Your knowledge.
Queried in plain English.

Three differences that matter. First — context windows are finite, so you can't feed an entire knowledge base into a single prompt. RAG retrieves only the relevant chunks for each query. Second, RAG keeps your data in your infrastructure (we retrieve, then send only the matched chunks to the model API). Third, RAG produces citations — you can verify every answer against its source documents, which "paste it into ChatGPT" cannot do. For one document, ChatGPT is fine. For a real knowledge base, RAG is the only viable pattern.

PDFs (including scanned with OCR), Word, PowerPoint, Excel, Markdown, HTML, plain text, Google Workspace docs, Notion pages, Confluence, SharePoint, code repositories, customer support tickets, Slack channels (with retention policies), and structured data with descriptions. For more unusual formats, we extract during the data audit phase and confirm before scoping the build.

Honest answer: well-built RAG dramatically reduces hallucinations but doesn't eliminate them. Three controls we build in: (1) the model is instructed to cite sources and abstain if no relevant content was retrieved; (2) we run an eval suite that scores every change against a ground-truth Q&A set you approve; (3) confidence thresholds trigger human escalation below a defined floor. For accuracy-critical use cases (legal, medical, financial), we recommend human review on outputs regardless. Studios builds the system; you set the risk tolerance.

Yes. We build refresh pipelines that re-index changed documents on a schedule (typically nightly) or in real time via webhooks where the source system supports it (Google Drive, Notion, Confluence). Stale content is the most common cause of RAG quality decay, so we treat refresh as a core engineering concern, not an afterthought. Your admin panel shows what's indexed, when it was last refreshed, and any indexing failures.

Permission-aware retrieval is built in for any engagement where document access matters. We index document-level ACLs from your source systems (Google Drive sharing, Notion permissions, SharePoint groups) and filter retrieval at query time by the authenticated user's permissions. The AI never sees content the user shouldn't see. For enterprise SSO and complex permission models, this is part of Studios Growth and Enterprise scoping.

Your documents are stored in your infrastructure or in a managed vector DB you control. At query time, only the retrieved chunks relevant to the user's question are sent to the model API for synthesis — never the entire knowledge base. We select model providers whose enterprise terms guarantee no training on API inputs (Anthropic, OpenAI Enterprise, others). For maximum data sovereignty, we can deploy entirely on open-source models running on your infrastructure. Details in our Privacy Policy.

Depends on volume, latency requirements, and what your team can operate. Pinecone is the easiest managed option for under 10M vectors. pgvector (Postgres extension) is the best choice if you already run Postgres and want one fewer system. Weaviate and Qdrant fit teams that want self-hosted with rich filtering. We recommend during discovery based on your specific situation — we don't have a kickback relationship with any of these.

Three components: (1) vector DB hosting — $50 to $2,000/month depending on volume and tier; (2) embedding regeneration — usually under $200/month for steady-state operation, more during initial indexing; (3) generation API costs — varies wildly by query volume and model choice, typically $200 to $5,000/month for SMB and mid-market deployments. We estimate all three during scoping and build cost dashboards so you can monitor in real time. Optional Studios retainer ($5K-$25K/month) covers ongoing optimization and feature additions.