RAG System Development
Retrieval-augmented generation, built for production.
AR Data builds enterprise RAG systems that ground large language model responses in your proprietary data — not the model's training corpus. The result is accurate, auditable, compliance-ready answers drawn from your internal knowledge bases, regulatory documents, product documentation, contracts, and operational records.
We design, build, and deploy end-to-end retrieval pipelines using vector databases, hybrid search, re-ranking, and rigorous evaluation frameworks. Every system we ship is production-grade — not a demo, not a proof of concept that dies in staging.
What we build
RAG is not a single product. The architecture that serves a compliance team searching 50,000 regulatory documents is fundamentally different from a customer support system answering questions over a product knowledge base. We scope each engagement around the specific retrieval problem — the data sources, the query patterns, the latency requirements, and the compliance constraints.
Internal Knowledge Bases
Enterprise organizations accumulate knowledge across wikis, Confluence pages, SharePoint libraries, Notion workspaces, Google Drive, and internal PDFs that no single employee can search effectively. We build employee Q&A systems that ingest all of it — ingestion pipelines that handle mixed document types, chunking strategies that preserve semantic coherence, and retrieval layers that surface the right policy, the right procedure, or the right answer regardless of where it lives. Employees stop guessing and start getting grounded answers sourced from your own documentation.
Compliance and Regulatory Document Search
Regulated industries — financial services, healthcare, government, legal — operate under document-heavy compliance requirements. Compliance teams need to locate specific clauses in hundreds of policy documents, cross-reference regulatory guidance, and verify that internal procedures align with external requirements. We build RAG systems that make this tractable: semantic search over large regulatory corpora, citation-level attribution so every answer traces back to a specific document and section, and access controls that ensure teams only retrieve documents they're authorized to see.
Customer Support RAG
Generic LLM responses are not adequate for product support. Customers asking about your specific integration, your specific version, your specific configuration need answers grounded in your documentation — not the model's approximation of what your product might do. We build customer-facing and agent-assist RAG systems over product documentation, release notes, and support ticket histories. The system retrieves the most relevant content, constructs a grounded answer, and attributes it to the specific document the customer or agent can inspect. Hallucinations are structurally reduced because the model is not inventing — it is retrieving and summarizing.
Legal and Contract Analysis
Legal teams managing large contract repositories need more than keyword search. They need to ask semantic questions — which contracts contain automatic renewal clauses, which agreements include limitation of liability carve-outs for gross negligence, which vendor contracts expire in the next 90 days. We build RAG pipelines that ingest contract PDFs and legal documents, extract and preserve structure (parties, dates, clauses, governing law), and make the entire corpus queryable in natural language. Every answer is traceable to the source paragraph.
Financial Document Retrieval
Financial analysts, portfolio managers, and risk teams work across earnings transcripts, 10-K filings, analyst reports, internal investment memos, and regulatory submissions. Querying across that volume manually is not feasible. We build retrieval systems that handle the specific structure of financial documents — tables, numerical data, footnotes, forward-looking statements — and allow teams to query across the corpus with precision. The retrieval architecture accounts for temporal ordering (a 2022 earnings call is not the same as a 2024 one), numerical extraction, and multi-document synthesis.
Healthcare Knowledge Systems (HIPAA-Compliant)
Clinical teams, care coordinators, and medical coders operate with documentation requirements that are simultaneously exhaustive and time-critical. We build HIPAA-compliant RAG systems over clinical guidelines, formularies, coding references, and internal protocols — deployed in environments that satisfy PHI handling requirements. The architecture is designed from the ground up for data residency, access logging, encryption at rest and in transit, and audit trail requirements. We do not bolt on compliance at the end. It is designed in from the start.
Multi-Modal RAG (Text, Images, and Tables)
Enterprise documents are not plain text. Technical manuals contain diagrams. Financial reports contain tables. Slide decks contain charts. Standard text-only RAG pipelines discard this information at ingestion time, producing retrieval systems that are blind to a significant portion of the document corpus. We build multi-modal pipelines that extract, embed, and retrieve across text, tables, and images — using vision models where appropriate, structured table extraction for numerical data, and layout-aware chunking that preserves the relationship between a figure and its caption or a table and its surrounding context.
