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Deploris

Retrieval-augmented AI grounded in your own knowledge.

A production RAG pipeline over your knowledge chunking, embedding, vector search, prompt orchestration, retrieval evaluation, and access-controlled answers with source citations.

Retrieval-augmented generation systems that ground LLM answers in your own documents, with access control, citations, and evaluation.

user askedWhat is our refund policy?▎corpus · 24 chunksretrieved 3123answerstreamingRefundsaregrantedwithin30 days1ofinvoice,perpolicy2section24.22andcontract3clause37.sources:[1] chunk 03[2] chunk 11[3] chunk 18

What it is

A production RAG pipeline over your knowledge chunking, embedding, vector search, prompt orchestration, retrieval evaluation, and access-controlled answers with source citations.

Who it's for

Companies whose employees or customers keep asking the same knowledge-heavy questions, and who need answers that reflect the current state of documented truth.

Outcomes

  1. 1

    Knowledge audit

    What sources, how big, how sensitive, how often they change.

  2. 2

    Prototype

    End-to-end RAG on a scoped corpus, evaluated against real questions.

  3. 3

    Productionization

    Access control, monitoring, cost controls, retraining cadence.

  4. 4

    Continuous eval

    Weekly eval on a growing question set precision, recall, hallucination rate.

Pilot

$20–50k

Scoped RAG on one corpus, evaluated on real questions.

Production

$60–160k

End-to-end pipeline with access control and monitoring.

Continuous

$5–15k / mo

Ongoing eval + retraining + optimization.

Bands, not quotes every engagement is priced in writing after discovery.

Common questions

What is a RAG system?
A RAG (retrieval-augmented generation) system grounds a large-language-model’s answers in your own documents. It retrieves the most relevant passages from your knowledge, then uses the LLM to answer using those passages and cites them so a human can verify.
How is RAG different from a fine-tuned model?
Fine-tuning changes the model’s weights. RAG leaves the model as-is and instead feeds it your current documents at query time. RAG is faster to update, easier to audit, and safer for regulated content because you can prove which source drove which answer.
Does RAG expose our internal data to the model provider?
Only the retrieved passages that answer a specific question are sent to the model. With enterprise API tiers those passages are not used for training. If exposure must be zero, we can run the pipeline against an on-prem or EU-hosted model.
How is prompt injection mitigated?
Tagged retrieval boundaries so retrieved content cannot override system instructions, system reminders at every turn, and tool-use allow-lists. Logging makes every attempt reviewable after the fact.
How do you measure RAG quality?
A labelled eval set that grows as real questions come in; retrieval precision, answer faithfulness (grounded in retrieved content), and refusal rate on out-of-scope questions. Weekly report during active engagements.
Which document sources can a RAG system ingest?
Confluence, Notion, SharePoint, Google Drive, Dropbox, S3 buckets, Postgres and MySQL databases, Zendesk and Intercom knowledge bases, GitHub wikis, PDF archives, and transcripts from Fireflies, Grain, or Otter. Every source respects its native permission model.
How often does the index refresh?
For actively edited sources (Notion, Confluence, Google Drive) we poll webhooks or change feeds and reindex within minutes. For slower-moving archives a nightly batch is enough. The refresh cadence is a configurable per-source policy.
What does a RAG system cost to run?
Running cost scales with document volume and query load. For a knowledge base of a few hundred thousand pages served at modest query volume, infrastructure plus model inference typically lands in the low four figures per month. We publish the full cost model before you sign.
Which models do you support?
Claude (Opus, Sonnet), GPT-4-class models, Gemini, open-weights models via Together, Fireworks, Groq, and self-hosted deployments on vLLM or SGLang. The orchestration layer is model-agnostic so you can switch without rewriting the application.

Use-case pillar

RAG knowledge base for customer support teams. →

A retrieval-augmented generation system grounded in your current product docs, release notes, ticket history, and internal runbooks. Deploris builds it, ships it in 4 to 6 weeks, and runs the infrastructure it answers from.

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