Answers withreceipts.
A RAG system is only as good as what it retrieves. We engineer the retrieval, the evaluation, and the permissions, so every answer is grounded in your documents and provably so.
Watch an answerassemble.
A question goes in. The answer composes one claim at a time, and every claim arrives holding the passage it stands on. A claim with no source does not ship.
An illustrative assembly of the citation pattern we ship. The documents change; the receipts do not.
Source, section, passage. We trace each claim back to the document and paragraph it came from. No black boxes.
Two readers,two answers.
Permissions are not a polite layer on top of the answer. They decide what the retriever is allowed to see before the similarity search runs, so the same question over the same index produces a different answer for each reader.
RBAC, ABAC, or row-level security, matched to the identity system your organisation already runs. An engineering login cannot retrieve a People document even when it is the most semantically relevant result in the index.
Two sources retrieved. Two filtered out before the search ran, not after the answer was drafted.
On-call runs in one-week rotations with a recovery day after each shift, per the engineering handbook, and the runbook sets the escalation tiers. The pay specifics live in People documents this role cannot retrieve, so the answer says so instead of guessing.
An illustrative pairing; the boundary it demonstrates is real and enforced at the retrieval layer.
Why enterpriseRAG fails.
It is rarely the model. Failed deployments stumble on the same three engineering gaps, and every one of them is tractable when you treat it as engineering rather than configuration.
Retrieval treated as solved.
It is not. Chunking strategy, hybrid search, and reranking matter enormously; a naive top-k similarity search returns plausible context, and the model dresses plausible up as true.
We engineer the retrieval layer itself: chunks split on logical boundaries, dense vectors paired with sparse BM25, a cross-encoder reranking the candidates, and the vector store benchmarked for your workload before we commit to one.
No golden dataset.
No golden dataset means no accuracy measurement. The demo looks right, and nobody in the room can say how often it actually is.
Every system ships with a golden set built from your real questions, scored with RAGAS and an LLM judge, and calibrated against human review. Accuracy becomes a number you track, not a feeling you defend.
Access control forgotten.
Role-based document permissions must propagate through retrieval. Index everything into one store and the most semantically relevant passage will eventually be one the reader was never cleared to see.
Permissions filter the index before the similarity search runs: RBAC, ABAC, or row-level security, matched to your IAM. The retriever never sees what the reader cannot.
We engineer all three. That is the difference between a demo and a system.
One conveyor,six stations.
Documents go in one end; grounded, cited context comes out the other. Each station is a craft decision we make for your corpus, not a default we inherit from a framework.
PDFs, SQL, Notion, Slack: the silos connect through one ingestion layer and get cleaned before anything is split.
The store underneath is chosen the same way: Pinecone, Qdrant, Weaviate, pgvector, or Elasticsearch, benchmarked on your workload before we commit to one.
Measured beforeit ships.
We do not ask you to trust the demo. Accuracy is scored on a golden set built from your real questions, and the system clears its gates before it goes anywhere near production.
If it cannot clear the bar on your data, it does not ship. That is what the bar is for.
Common questions about RAG and knowledge systems.
What teams ask before they put retrieval in front of their documents.
Three reasons: (1) they treat retrieval as solved (it isn't: chunking, reranking, and hybrid search matter enormously), (2) they skip evaluation (no golden dataset means no accuracy measurement), (3) they forget access control (role-based document permissions must propagate through retrieval). BearPlex engineers all three.
RAG injects knowledge at query time (good for frequently-changing data, specific facts, permissions). Fine-tuning bakes knowledge into the model weights (good for style, format, implicit behaviors). Most enterprise deployments use both: fine-tune for tone, RAG for facts.
Pinecone for managed production deployments. Qdrant for self-hosted. Weaviate for hybrid search at scale. pgvector for teams already on Postgres. Elasticsearch for teams already on Elastic. We benchmark for your specific workload before choosing.
We implement filter-first retrieval: before any similarity search, we filter the index by user permissions (RBAC, ABAC, or row-level security depending on your IAM). This ensures an engineering user can't retrieve HR documents, even if they're the most semantically relevant result.
RAGAS for automated metrics (faithfulness, answer relevancy, context precision/recall). LLM-as-judge for nuanced quality. Human eval on a sampled golden set for calibration. We target >90% faithfulness and >85% answer relevancy before production.
Ask your companyanything.
If the answer lives somewhere in your documents, we can make it findable, cited, and scoped to the reader asking. Bring the corpus; we will engineer the rest.