The RAG stack we use
We are not tied to a single stack. We select components based on the requirements of the engagement — cost profile, latency tolerance, deployment environment, compliance constraints, and scale. These are the technologies we have production experience with.
Embedding Models
OpenAI text-embedding-3-large and text-embedding-3-small for most production deployments. Cohere embed-v3 for multilingual use cases. BGE and E5 models for self-hosted, air-gapped, or cost-sensitive environments where sending data to a third-party API is not permissible. Embedding model selection has a direct, measurable impact on retrieval quality — we evaluate and benchmark before committing.
Vector Databases
Pinecone for managed, serverless deployments. Weaviate for organizations that need self-hosted vector search with rich filtering and hybrid capabilities. pgvector for teams already running PostgreSQL who want vector search without a separate infrastructure component. Qdrant for high-performance, self-hosted deployments requiring fine-grained quantization and filtering. The right choice depends on your existing infrastructure, your scale, and your data residency requirements.
LLM Layer
Claude (Anthropic) for long-context synthesis, nuanced instruction following, and low hallucination rates on generation tasks. GPT-4o and GPT-4-turbo via OpenAI for broad capability and strong structured output. Llama 3 and Mistral for self-hosted deployments where data cannot leave the organization's infrastructure. Model selection at the generation layer is separate from embedding model selection — we evaluate both independently.
Orchestration Frameworks
LangChain for complex multi-step retrieval chains and tool-augmented pipelines. LlamaIndex for document-centric RAG where the index structure and query engine flexibility are the primary concern. Direct SDK integration for high-performance pipelines where framework overhead matters. We do not over-engineer with frameworks when a simpler architecture serves the use case.
Chunking Strategies
Fixed-size chunking with overlap for simple document types. Recursive character splitting with semantic boundary detection for general prose. Sentence-window retrieval for documents where surrounding context improves answer quality. Hierarchical chunking (small chunks for retrieval, larger parent chunks for generation) for long documents where context windows matter. Structural chunking for HTML, Markdown, and code where document structure provides natural split points.
Hybrid Search and Re-ranking
Hybrid search combining dense vector retrieval with BM25 sparse retrieval consistently outperforms either approach alone for most enterprise document corpora. We implement reciprocal rank fusion or weighted hybrid scoring to merge results. Re-ranking with Cohere Rerank or cross-encoder models runs a second-pass relevance check over the initial retrieval candidates before they reach the generation layer. This significantly reduces the noise passed to the LLM and improves answer precision.
Evaluation with RAGAS
We evaluate RAG systems using RAGAS (Retrieval Augmented Generation Assessment), which provides automated metrics for faithfulness (does the answer stay grounded in the retrieved context), answer relevancy (does the answer actually address the question), context precision (is the retrieved context relevant), and context recall (did retrieval capture the relevant information). We construct domain-specific evaluation datasets for each client, run baseline benchmarks before and after optimization decisions, and use evaluation results to make evidence-based choices about chunking, retrieval depth, and re-ranking thresholds.
What makes a good RAG system
Most RAG failures are not LLM failures. They are retrieval failures. The language model can only synthesize what the retrieval layer hands it. A strong generation model cannot recover from weak retrieval. Here is what we think about when building production RAG systems.
Retrieval quality is the constraint
The ceiling on your RAG system's quality is set at retrieval time. If the retrieval layer does not return the relevant passages, the generation layer has nothing to work with. We measure retrieval quality independently from generation quality — recall@k (did the relevant document appear in the top k results), mean reciprocal rank, and normalized discounted cumulative gain. These metrics guide decisions about embedding model selection, index configuration, and hybrid search weighting before we consider what the language model does with the retrieved content.
Chunk size decisions are not arbitrary
Chunk size is one of the highest-leverage decisions in a RAG architecture, and it is almost always set arbitrarily in prototype implementations. Too small, and individual chunks lose the context needed to answer multi-sentence questions — the answer is split across chunk boundaries and the retrieval system returns half of it. Too large, and you are passing irrelevant content to the generation layer, increasing hallucination risk and cost, and the relevant signal gets diluted in the context window. The right chunk size depends on the nature of the documents, the nature of the queries, and the context window of the generation model. We test systematically rather than defaulting to 512 tokens because a tutorial said so.
