RAG systems grounded in your data. Hybrid retrieval. Citation fidelity. Production evals.
Operonn builds retrieval-augmented generation systems for teams where a wrong citation is worse than no answer. Hybrid retrieval with reranking, temporal-aware freshness handling, citation grading, and evaluation harnesses that catch regressions before they reach users.
Production RAG requires more than a basic pipeline.
A vanilla RAG pipeline — chunk the docs, embed, top-k, prompt the model — can look good on hand-picked queries. In production, the challenges are different: confident hallucinations, deprecated citations, and corpus drift. Production RAG engineering requires careful attention to chunking strategy tuned to your data, hybrid retrieval combining dense and lexical signals, reranking on the top pool, freshness handling for dates and versions, and a citation-grading step that verifies the cited span genuinely supports the claim.
- Chunking strategy tuned to the actual structure of your documents.
- Hybrid retrieval — dense + lexical — with a reranker on the top pool.
- Temporal awareness: version-aware citations and freshness thresholds.
- Citation fidelity checks: every answer grounded in a retrievable span.
- Evaluation harness with regression sets, graders, and CI gates.
End-to-end RAG systems, not components.
We build the full stack — document ingestion and chunking, vector and keyword indexing, retrieval orchestration, reranking, generation, citation grading, evaluation, monitoring, and the interface layer that actually puts this in a user's hands. Integrations into your existing document sources (Confluence, SharePoint, Notion, Google Drive, S3, internal databases) are part of the scope, not a follow-up SOW.
- Ingestion connectors for Confluence, SharePoint, Notion, Drive, S3, Postgres, and custom sources.
- Embedding model selection, chunking, and indexing tuned for your domain.
- Retrieval orchestration with hybrid signals and reranking.
- Citation fidelity and confidence-gated generation.
- Eval harness with golden sets, judge models, and regression CI.
Where RAG earns its keep.
RAG is the right architecture when the answer must be grounded in a corpus that changes, when citations are non-negotiable, and when the questions are not predictable enough for a rules engine. Analyst copilots that summarise filings with sourced quotes. Internal knowledge assistants that self-heal from ticket resolutions. Customer-facing agents that answer policy questions against the current version of the policy — not last year's.
Evals first. Then retrieval. Then generation.
Most RAG projects that fail fail because the team shipped generation before they had a measurement loop. We flip that. The first week is spent building the evaluation harness — a regression set of 50 to 200 real queries with expected behaviour, an automated grader, and a hallucination check specific to the domain. Retrieval is then tuned against the golden set. Generation is the last thing to get touched. The judge model is pinned separately from the system model to prevent correlated drift.
- Week 1: build the eval harness and the regression golden set.
- Weeks 2–4: ingest, index, and tune retrieval against the eval set.
- Weeks 4–6: generation, citation grading, and confidence gating.
- Week 6+: production deployment with monitoring and trace review.
Chosen for the problem. Not for the vendor.
VECTOR & SEARCH
- Qdrant
- pgvector
- Elasticsearch
- OpenSearch
- Typesense
EMBEDDINGS & RERANKERS
- OpenAI
- Voyage
- Cohere
- BGE
- Domain-tuned rerankers
LLMS
- Claude
- GPT
- Open-source (Llama, Mistral, Qwen)
- Pinned judge models
ORCHESTRATION & EVAL
- LangChain
- Custom pipelines
- Ragas
- Promptfoo
- Internal eval harness
Common questions.
What is RAG and why is it different from fine-tuning?
Retrieval-augmented generation grounds an LLM's answers in your own data by retrieving relevant spans at query time and passing them into the prompt. Fine-tuning changes the model's weights — slower feedback loop, harder to update when your corpus changes. RAG is usually the right starting point for any system where the source of truth is a changing corpus.
Do you build RAG on Claude, GPT, or open-source models?
All three. The right choice depends on latency, cost, data residency, and reasoning depth. We pick per problem and make sure the architecture lets you swap models later.
How do you measure RAG quality?
Every RAG engagement ships with a regression set of real queries, an automated grader using either model-as-judge or deterministic checks, and a domain-specific hallucination check. CI blocks deploys if quality drops below the agreed threshold.
Can you work on an existing RAG system that has regressed?
Yes. We audit the retrieval layer, the chunking, the grader, and the generation prompt, identify where the regressions are coming from, and scope the fix. We've turned around several RAG systems that ran well on day one and drifted into unusable by month six.
How long does a first RAG engagement take?
Most first RAG builds ship in 6–10 weeks to production, with the eval harness live by end of week 1 and a working end-to-end system by week 4.
Describe your workflow. We'll tell you if we can move the number.
Hi thereWhat can we help you with?
Have a RAG problem we can take apart?
Send the corpus, the query pattern, and the current pain. We'll reply with scope.
hello@operonn.com →