RAG & Retrieval_
>> RAG quality is a retrieval and evaluation problem first. We design indexing, hybrid search, reranking, citation UX, and eval suites so answers hold up when the corpus and access controls get messy.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
Citeable answers need retrieval that respects ACLs and eval gates, not a dump of the corpus into context. We ship RAG systems with hybrid search, citation UX, and regression suites that block weak releases.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Knowledge assistants that must cite sources
- Multi-tenant document corpora with strict ACLs
- Teams replacing brittle keyword search with grounded generation
Weak fit
- Unstructured dump-everything chatbots with no evaluation plan
- Projects that refuse to invest in corpus quality or access control
Stack: RAG · Hybrid search · Embeddings · Rerankers · Citations · Eval
What we deliver
_> Capabilities on this stack
Retrieval architecture
Chunking strategies, hybrid search, and reranking tuned to your corpus shape.
Citations and trust UX
Answers that show where claims came from so users can verify.
Evaluation harness
Golden sets, faithfulness checks, and regression gates in CI.
Compliance-ready retrieval
ACL-aware fetch, redaction, and audit logs for regulated industries.
RAG delivery
Corpus and ACL map
Know what can be retrieved by whom before picking an index.
Eval-backed slice
One domain with golden questions and citation checks.
Harden and expand
Improve retrieval metrics, cost, and freshness pipelines.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
RAG Quality Guide: Evaluation That Holds Up in Production
A practical guide to RAG evaluation: offline test sets, retrieval and answer metrics, regression gates, and RAG monitoring to reduce hallucinations.
How to Choose a Production AI and RAG Partner
Hire a production AI partner when RAG or agents must survive security review and real traffic. Score vendors on shipped systems, compliance, senior delivery, and evaluation harnesses you keep.
RAG for SaaS: Add Retrieval Augmented Generation to a Boilerplate App
A practical guide to adding RAG to a SaaS boilerplate: data ingestion, chunking, embeddings, evals, security, and production patterns that hold up under load.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
FAQ
We set measurable eval targets per corpus and gate releases on them. Public claim of >98% applies to our PII detection systems, not every RAG answer set.
Long context helps. Production systems still need retrieval, ACLs, freshness, and cost control for large or private corpora.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.