Answers grounded in your own knowledge with citations your auditors can follow.
Retrieval-augmented systems tuned to your data, your latency budget, and your compliance posture — with citations your auditors can trace.
- Typical duration
- 8–14 weeks
- Squad
- 3–4 senior engineers
- Starting at
- $32,000 / month
- Answer grounding rate
- 97%Answer grounding rate
- Analyst throughput
- 2xAnalyst throughput
- Benchmark questions evaluated
- 1,200Benchmark questions evaluated
Retrieval quality is the whole game — and it's usually the part nobody measures.
A weekend RAG prototype is trivial. A RAG system your compliance team will sign off on is not. The difference is measured retrieval: hybrid search tuned on real queries, chunking that respects document structure, permissions enforced at retrieval time, and a citation for every claim the model makes.
Sound familiar?
- Confident answers sourced from the wrong document
- Retrieval that works in the demo corpus and fails on the real one
- No way for a user to verify where an answer came from
- Permission leakage — users seeing content they shouldn't
How we deliver rag systems.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Build the query set
We collect real questions from real users and label the correct source passages. Without this, every retrieval decision downstream is a guess.
Engineer the corpus
Parsing, structure-aware chunking, metadata enrichment, and de-duplication. Most retrieval failures are actually ingestion failures.
Tune retrieval
Hybrid dense + lexical search with reranking, tuned against the labeled set until recall@k clears the bar the task requires.
Ground & verify
Span-level citations, a groundedness check on every answer, and permission filters applied at query time rather than after generation.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Hybrid retrieval
Dense embeddings plus BM25 with a cross-encoder reranker — because pure vector search reliably misses exact identifiers and codes.
Citation grounding
Span-level attribution back to the source document and page, so every claim is one click from its evidence.
Document pipelines
Robust ingestion for PDFs, tables, slides, wikis, and ticket systems — with structure-aware chunking and incremental re-indexing.
Permission-aware search
ACLs enforced inside the retrieval query, so a user's results are filtered before the model ever sees the content.
Evaluation harness
Recall@k, groundedness, and answer-quality scored on every change — the regression gate that keeps quality from quietly decaying.
Freshness & re-indexing
Change-data-capture pipelines that keep the index current without full rebuilds, with staleness visible on a dashboard.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Claude
- OpenAI
- LangChain
- pgvector
- Elasticsearch
- Python
What this looks like in production.
Grounding an AI research analyst on a decade of data
Atlas Capital
- Problem
- Analysts spent 60% of their day searching filings, transcripts, and broker notes for context.
- Solution
- A citation-grounded RAG system with role-aware controls, evaluated against a 1,200-question benchmark.
- Outcome
- Analyst throughput doubled on coverage tasks and onboarding time for new hires dropped by half.
- Analyst throughput
- 2xAnalyst throughput
- Onboarding time
- −50%Onboarding time
- Answer grounding
- 97%Answer grounding
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.