Everything you build, one back-end underneath.

Second Brain — your personal knowledge base

Dump every note, idea, and link in one place — then just ask it.

Capture notes, ideas, bookmarks, and half-formed thoughts as typed records, then retrieve them by meaning instead of by remembering the exact words you used. Hybrid search spans keyword and semantic recall over a unified index, so “that article about pricing psychology” finds the note even when “psychology” never appears in it.

What it exercises

• —Semantic + hybrid search — over your own content — keyword, semantic, or both in one call.
• —Typed records — with lookups, so a note has structure (title, body, tags, source link), not just a blob.
• —Grounded answers — ask a question and get a response cited back to the notes it came from.

Run it

vectros bootstrap --blueprint second-brain

Pick the second-brain blueprint, bootstrap a narrow key, then drive it from an MCP agent (“save this, summarize what I’ve captured about X”) or straight from the data-plane UI. Zero application code to start.

Coding-Agent Project Knowledge — context that survives the session

Your coding agent remembers decisions, conventions, and gotchas across sessions — no app code.

Most coding agents start every session from zero. Give yours a durable, searchable place to keep what it learns about a project — the architectural decisions, the naming conventions, the “don’t touch this, it’s load-bearing” gotchas — and let it recall the right note at the right moment instead of relearning it every time.

What it exercises

• —Facts and entities — schema’d records for decisions, conventions, and components, with lookups and references between them.
• —Episodic history — every write is versioned on an audited model, so you can see how a decision evolved over time.
• —Semantic recall — the agent retrieves by meaning, surfacing the relevant prior gotcha even when the wording differs.
• —MCP-native — the agent reads and writes its own knowledge over the Model Context Protocol, on a scoped key that grants exactly the data-plane access you minted and no more.

Run it

vectros bootstrap --blueprint coding-agent-memory

Bootstrap the coding-agent-memory blueprint, merge the minted key into your MCP client config, and point your agent at it. This is agent memory that’s safe to point at a real project — isolated, scoped, auditable, hosted — not a single-user local file on your laptop.

Chat with your docs — answers grounded in your own files

Upload your documents, ask in plain language, get answers that cite the source.

Ingest documents — inline or by file upload — organize them in folders, and ask questions across the whole set. Retrieval grounds the model on your own content, and every answer streams back with citations to the documents it drew from, so you can check the source instead of trusting a confident guess.

What it exercises

• —Document ingest — text inline or file upload, organized in folders, with retrievable text and download URLs.
• —Document/RAG retrieval — one search call spans documents and records together; no separate vector index to wire up beside your store.
• —Citation-grounded answers — RAG over your data and document-scoped Q&A both stream with citations back to the source; a stateless chat mode is there for when you bring your own context.

Run it

This is an illustrative shape, not a bundled blueprint yet — but you can build it today on the same primitives. Define a document-bearing schema, scaffold a blueprint from the bundled exemplars (vectros blueprint init my-docs --from task-management), bootstrap a key, and drive ingest and Q&A from an MCP agent or the data-plane UI.

Support & internal-wiki Q&A — one place your team and your customers can ask

Turn your help center and your internal wiki into something people can just ask.

Combine ingested documents (help articles, runbooks, policy pages) with typed records (products, plans, known issues) and let an assistant answer over both — grounded, cited, and isolated per customer when you run it for the customers you serve.

What it exercises

• —Document/RAG over your knowledge base — articles and runbooks, retrieved by meaning.
• —Facts and entities alongside the docs — structured records for products, statuses, and entitlements, searchable in the same call as the prose.
• —Per-customer isolation — when you run this as a product for your own customers, each customer’s context is a mandatory, fail-closed partition. Not a forgotten WHERE clause away from a cross-tenant leak.

Run it

Illustrative today. Model your articles as documents and your structured facts as records in a blueprint, bootstrap a scoped key, and drive question-answering from an MCP agent or the data-plane UI. When a customer later asks “is my data isolated?”, the answer is already built in — the same platform, no rewrite.

A small vertical SaaS — listings, search, and a helpful agent

Recruiting, tutoring, real-estate search — structured listings your users can search by meaning.

A worked example: a real-estate listing search (the data is synthetic). Model listings as typed records — price, beds, neighborhood, a free-text description — with lookups for exact and range filters and hybrid search over the description. A buyer asks “a quiet two-bed near good schools under $600k,” and the agent narrows by the structured fields and ranks the rest by meaning.

What it exercises

• —Facts and entities — typed listing records with validated fields and lookupFields for exact, prefix, and range filters (price ranges, bed counts).
• —Hybrid search — keyword + semantic over the free-text description, narrowed by the structured filters in the same query.
• —Shared / multi-agent memory — context isolation plus scoped access, so the search agent and a follow-up agent share state without either one reaching outside its lane.

Run it

Illustrative — recruiting, tutoring, and listing search are all the same shape. Author a blueprint with your listing schema and lookups (vectros blueprint init listings), validate and plan it offline, bootstrap a key, then let an agent or your forked data-plane UI run the searches. All sample data synthetic.

Clinical Intake — structured intake with similar-case support

Structured intake that validates on the way in and surfaces the most similar prior cases by meaning.

The compliance peak of the gallery — and where the same platform that started lean shows what it grows into. Model an intake form as a validated record type, capture submissions (synthetic only), and use hybrid search to surface the most similar prior cases by meaning, as support for a clinician’s triage — never as automated decisioning.

This is triage support, not decisioning. It surfaces and structures information for a human; it does not diagnose, prioritize, or decide.

What it exercises

• —Facts and entities with validation — the intake schema validates on the way in (required fields, formats, ranges), so structure is enforced at write time.
• —Sensitive-field handling — fields you mark sensitive are destroyed before they reach retained history, masked on read unless a token is explicitly scoped to reveal them, and kept out of the search index entirely — three distinct, independent mechanisms.
• —Tamper-evident audit and version history — every write to the audited intake model accrues an immutable version record of what changed and who changed it, on a SHA-256 state-continuity chain that makes out-of-band alteration detectable.
• —Per-customer fail-closed isolation — each context is a mandatory partition derived from the credential, never a wildcard.

Run it

vectros bootstrap --blueprint clinical-intake

Bootstrap the clinical-intake blueprint, then drive intake and similar-case lookups from an MCP agent or the data-plane UI. All example data is synthetic. The platform is a HIPAA-grade substrate — it does not, on its own, make your application HIPAA-grade.