Embedding model selection changes everything
The embedding model determines how your documents are represented in vector space, which determines which documents are retrieved for a given query. Two documents that discuss the same concept in different terminology will only be retrieved together if the embedding model maps them to nearby vectors. For domain-specific corpora — legal language, medical terminology, financial jargon, proprietary internal vocabulary — general-purpose embedding models may underperform significantly relative to domain-tuned alternatives. We evaluate on a representative sample of your actual queries against your actual corpus before selecting an embedding model. MTEB benchmark scores are a starting point, not a decision.
Hallucination reduction is structural, not prompting
Telling a language model to "only answer based on the provided context" in the system prompt helps at the margin. It is not a hallucination prevention strategy. Structural hallucination reduction comes from: retrieval depth (how much relevant context reaches the generation layer), context quality (re-ranking to remove noise), citation enforcement (requiring the model to attribute specific claims to specific source passages and verifying those attributions), and confidence thresholding (refusing to answer when retrieval confidence is below a defined threshold rather than generating a plausible-sounding response from nothing). We build these mechanisms into the architecture rather than hoping prompt instructions will hold.
Evaluation methodology determines what you can improve
You cannot optimize a RAG system you cannot measure. We construct domain-specific evaluation datasets from real user queries (or representative synthetic queries when real queries are not available), annotate expected answers and source documents, and run automated evaluation using RAGAS metrics supplemented by human review of failure cases. Optimization decisions — changing chunk size, swapping embedding models, adjusting retrieval depth, tuning re-ranking thresholds — are made against the evaluation set, not by intuition. This is the difference between a RAG system that degrades gracefully as your document corpus changes and one that fails unpredictably.
Industries we serve
RAG systems built for regulated industries require a different level of engineering discipline than general-purpose chatbot wrappers. We have delivered in environments where accuracy is audited, where data residency is a legal requirement, and where a wrong answer has real consequences.
Compliance-ready RAG architectures
Compliance is not something you add to a RAG system after it is built. Access controls, data residency, audit trails, and encryption requirements shape architectural decisions from the start — which vector database you use, where embeddings are computed, how queries and responses are logged, and what data ever leaves your environment.
HIPAA
PHI never touches third-party APIs unless covered by a signed BAA. Embedding computation and vector storage happen within your HIPAA-compliant environment. Access is role-controlled and fully logged. Every query against PHI-containing documents produces an auditable record of what was retrieved, by whom, and when.
SOC 2
RAG system deployments for SOC 2-scoped environments include role-based access control on document namespaces, encrypted data at rest and in transit, audit logging of retrieval and generation events, and deployment architectures that support the availability and confidentiality requirements of a SOC 2 Type II audit.
GDPR
Data residency within EU infrastructure where required. Right-to-erasure implemented at the vector index level — when a document or record is deleted, its embeddings are purged from the vector store. Processing agreements cover all third-party model API calls. Retention policies enforced at the ingestion pipeline level.
Why AR Data
We are not a research team that got interested in building products. We are a delivery firm. The founders of AR Data have spent 20+ years shipping production systems at Oracle, IBM, Protocol Labs, Macquarie, Scotiabank, and Iron Mountain — organizations where the consequence of a broken system is measured in dollars, regulatory penalties, or worse.
That delivery background shapes how we build RAG systems. We design for operational reality: the document corpus will grow, the embedding model will need to be swapped, the evaluation scores will regress when new document types are added, and the compliance requirements will evolve. Systems that are not designed for those realities become technical debt within 12 months of deployment.
Our agentic workflows make us meaningfully faster than traditional development shops — we use AI-augmented delivery throughout the build process, which means the speed advantage is structural, not situational. That speed does not come at the cost of rigor. We evaluate before we ship. We document architectural decisions. We build in the observability needed to operate and maintain the system after handover.
We have shipped in financial services, worked on decentralized infrastructure at Protocol Labs, and built data systems for regulated environments. RAG system development for enterprise is not a new problem class for us — it sits directly at the intersection of the data engineering, AI integration, and production delivery work we have been doing for two decades.
Every engagement produces a system that runs in production — not a prototype handed over as a starting point, not a Jupyter notebook dressed up as a deliverable. If you are evaluating RAG vendors, ask them to show you production systems they have built for organizations with real compliance requirements. That is where the difference becomes visible.
Ready to build your RAG system?
30 minutes. We scope the retrieval problem, the data sources, and the compliance requirements. No pitch deck.
