Retrieval-augmented generation specialists
RAG systems engineered for answers people can trust
We build retrieval-augmented generation applications that connect language models to approved business knowledge—with citations, permissions, evaluation, and operational controls built in.
Source-connected
Permission-aware
Measured before launch
01
What production RAG requires
RAG can reduce hallucination and make private knowledge available to language models, but retrieval alone does not guarantee a correct answer. Production quality depends on document preparation, metadata, chunking, embeddings, search strategy, reranking, context assembly, prompting, citations, access control, evaluation, and feedback.
We treat those components as an engineered system. Each design decision is tested against representative questions, source material, user expectations, latency targets, and the cost of failure.
02
End-to-end RAG development services
Source-system connectors and scheduled ingestion
Parsing, normalization, metadata, and document lifecycle handling
Vector, keyword, hybrid, and reranked retrieval
Prompt and context engineering with attributable citations
Identity integration and document-level authorization
Golden datasets and automated evaluation pipelines
Observability for quality, latency, usage, and cost
Deployment, documentation, training, and ongoing optimization
03
A RAG architecture selected for your constraints
There is no universal best stack. A legal research assistant, manufacturing service tool, and internal policy search system have different source structures, risks, update cycles, and evidence requirements. We select components only after understanding those constraints.
04
From representative questions to reliable release
Baseline
Collect real questions, expected evidence, source documents, and current-process performance.
Retrieve
Test ingestion, chunking, metadata, search, filtering, and reranking strategies.
Generate
Design answer behavior, citations, refusal rules, and structured outputs.
Evaluate and operate
Automate quality testing and monitor production feedback, latency, and cost.
05
RAG development deliverables
A production engagement creates reusable assets as well as application features. Your team receives source and ingestion documentation, retrieval configuration, evaluation questions, deployment guidance, monitoring definitions, and a backlog for measured improvement.
Documented data flows and source ownership
Reproducible ingestion and index lifecycle
Retrieval benchmark and reviewed test set
Citation, refusal, and answer behavior specification
Threat model and permission design
Operational dashboard and alert definitions
Runbooks, handoff, and architecture decisions
Prioritized post-launch quality roadmap
Questions
Frequently asked questions
A practical next step
Bring us the workflow that is stuck—not a finished AI specification.
We will help clarify the opportunity, data, risks, and smallest useful way to prove value.