Engineering & Data

Wire RAG into your product
instead of building it.

Ingestion, chunking, indexing, reranking and evaluation are things you keep maintaining once you build them. Schift takes that part and leaves you the surface to wire in through API and SDK.

Talk to us about a technical review →

WHY SCHIFT

The part you no longer have to operate

One path from ingestion
to retrieval

Upload a file and extraction, chunking and indexing finish on their own, leaving a search API. No pipeline to hold together yourself.

Originals are kept

The source text is stored alongside the chunks. Grounding can be traced to the paragraph, and changing indexing strategy does not mean re-ingesting everything.

MCP connects directly

Use company memory from tools like Claude and Cursor as they are. No separate client to build.

Runs leave a history

Which grounding attached to which query is recorded, so when quality drops you can see where it diverged.

WHAT YOU GET

The surfaces you wire into

Python and TypeScript SDKs plus a REST API
MCP server (Claude, Cursor, IDEs)
Endpoints for search, re-search and grounded results
Ingestion status and failure reasons
Dedicated deployment and on-premises installation

Frequently asked

Can we keep the vector database we already use?

Schift runs its own search engine. If an existing index has to move and you still have the source documents, re-ingestion moves it. Which way is cheaper depends on the size of your corpus.

Can we choose the models?

The allowed model list is set per organization. Bringing your own keys is possible depending on contract scope.

Is the API the same on-premises?

It is the same API surface. The installation bundle and supported-version matrix are managed separately.

Wire it in rather than build it.

Send us a few real files and a few real queries, and we will show you retrieval quality on those first.