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Salvus AI
AI & Implementation

Search and reason across private documents without exposing them.

Retrieval-augmented generation built for businesses that cannot put proprietary data on shared SaaS. Your documents stay in your environment. Your team gets a search-and-answer experience that feels like consumer AI but holds up under audit.

Scope this serviceGet a written quote

Engagement details

Quote
In writing
Duration
4 to 10 weeks
Citations
Every answer cited
Audit
Log on every query
Compliance
HIPAA add-on available
Ownership
Code, data, and index all yours
How this engagement runs

Three steps from first call to system you own.

  1. 01

    Scoping

    We pick where this moves a number.

    Working session, written read-out, 1 to 3 places this service actually moves the needle for your business.

  2. 02

    Build

    We ship on your infrastructure.

    Code lands on your server, wired into the tools your team already uses. You see progress every week.

  3. 03

    Tune

    90 days of weekly edits.

    Weekly tuning calls, accuracy reporting, and a single weekly number that says working or broken.

Inside the engagement

What you actually receive.

  • 01Vector DB

    Private vector store

    Your documents indexed in a database you control. Pinecone, pgvector, or Chroma depending on the volume and the compliance bar.

  • 02Citations

    Every answer cited

    The system shows the document and the passage the answer came from. No hallucinated facts, no untraceable claims.

  • 03Audit

    Audit log on every query

    Who asked what, when, and which documents the system reached. Compliance teams get what they need.

  • 04Compliance

    HIPAA-ready

    Add the HIPAA layer and we deploy on infrastructure that supports BAAs. For dental, medical, legal, and regulated buyers.

Method

Predictable phases. Honest checkpoints.

  1. 01
    Phase 01

    Scoping

    Map the document corpus, the user groups, the compliance bar, and the questions the system needs to answer.

  2. 02
    Phase 02

    Ingest and index

    Document parsing, chunking, embedding, and indexing into the vector store. Done on your infrastructure, not ours.

  3. 03
    Phase 03

    Query layer

    Build the chat or search interface, wire up role-based access, and run accuracy testing on a held-out question set.

  4. 04
    Phase 04

    Production and tune

    Roll out with monitoring on retrieval quality, answer accuracy, and citation correctness. Tune monthly.

Next step

Ready to scope secure rag systems?

Thirty minutes on the phone, and you will leave with a clear answer on whether this fits. Not a soft proposal, a yes or no.

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