Bluey Synapse/Platform/Your Base (RAG)
Layer 02 · Knowledge

Your AI knows your business — not the entire internet

Upload PDFs, manuals, contracts, and spreadsheets. Synapse organizes, indexes, and answers based on your real documents, always citing the source. The proprietary rerank makes sure the right answer ends up on top — not the one that merely looks most like the question.

The precision gain
3–5× more precision than a plain RAG, with Bluey's proprietary rerank.
Semantic search finds what looks like the question. The rerank decides what actually answers it. It's the difference between citing the right paragraph and citing a similar one.
Source citation Human approval Continuous updates No hallucination
Pipeline

From loose file to sourced answer

Four steps, with a person in control of the second. Nothing enters the base without someone approving it.

01
Your documents

PDF, DOCX, XLSX, CSV, document images. Drag them onto the screen or connect the folder and the system you already use.

02
Automatic organization

Splitting into chunks, metadata extraction, and indexing. Before anything counts toward answers, a person approves what came in.

03
The right answer on top

Semantic search retrieves candidates; the proprietary rerank reorders them by what actually answers the question.

04
Source citation

Every answer comes with document and page. If the source doesn't support the answer, the application says it doesn't know.

A query, from the inside

Pick a question and see where the answer comes from

The retrieved chunks appear with their search score and their score after the rerank. That reordering is what changes the result.

In the technical demo we do this with your documents, not with examples.
What supports the base

One base shared by every application in the company

Dedicated Lakehouse

Each company has its own, isolated. Documents, spreadsheets, databases, and systems feed the same base — so every application understands the business the same way.

Proprietary rerank

The second search layer reorders candidates by real relevance to the question. It's where the 3–5× precision gain over a plain RAG comes from.

A preference for silence

When no chunk supports the answer, the application says it didn't find one and opens a path to a person. We prefer that over a well-written wrong answer.

Continuous updates

A new price table, a revised policy, a signed contract: it enters the base and starts counting in the answers that follow, with the old version preserved in the history.

Permission at the chunk level

Whoever can't see the document doesn't get an answer based on it. Permission applies at the source, not just on screen.

Your data trains no one

Nothing that enters your base is used to train third-party models. For sensitive content, the query can stay restricted to your environment.

Continue through the stack

The base decides what your AI knows. The router decides which brain it thinks with.

Multi-LLM → HitL Governance →

Bring ten documents. We build the base right in front of you.

In the technical demo we upload your files, ask the questions your team asks every day, and show the citation behind every answer.

Book a technical demo Back to the platform