hard question, and judge the answers by the sources they cite.
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