Applied AI
RAG & Enterprise Knowledge
Retrieval systems that ground answers in your documents: hybrid search, reranking, citations, and measured accuracy.
Retrieval-augmented generation is the difference between an assistant that sounds right and one that is right. We build the whole retrieval path: ingestion and chunking tuned to your document types, hybrid semantic and keyword search, reranking, permission-aware filtering so people only see what they are allowed to, and citations on every answer. Then we measure it, because retrieval quality is a metric, not a vibe.
What's included
- Hybrid search & reranking
- Permission-aware retrieval
- Citations on every answer
- Retrieval evaluation
What you get out of it
- Answers grounded in your own documents, with a source link for every claim
- Access control enforced at retrieval time, not bolted on afterwards
- Measured recall and precision, so you know when a change made retrieval worse
Most RAG failures are retrieval failures
When a grounded assistant gives a wrong answer, the model is usually not at fault. It was handed the wrong context. We treat retrieval as its own engineering problem: chunking that respects document structure, embeddings chosen for your domain, hybrid search so exact identifiers still match, a reranker to fix ordering, and an evaluation set that scores whether the right passage made it into the prompt at all.
query: “what is the SLA for enterprise support?”retrieve: bm25(12) + vector(12) -> fused 18 uniquefilter: acl(user=finance) -> 14 permittedrerank: cross-encoder -> top 5 (score 0.91…0.62)generate: grounded answer + 3 citationseval: recall@5 0.96 · groundedness 0.98 · 0 unsupported claims
How we build retrieval
Ingestion & chunking
Parsers per document type (PDFs with tables, spreadsheets, wikis, tickets, code), chunked along real structure rather than a fixed character count, with metadata preserved for filtering.
Hybrid search
Vector similarity fused with keyword search, so product codes, names, and identifiers still match exactly while semantic queries still work. Tuned per corpus, not copied from a tutorial.
Reranking & context assembly
A cross-encoder reorders candidates and a context budget decides what actually reaches the model. It is the cheapest large accuracy win available in most RAG systems.
Permission-aware retrieval
Row- and document-level access control applied at query time, mirroring your existing identity model, so an assistant can never surface a document its user could not open directly.
Graph & structured retrieval
Where relationships matter more than similarity, we combine knowledge graphs and direct SQL over your warehouse with vector search, so questions about entities and aggregates get exact answers.
Technologies we work with
- Qdrant
- pgvector
- Elasticsearch
- OpenSearch
- Cohere Rerank
- Agno
- Langfuse
- Anthropic Claude
- OpenAI Embeddings
- Python
- Postgres
Frequently asked questions
Context windows are large but not free, and stuffing everything in degrades accuracy and cost. Retrieval selects the handful of passages that actually answer the question, which is faster, cheaper, and measurably more accurate. It also scales past the point where pasting stops being possible.
Yes, and it should. We enforce your access rules inside the retrieval query itself, so results are filtered before they ever reach the model.
Incremental sync from the source of truth, via webhooks or scheduled deltas, so edits and deletions propagate. Deleted documents disappear from answers, which is a compliance requirement, not a nicety.
We build a golden question set with your team and score retrieval and groundedness on it continuously. You get a number for how often the right source was retrieved and how often the answer stayed faithful to it.
Next step
Need rag & enterprise knowledge?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.