Production RAG Systems that actually retrieve the right thing
We engineer end-to-end Retrieval Augmented Generation pipelines — from chunking strategy and embedding selection to hybrid search, reranking, and RAGAS-evaluated production deployments. No black boxes, no prompt wrappers.
What is RAG — and where does naive RAG break?
Retrieval Augmented Generation (RAG) is the architecture that lets a language model answer questions grounded in your private corpus — without retraining. At query time, a retrieval layer fetches the most relevant chunks from your vector store, injects them into the LLM context, and the model generates a sourced answer.
The problem: naive RAG fails in production. Single-vector cosine similarity misses multi-hop questions, struggles with ambiguous phrasing, and breaks when documents contradict each other. Chunk size mismatches cause truncated context or drowned signal. Without reranking, the LLM sees irrelevant passages and hallucinates anyway.
We solve this with advanced retrieval strategies: hybrid search (BM25 + dense vector), HyDE (Hypothetical Document Embeddings), query rewriting, multi-query retrieval, parent-document retrieval, and agentic retrieval for complex reasoning chains. The right strategy depends on your corpus structure, query distribution, and latency budget — which is why we start with a technical discovery, not a sales deck.
"The retrieval pipeline is the product. The LLM is just the last mile. Most RAG failures are retrieval failures, not model failures."
The full engineering stack
Every layer of the RAG pipeline, benchmarked and chosen for your constraints — not cargo-culted from a tutorial.
Embeddings
We benchmark embeddings against your corpus: OpenAI text-embedding-3-large, Cohere Embed v3, and open-source models like bge-large or e5-mistral for air-gapped deployments. The right model depends on domain vocabulary and retrieval latency budget.
Vector Stores
We select and configure the right vector DB for your scale and ops model: Pinecone for managed simplicity, Weaviate for hybrid search, pgvector for teams already on Postgres, Qdrant for self-hosted performance. We handle indexing strategy, metadata filtering, and namespace isolation for multi-tenant access control.
Orchestration Frameworks
We build with LangChain, LlamaIndex, and Haystack — choosing the right abstraction for your use case, and knowing when to drop down to raw API calls for latency-sensitive paths. We avoid framework lock-in by keeping retrieval logic modular and testable.
Retrieval Strategies
Hybrid search (BM25 + vector, RRF fusion), HyDE, multi-query retrieval, parent-document retrieval, and agentic retrieval for multi-hop queries. We choose and compose strategies based on your query distribution — measured, not guessed.
Evaluation
We instrument with RAGAS and TruLens: faithfulness, answer relevance, context precision, context recall, and retrieval MRR. We build custom eval sets from your real queries so benchmarks reflect actual usage — not synthetic toy examples.
Deployment
We ship to AWS Bedrock, Azure OpenAI Service, or self-hosted on EKS / GKE inside your VPC. Fully air-gapped options available with open-source LLMs. CI/CD pipelines, chunking refresh jobs, and monitoring included.
Why RAG over fine-tuning or off-the-shelf chatbots
The most cost-effective and auditable architecture for deploying generative AI on private corpora.
No retraining cost
Fine-tuning GPU runs and labeled data are expensive and go stale. RAG keeps knowledge external — update your corpus, not your weights.
Grounded answers, fewer hallucinations
Every answer is traceable to retrieved source chunks. Faithfulness is measured — not assumed. Users can audit citations before acting on outputs.
Real-time corpus updates
Connect live feeds — SharePoint, Confluence, SQL, APIs, call transcripts. The knowledge assistant reflects today's state of your organization, not last quarter's.
Granular access control
Retrieval is scoped per user role. Namespace isolation in the vector store means finance documents never surface in a sales assistant. Your access model maps directly to retrieval filters.
Where we ship RAG in production
Industry use cases we have engineered and deployed — with real retrieval challenges, not toy demos.
Industrial Knowledge Base
Engineering documentation, maintenance manuals, and compliance specs queried in natural language across 2,000+ users. Hybrid retrieval with metadata filtering by equipment type and revision date.
Healthcare / MedTech Support
User support assistant for a medical software publisher — RAG over product documentation, release notes, and regulatory filings. Faithfulness gated by RAGAS before each release. Deployed in Azure OpenAI Service, EU data residency.
Legal Research
Case file and contract search for legal teams — multi-query retrieval with parent-document chunks to preserve legal context. Answer citations link directly to clause-level source passages.
