Coding Classroom VS VATES.jp

What it is

Embeddable B2B AI infrastructure. Developers integrate conversational AI into their own product through an API, in the same role as Stripe or Twilio. It also runs as a self-serve SaaS — an embedded widget added with one line of JavaScript, or a standalone URL. Both share one foundation.

How it differs from RAG

Knowledge is stored as structured plain text and interpreted at query time. No vector database, no embedding step, no re-ranking.

Updates take effect the moment a line is written — no re-embedding, no re-indexing.
The knowledge base stays inspectable plain text, so its contents can be read and audited directly.
Editing or deleting a line removes it, verifiable by inspection.
No dedicated vector database, no GPU, no per-use embedding API.

Three roles

bard — turns natural language, images and PDFs into structured entries.
druid — management UI: full-scan search, log analysis, reference tracing.
vates — the conversational interface; replies in the reader's language.

MCP

A remote MCP server is included, so external AI agents can query a knowledge base directly — by OAuth as a custom connector, or with a bearer API key. One tool is exposed, vates_ask, and it is read-only: it cannot change settings, read conversation logs, or reach other instances. Access is revocable from the console.

Operations

Setup, instance settings, the knowledge base, billing and security are all operable from a phone. Hash-chained tamper-evident audit logs, passkeys, 2FA, per-customer IP allow/denylists, per-instance rate and consumption limits, encrypted daily backups, and an automated data-retention lifecycle.

Pricing

Deposit-based. No subscription, no monthly fee, no minimum spend. Usage is deducted from a prepaid balance; minimum top-up is USD 5.00.

Questions & Answers

As answered by people managing Coding Classroom and VATES.jp.

What makes your product unique?

VATES.jp's answer:

Knowledge is held as structured plain text and interpreted at the moment it is queried. There is no vector database, no embedding step, and no re-ranking. A line written into the knowledge base takes effect immediately, with no re-indexing, and the contents stay readable — you can see exactly what the system holds, and delete a line and verify it is gone.

Why should a person choose your product over its competitors?

VATES.jp's answer:

Fewer moving parts. No vector database to run, no GPU, no per-use embedding API — which lowers both cost and the number of things that can break. Nothing is hidden behind numeric vectors, so answers can be traced to the material behind them. Billing is a prepaid deposit with no subscription and no monthly fee, so it can be tried at small scale and grown without renegotiating a contract.

How would you describe the primary audience of your product?

VATES.jp's answer:

Two groups. Developers who want to embed conversational AI into their own product through an API, in the same role as Stripe or Twilio. And small to mid-sized businesses that want to run it out of the box — as a widget on their site or a standalone chat — without engineering work. Both are served by the same foundation.

Which are the primary technologies used for building your product?

VATES.jp's answer:

Python and FastAPI on the backend, React with Vite on the front end, SQLite for storage, on Ubuntu with Nginx. Payments through Stripe. Deployed on AWS EC2 behind Cloudflare. Notably absent: there is no vector database and no embedding pipeline in the stack.

What's the story behind your product?

VATES.jp's answer:

VATES is built by STANDOUT Inc. in Okayama, Japan, on a theory its developer worked out independently rather than assembling from existing frameworks. The premise is that meaning is not fixed in a symbol but arises when something is observed — so knowledge is kept as structure and interpreted at read time rather than compressed into coordinates in advance.

A concise formulation has been published as a preprint, IIIS: An Inter-Intelligence Intermediary Syntax for Shared Observation Across Perceptions (https://doi.org/10.5281/zenodo.21374493). The full theory is being prepared as a book.

The three working roles are named after the Celtic oral tradition: bard receives, druid keeps, vates speaks across.

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