RAG and private knowledge assistants
Retrieval-augmented generation over your documents and systems so answers cite your sources instead of inventing them.
RAG is the pattern teams ask for when answers must come from their own PDFs, policies, product manuals, or ticket history—not from a model’s training memory alone.
Trevyk designs ingestion, chunking, embeddings, retrieval filters, and answer prompts so the model must show which passage it used. Role-aware access matters: different roles should not see the same corpus.
A useful first project is narrow. Wide “chat with the whole company drive” projects fail when permissions and freshness are ignored. Training or fine-tuning on private corpora is a separate, explicit decision—not a default.
Ingestion, chunking, and embeddings
We inventory sources (drives, CMS, ticketing exports, manuals), clean formats, and attach metadata such as department, language, and sensitivity.
Chunking follows document structure where possible so retrieval returns coherent passages. Embeddings index those chunks for semantic search; exact filters (product line, date, role) sit beside vectors so retrieval stays precise.
Reference architectures from cloud providers describe event-driven refresh so new or updated documents enter the store without waiting for a monthly rebuild. Freshness is a product requirement, not an afterthought.
Retrieval, grounded answers, and citations
At query time the system retrieves candidate passages, optionally reranks them, and asks the model to answer only from that context. If context is insufficient, the assistant should say so or escalate—not guess.
Agentic RAG patterns (query rewriting, multi-hop search, sufficient-context checks) help on complex questions; we use them when a single retrieval pass is not enough, not as decoration.
Visible citations—document title, section, or link—let a reviewer verify the answer. That is how private knowledge assistants earn trust inside a company.
Permissions, isolation, and failure handling
Document ACLs must travel with retrieval. A finance clerk and an admissions officer should never share an unfiltered index.
Data residency and provider settings are chosen with you. Secrets and raw uploads stay out of client-side code.
When retrieval returns nothing, the UX refuses cleanly, offers a human path, and logs the miss so you can improve the corpus.
Evaluation, deployment, and deliverables
We build a golden question set from real staff queries: accuracy, citation presence, refusal correctness, latency, and cost per answer.
Deployment may be a secured web assistant, an internal chat surface, or a tool behind an agent. Integrations stay on approved APIs.
You leave with: indexed corpus design, retrieval pipeline notes, assistant UI or API, eval report, refresh runbook, and source for custom components.
What you leave with
- Corpus selection and permission model
- Chunking, retrieval, and optional reranking checks
- Cited answers and refusal when context is thin
- Refresh path when documents change
Questions buyers ask
What does RAG mean here?
Retrieval-Augmented Generation: the system searches your approved documents first, then asks the model to answer using those passages, reducing reliance on the model’s training memory alone.
Will this train on our private data?
We design for retrieval and API-based generation with your configured providers. Training or fine-tuning on private corpora is a separate, explicit decision—not a default.
How is this different from a public chatbot?
Answers are constrained to your corpus and roles. Without retrieval hits, the system should refuse or escalate instead of inventing policy.
What drives cost?
Corpus size and refresh frequency, embedding and inference usage, languages, integrations, and how deep evaluation and monitoring need to be. We estimate from your scope rather than quoting a fixed brochure price.