RAG & Memory Architecture
Retrieval and memory systems that ground AI in your data, not the internet.
Generic AI gives generic answers. We build retrieval and memory systems grounded in your proprietary data: your documents, your policies, your customers. AI answers from your business, not the internet.
Who RAG & Memory Architecture Is For
- Teams whose chat assistant or copilot is hallucinating because it has no grounding in their actual content.
- Companies with deep document corpora (policy, clinical, legal, technical) that need search by meaning, not keywords.
- Product teams adding personalization and need persistent memory the user can audit and edit.
How RAG & Memory Architecture Works
- Step 01
Ingestion and chunking strategy
Inventory the source data, decide on chunk boundaries that preserve meaning, and design for incremental updates instead of full re-indexing.
- Step 02
Retrieval architecture
Vector search, keyword fallback, hybrid scoring, and re-ranking, tuned for the actual queries users send, not synthetic benchmarks.
- Step 03
Evals and grounding checks
Build a labeled eval set from real queries, score retrieval and generation separately, and add citation enforcement so claims trace to source.
- Step 04
Memory layer (when needed)
Persistent, user-scoped memory with explicit write/read/forget semantics, not an opaque vector blob.
What you get
- Ingestion pipeline with incremental update support.
- Retrieval API and grounded generation layer with citation enforcement.
- Answers cite source documents the user can open and verify, not an opaque vector blob.
- Eval suite covering retrieval quality, answer quality, and regression detection on content updates.
Where we've shipped this
All case studies →Frequently asked questions
Why do I need RAG when models keep getting bigger context windows?
Big context windows are expensive, slow, and not a substitute for retrieval. They also do not solve grounding: the model still cannot tell you which document it cited or whether the answer came from your data versus its pretraining. RAG plus memory gives you grounded answers, smaller prompts, lower cost, and audit trails.
What kinds of data work well for retrieval?
Anything structured enough to chunk and indexed enough to retrieve. Documents, policies, support tickets, internal wikis, customer records, product catalogs, and clinical notes all work. Quality of retrieval depends on the chunking strategy and the metadata, not just the embedding model. We tune both during the build.
How do you stop a RAG system from confidently citing the wrong source?
Three layers. Retrieval-side filters and reranking so the top results are actually relevant. Prompt-side instructions that force the model to cite document IDs and refuse when the retrieved context is weak. Eval-side regression tests that catch silent quality drops. Confident wrong answers are a system failure, not a model failure.
Where does the retrieval and memory infrastructure live?
In your cloud, your accounts, your repos. Source code and infrastructure-as-code are yours from day one. We are comfortable with AWS, GCP, Azure, and Cloudflare. Vector store choice is a working decision based on your data shape and existing stack, not a fixed preference. The system you ship is one you can run without us.
Let's build something that actually works.
Tell us where you are and what you need. We'll come back with a clear, honest plan within 48 hours.