→ RAG implementation services
RAG implementation services for production retrieval-augmented generation
Hybrid search (BM25 + dense), reranking, eval-driven chunking, pgvector or Qdrant depending on scale. We build the retrieval layer with the same rigor you build the rest of your backend.
Overview
How we approach this work.
RAG implementation services from Resser cover the full retrieval stack: chunking, embedding, hybrid search, reranking, prompt assembly, citation enforcement, and eval.
We have shipped RAG over patient documents, technical manuals, contracts, sales playbooks, regulatory filings, and internal Confluence / SharePoint stores. Each build had the same hard parts: getting the chunks right, balancing recall and precision, and making sure the model cites instead of hallucinates.
Most RAG agencies sell embedding + vector DB + prompt as if that is the system. The real system is the eval suite that catches retrieval regressions, the reranker that fixes recall errors, and the citation gate that refuses to answer when no source meets the threshold.
What we build
Concrete deliverables.
Document Q&A systems
Ask questions across documents and get cited answers. PDFs, Word, HTML, Confluence, Notion, SharePoint, S3.
Knowledge-base agents with citation enforcement
Every answer cites the source paragraph; if no source meets the threshold, the agent escalates rather than guesses.
Hybrid retrieval at scale
BM25 + dense vectors + reranking. Filters by metadata, tenant, language, recency.
Eval-driven RAG
Real eval set of (question, expected source, expected answer) tuples that runs on every change.
Multi-tenant RAG
Per-tenant indexes, per-tenant access control, per-tenant cost telemetry.
Private / on-prem RAG
Self-hosted embeddings, self-hosted vector DB, self-hosted LLM. No data leaves your perimeter.
Stack
What we build with.
Vector stores
pgvector on Postgres for 95% of cases. Qdrant for heavy filtering. Pinecone or Turbopuffer for scale. Weaviate or Chroma when the customer is already there.
Embeddings
Voyage AI (voyage-3, voyage-3-large), Cohere embed-v3, OpenAI text-embedding-3, BAAI bge-m3, JinaAI embeddings. Self-hosted via Infinity for private deployments.
Retrieval and reranking
BM25 via Postgres or Elasticsearch / OpenSearch. Cohere Rerank. ColBERT-v2 when latency permits. Cross-encoder rerankers on-prem.
Eval
Custom eval harnesses with question / source / answer tuples. Ragas for automated metrics. Braintrust or LangSmith for tracking over time.
Outcomes
What we ship.
Industrial SaaS knowledge agent: minutes-long search reduced to seconds; high citation accuracy and clean refusal on out-of-scope queries.
Healthcare RAG over the diagnostic codebook: meaningful accuracy lift over the previous prompt-only classifier.
Compliance review RAG over policy documents: full citation enforcement; the agent refuses to answer when no source meets the threshold.
References with names available after a scoping call.
Related services
Other places this work shows up.
FAQ
Frequently asked.
When do we use RAG vs fine-tuning?
RAG when the knowledge is dynamic, too big to fit in a prompt, or needs citation. Fine-tuning when prompting + RAG have been exhausted and you need to teach the model a behavior. Fine-tuning carries a maintenance tax most teams underestimate. We default to RAG.
pgvector vs Pinecone vs Qdrant?
pgvector for 95% of cases because the same Postgres your engineers already operate covers it. Qdrant when filtering and metadata queries get heavy. Pinecone or Turbopuffer when scale or hosted ops are the constraint.
Why do you reject pure semantic search?
Users phrase queries the way they phrase them, not the way embeddings prefer. Hybrid retrieval (BM25 + dense) beats pure semantic in production for almost every B2B use case we have shipped. Reranking adds 30-100ms and pays for itself 10x in answer quality.
Can the RAG run entirely on-prem?
Yes. Self-hosted embeddings (BAAI bge, Infinity), self-hosted vector DB (pgvector or Qdrant), self-hosted LLM (Llama on vLLM). No data leaves the perimeter. See private AI infrastructure for the full deployment story.
How much does a RAG implementation cost?
Single-tenant RAG over a defined corpus: €15,000-€40,000. Multi-tenant RAG with admin tooling: €40,000-€100,000. On-prem RAG with full eval and observability: €60,000-€150,000+.
Want to scope this for your project?
Fill the project-estimate form. We reply within one business day with a preliminary scope and a rough budget bracket.