Vendor Catalog

Divinci is a multi-vendor platform. Almost every step of the pipeline — embedding a chunk, storing a vector, parsing a PDF, searching the web, speaking a reply, fine-tuning a model, stripping PII — is a swappable tool, and most steps have several vendors behind them.

There are 56 such tools today, excluding the ~160 conversational models (those have their own live Model Catalog).

Why you would care

If you…	Look at
already pay a vendor and want to use that account	the BYOK column — attach your key and the tool becomes selectable
want to start with nothing	the Divinci-managed rows — usable with no key of your own
are choosing between two options	the Divinci usage column: what we actually run in production

The categories

RAG & Vector Stores Where chunks live and how they are retrieved — 14 stores, from Cloudflare Vectorize to Neo4j.

Embeddings The models that turn chunks into vectors, and why mixing them breaks search.

Parsing & Chunking Turning a PDF, a page or a record into chunks worth embedding.

Search & Scraping Web search providers and the scraping cascade.

Voice & Audio Text-to-speech, transcription, diarization, voice cloning and voiceprints.

Fine-tuning Where a fine-tune actually trains — Vertex, OpenAI, Cloudflare LoRA, Modal, Tinker, AWS.

De-identification Presidio and Stanford NER, and the one that fails closed.

Red-teaming & Evals First-party, and deliberately so — TrustBench probes, scorers and attested runs.

How vendor selection works

Every tool has an auth provider. A tool is selectable when credentials for its provider are available — either Divinci's (for the runtimes we pay for) or yours, attached as a Provider Key (BYOK).

Credentials are per workspace, and validated on save. Each provider declares a JSON-schema credential shape and a live validation call, so a bad key is rejected at the point you paste it rather than at the point a customer asks a question.

A vector's tool is fixed at creation

Vector store and embedding model are properties of the vector, not of the query. Re-pointing an existing vector at a different embedding model does not re-embed anything — and vectors from two models are not comparable even at the same dimensionality, so similarity search degrades quietly rather than failing. Create a new vector and re-ingest.

About the numbers on these pages

The Divinci usage column is generated, not asserted: a weekly job asks the production API for aggregate counts and commits them into these pages. It carries counts only — no workspace, release, customer or corpus is identified, and the endpoint that produces it is a $group-and-$sum with nothing else in the projection.

Two honest caveats about reading it:

• Zero does not mean bad. Several vendors are supported because a specific customer asked for them; the rest of the platform has no reason to use them.
• A count is a count of configurations, not of traffic. A vector store used by one enormous corpus and one used by two hundred small ones look similar here.

The first weekly refresh has not run yet, so usage columns read —.