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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Ground AI in your data with cited, accurate answers
App Corp builds RAG systems that give AI access to your documents, knowledge bases, and databases — with accurate citations, permission-aware retrieval, and production-grade reliability.
Retrieval-Augmented Generation (RAG) solves the fundamental limitation of generic AI: it does not know your data. Without RAG, an LLM can only answer based on its training data. With RAG, it can search your documents, knowledge bases, and databases, then generate answers grounded in your actual information.
But RAG is not magic. Bad retrieval means bad answers. Bad chunking means missed context. Bad permission handling means data leaks. Bad caching means runaway costs.
App Corp builds production RAG systems with the engineering discipline they require: hybrid search (vector + keyword), intelligent chunking, permission-aware retrieval, citation tracking, evaluation pipelines, and cost optimization. We use PostgreSQL with pgvector for most deployments — avoiding the operational overhead of dedicated vector databases when they are not needed.
Every RAG system we build is designed for accuracy, security, cost control, and maintainability.
Estimate architecture, cost, and ROI before you commit.
Deep-dives into architecture, cost, and implementation.
Retrieval-Augmented Generation lets AI answer questions using your actual data instead of generic training knowledge.
When to use retrieval vs training — and why RAG is usually the better choice for enterprise data.
Production patterns for building accurate, cost-effective RAG systems.
How to prevent data leaks and enforce access controls in RAG systems.
Real projects, real architecture, real outcomes.
We build RAG systems that actually work — accurate citations, permission-aware retrieval, cost control. Tell us about your data.