Fintech / B2B SaaS
Internal policy assistant, pricing Q&A, and onboarding accelerator for sales and support teams. Integrated with Salesforce and Confluence via async ingestion pipelines. Role-based namespace isolation at retrieval time.
Manufacturing / Supply Chain
Supplier qualification and procurement knowledge assistant. RAG over structured and unstructured data — combining SQL lookups with dense retrieval for hybrid answers that cross database and document boundaries.
Agentic RAG
For multi-hop questions that require chaining retrievals — we build agentic pipelines where the LLM decides when to retrieve, what to retrieve next, and when it has enough context to answer. Built on LangChain or LlamaIndex agent abstractions with deterministic fallback paths.
Read our agentic RAG deep-dive →How we engineer your RAG system
Discovery to production — with eval gates at every stage. No handwaving, no "it depends" without a follow-up benchmark.
Technical Discovery
We map your corpus: source formats (PDF, HTML, SQL, API), volume, update cadence, and access control requirements. We collect 50-100 representative queries from your team and build a baseline retrieval benchmark before writing a single line of pipeline code.
- ✓ Corpus audit & chunking strategy decision
- ✓ Embedding model benchmarking on your data
- ✓ Vector DB selection with ops requirements
- ✓ Security & compliance architecture review
Pipeline Engineering & POC
We ship a working POC in 2-3 weeks: ingestion pipeline, vector store indexing, retrieval API, and a minimal UI. We instrument RAGAS metrics from day one and iterate on chunking, reranking, and prompt structure against the benchmark query set — not gut feel.
- ✓ LangChain / LlamaIndex / Haystack pipeline
- ✓ Hybrid retrieval + reranker (Cohere / ColBERT)
- ✓ RAGAS evaluation loop on real queries
- ✓ Auth integration & namespace access control
Production Deployment
We deploy to your target infrastructure — AWS Bedrock, Azure OpenAI, EKS/GKE, or fully self-hosted. We set up CI/CD for the ingestion pipeline, alerting on retrieval quality regression, and documentation for your engineering team to own the system post-handoff.
- ✓ AWS Bedrock / Azure OpenAI / self-hosted
- ✓ CI/CD for corpus refresh & re-indexing
- ✓ Retrieval quality monitoring & alerting
- ✓ Engineering handoff & runbook
Where RAG fails — and how we fix it
We tell you the failure modes upfront. A vendor who doesn't is either naive or not shipping to production.
Multi-hop questions
Single-vector retrieval fails when the answer requires synthesizing multiple non-adjacent documents. Fix: agentic retrieval, iterative chain-of-thought with multiple retrieval steps.
Ambiguous or short queries
A 3-word query gives the vector store nothing to work with — cosine similarity to noise. Fix: HyDE, query rewriting, multi-query retrieval with result fusion.
Conflicting sources
When retrieved chunks contradict each other (old policy vs. new), the LLM blends them into a confident-sounding wrong answer. Fix: metadata-aware retrieval with versioning, faithfulness scoring to surface conflicts explicitly.
Chunk boundary truncation
Fixed-size chunking splits a sentence mid-thought — the retrieved chunk is syntactically correct but semantically incomplete. Fix: semantic chunking, parent-document retrieval, or sliding window with overlap tuned to your document structure.
Deep dives on RAG engineering
Technical content for engineers and technical buyers evaluating RAG implementations.
Understanding RAG architecture
A technical guide to how RAG works — embeddings, vector stores, retrieval strategies, and evaluation. Written for engineers, not executives.
Read the guide → AdvancedAgentic RAG — when retrieval needs to reason
Multi-hop queries, iterative retrieval, and tool-calling agents for knowledge bases that require synthesis across multiple documents.
Read the deep-dive → Case StudyIndustrial RAG at Continental — 67% to 89% answer precision
How we deployed a RAG assistant for 2,000 users at Continental, including the hybrid search architecture and reranking decisions that moved the needle.
Read the case study → Related ServiceLLM Integration
RAG sits inside larger LLM pipelines. We also ship LLM-powered features into existing products — structured outputs, streaming, evaluation, cost control — without the RAG overhead when retrieval isn't required.
See LLM integration → Get StartedEvaluate your RAG use case
Not sure if RAG is the right architecture for your problem? We'll give you an honest technical assessment — no sales pitch, no commitment required.
Book a technical call →Technical FAQ
Ship a production RAG system that actually works
Book a technical call. We will assess your corpus, query distribution, and infrastructure — and give you a straight answer on architecture, stack, and realistic timeline. No pitch deck, no commitment required.