For builders

Stop assembling a back-end. Ship the product.

The vector DB, the search layer, the auth, the isolation, the RAG — that’s the six-month nightmare hiding behind the weekend demo. Vectros is the one back-end you don’t build, and don’t operate.

Store it, search it, ground a model on it, isolated per customer — in minutes.

Self-serve and a free tier are on the way as we open access. Right now it’s invite-only — request access and we’ll get you in.

The plumbing you were about to write

You wanted to ship a thing people use. Instead you’re three weekends into wiring:

  • a database, then a separate vector index beside it
  • the embedding job that keeps the two in sync — and the cron that re-runs it
  • auth and scoped keys, hand-rolled
  • per-tenant isolation — a WHERE owner_id = ? on every query, and the cold sweat that you forgot one
  • a RAG pipeline stitched out of four libraries
  • and then you own and operate all of it, forever, while the actual product waits.

The back-end is the easy demo and the long nightmare. You don’t have to build it. You don’t even have to run it.

Declare your data model once. You get typed records, document ingest, and hybrid search over a unified records-and-documents index — one thing to reason about. No index to babysit, no sync glue to maintain, no embedding cron to nurse. Your source of truth is the thing that’s searchable.

What you actually get on day one

Concrete things you can run, not adjectives.

One model, not four systems

Define your record schemas — fields, validation, references, what’s searchable — and write records against them. Ingest documents inline or by upload, drop them in folders, retrieve text and download URLs. Every write to an audited type is versioned, with optimistic concurrency so two writers don’t silently clobber each other. Search spans records and documents in one call — keyword, semantic, or hybrid.

Grounded RAG, out of the box

Ask a question over your own data and get an answer with citations back to the source — streamed. Ask against a single document, or across the whole indexed corpus. It ships as the product, not as a retrieval framework you assemble and then maintain forever. In-perimeter inference is the default path: on the partner data plane, sensitive content stays inside the Vectros perimeter instead of crossing to a third-party model host.

Agent memory that’s safe to point at customers’ data

Vectros ships an official MCP server — 21 data-plane tools, one line in your agent config (Claude Desktop, Cursor, Cline, Continue, VS Code, hosted platforms). Your agent searches the corpus, reads and writes records, ingests documents, and asks grounded questions — no custom integration code. Unlike a local SQLite or pgvector memory hack, this is multi-tenant, isolated, scoped-key, auditable, and hosted. And it has no web-search or web-fetch tools — by design. The agent reaches your tenant’s data and nothing else.

Per-customer isolation without the forgotten WHERE clause

Every data-plane resource is partitioned by an auth-derived context that fails closed. Lookups can’t cross a context boundary, search can’t return another tenant’s content no matter what filter the query carries, and a probe with someone else’s id gets the same “not found” as a probe with an invented one. This isn’t row-level rules you hand-author and pray you got right on every query. It’s a structural property of the platform — the same on every tier.

Pay for what you turn on

No surprise invoice. No idle burn. No enterprise pricing to start. The cost story here is architecture, not a discount:

  • Turn a capability on, and you pay for it. Leave it off, and it costs nothing. Turn off embedding on a record type and there’s no vector cost on that type at all — no index sitting there billing you to do nothing.
  • An isolated per-customer index means you don’t pay for other tenants’ scale. Your cost tracks your work, not the size of the platform.
  • Cost follows the work you do, not a flat floor you pay for the privilege of starting.

Pricing isn’t buried in marketing copy where it goes stale — see the pricing page for the tier shape and credit model.

The part you don’t throw away later

Start lean. Scale to compliance-grade without re-platforming.

The trap every cheap tool sets

It’s great until you land the customer who needs isolation, audit, or a BAA — and then the price leaps to an “enterprise” tier, or the auth/DB you picked was never eligible for that customer in the first place, so you rip it out and re-platform mid-flight, usually with that first real customer watching.

Why there’s no wall here

Per-customer isolation is on from day one. When a customer needs audit history, you turn it on — every write to an audited type already accrues an immutable, tamper-evident version record. When a customer needs sensitive-field handling, you turn that on too: values destroyed before they ever hit history, masked on read unless a token is scoped to reveal them, and kept out of the search index entirely. Same platform, no rewrite, no escape tax.

The platform was extracted from a HIPAA-grade clinical product and hardened through extensive adversarial security review. The compliance story isn’t the pitch here — it’s the proof the ceiling is gone.

Building on regulated data and want the full controls-first story? See the compliance-grade track. The legal specifics — BAA, HIPAA applicability, attestation status — are available under NDA.

Honest answers

The things you’re already thinking

Two things you can run this week

Pick a blueprint, bootstrap it in one command.

Drive it from an agent with zero application code.

Second Brain

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

A personal knowledge base: capture notes, ideas, and links as typed records, then ask them anything with grounded, cited answers. The widest on-ramp — semantic recall plus structured facts, no app to write.

vectros bootstrap --blueprint second-brain

Coding-Agent Project Knowledge

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

A persistent, searchable knowledge store your coding agent reads and writes over MCP. The decisions you made, the conventions you set, the gotcha that bit you last Tuesday — it stops starting from zero every session.

vectros bootstrap --blueprint coding-agent-memory
Both run on a least-privilege scoped key the bootstrap mints for you — data-plane only, no root key, no manual portal juggling. A blueprint is one reviewable file: schemas, access profile, a service principal, optional seed data. Apply it twice and it converges instead of duplicating. Want more shapes? See what you can build — six use cases on one back-end.

The honest caveats

Right where you can see them.

We’d rather you find these here than in a surprise later.

Bootstrapping needs a human step.

Applying a blueprint requires a bridge token from the developer portal — there’s a real sign-in; there’s no fully unattended path that mints one for you.

The agent surface has no web tools — on purpose.

No web-search, no scraping, no third-party fetch. The MCP server reaches your Vectros data plane and nothing else.

Agent document upload is local-file on the desktop transport.

Over HTTP, ingest text inline (or call the SDK from your own code).

Audit history is tamper-evident, not tamper-proof.

The SHA-256 state-continuity chain makes out-of-band alteration detectable; the platform doesn’t re-verify the chain for you on every read.

It’s an invite-only 0.x preview.

Some things are reserved and named as such in the docs. We draw the line ourselves rather than round up.

Ready to stop plumbing?

Invite-only today. Self-serve and a free tier are on the way as we open access.