Enterprise RAG Development Services & Retrieval Architecture
Reviewed by Umar Abbas • CTO & Principal AI Architect
Last reviewed: 14 August 2026
Enterprise RAG development builds retrieval-augmented generation systems that ground large language models in your own documents, so answers are current and traceable to a source. We design chunking, hybrid retrieval, reranking, and evaluation with pgvector, Qdrant, and Cohere rerankers, then add guardrails and ingestion pipelines that keep the index fresh in production.
The parts of a RAG system that decide accuracy
A RAG demo is easy. A RAG system that stays accurate on ten thousand documents is an engineering problem, and most of it lives in retrieval, not in the model.
Chunking & parsing
Layout-aware extraction for tables and scans, then chunking that keeps meaning intact instead of cutting mid-thought.
Hybrid retrieval & reranking
Dense vectors plus BM25 keyword search, merged and reranked so exact identifiers are never lost.
Evaluation harness
A fixed question set that scores retrieval and faithfulness separately, so you know which half to fix.
Ingestion & freshness
Pipelines that re-embed only changed documents and monitor for stale or failed updates.
The query path, end to end
A query runs two searches in parallel, merges and reranks the results, then the model answers only from what survived. If nothing relevant is retrieved, the system returns a no-answer rather than a guess.
Running both searches in parallel keeps latency close to a single search while recovering the exact-match results that vectors alone miss.
How we deliver a RAG build
Retrieval first, generation second. Run under our core engineering process. We do not tune prompts until retrieval recall is where it needs to be.
1. Corpus and question-set audit
Inspect document types, build a fixed evaluation set of real questions, and set the recall target before building.
2. Parsing and chunking
Layout-aware extraction for tables and scans, then chunking tuned to the corpus rather than a default size.
3. Hybrid retrieval and rerank tuning
Combine dense and sparse search, add a reranker, and push recall up against the evaluation set.
4. Grounding, guardrails, and ship
Require citations, add a no-answer path, wire the ingestion pipeline, and deploy with monitoring for drift and staleness.
Why we score retrieval before generation
Most failing RAG systems fail at retrieval, not generation. Separating the two metrics tells you where the problem is. A high faithfulness score means nothing if recall is low, because the model is faithfully answering from the wrong passages.
{{TODO: publish baseline vs tuned recall@k and faithfulness from the clinical-RAG evaluation set}}
No recall, no answer
If the correct passage is never retrieved, no model, prompt, or reranker can recover it. Retrieval is the ceiling on RAG quality.
Industries that live on document retrieval
RAG matters most where answers must cite a source and be current: regulated records, policies, contracts, and clinical documents.
Clinical retrieval with citations and a strict no-answer path when sources are missing.
Policy and compliance search where the exact clause, not a paraphrase, is what matters.
See every sector where we deploy retrieval systems.
Case studies
Clinical RAG System
A retrieval system for clinical documents built retrieval-first, with citations and a no-answer path.
Read Case Study →More production systems
Browse the full set of retrieval, agent, and automation builds with measured outcomes.
View Case Studies →What goes wrong on RAG projects
1. Tuning the prompt to fix retrieval
The failure: Weeks spent rewriting prompts when the right chunk is never retrieved in the first place.
Our prevention: Measure recall first. Fix retrieval before touching generation.
2. Vectors that miss exact terms
The failure: Pure vector search cannot reliably find a specific product code or clause number.
Our prevention: Hybrid retrieval with BM25 so exact identifiers are always in the candidate set.
3. Silent failure on complex documents
The failure: A pipeline tuned for clean text quietly mangles tables and scanned PDFs.
Our prevention: Assess document types up front and use layout-aware parsing with OCR where needed.
4. An index that goes stale
The failure: The knowledge base is loaded once and slowly drifts out of date, so answers quietly rot.
Our prevention: An ingestion pipeline that re-embeds changed documents and monitors index freshness.
Where this service starts and stops
If you want a conversational assistant on top of retrieval, see AI chatbot development. If you need the data pipelines that feed the index, that is AI data engineering. If several agents will call this retrieval layer, see multi-agent systems. This page is the retrieval system itself.
Terms used on this page
Frequently asked questions
What is the difference between RAG and fine-tuning?↓
RAG retrieves relevant documents at query time and puts them in the prompt, so the model answers from current data it can cite. Fine-tuning changes the model's weights to shift style or format. Most enterprise knowledge problems are a retrieval problem, so RAG is usually the first tool, sometimes combined with light fine-tuning.
Why does our RAG system return wrong or irrelevant answers?↓
Usually retrieval, not the model. If the right chunk is never retrieved, no model can answer correctly. Common causes are poor chunking, embedding-only search that misses exact terms, and no reranking. We measure recall first, because a system that retrieves the wrong context will confidently produce a wrong answer.
What is hybrid retrieval and why does it matter?↓
Hybrid retrieval combines dense vector search, which captures meaning, with sparse keyword search such as BM25, which captures exact terms like product codes and names. Vectors alone often miss precise identifiers. Combining both, then reranking the merged set, is what moves recall from acceptable to reliable on real enterprise documents.
Which vector database should we use?↓
It depends on scale and existing infrastructure. pgvector fits teams already on Postgres who want one system to operate. Qdrant and Pinecone suit larger or higher-throughput workloads with advanced filtering. We pick by your data volume, latency target, and operational preference, and we tell you when the simpler option is enough.
How do you measure whether a RAG system is good?↓
We measure retrieval recall at k, answer faithfulness to the retrieved sources, and citation accuracy, on a fixed question set built from your documents. A single accuracy figure hides where a system fails. Separating retrieval quality from generation quality tells us which half to fix, which saves weeks of guessing.
How do you stop a RAG system from hallucinating?↓
We ground every answer in retrieved passages, require citations, and add a check that flags answers not supported by the sources. When retrieval finds nothing relevant, the system says so instead of inventing an answer. Hallucination usually drops most when retrieval improves, not when the prompt is rewritten.
How do you keep the knowledge base current?↓
We build ingestion pipelines that detect changed documents, re-chunk and re-embed only what changed, and update the index without a full rebuild. Freshness is a data-engineering task as much as a retrieval one, so we define ownership of the pipeline and monitoring for stale or failed updates from the start.
Can RAG work over documents with tables, images, and scans?↓
Yes, with the right parsing. Tables and scanned PDFs need layout-aware extraction and often OCR before chunking, or retrieval quality collapses. We assess your document types up front, because a pipeline tuned for clean text will fail silently on complex layouts, and that failure is invisible until users complain.
Do we own the RAG system and its data?↓
Yes. Your documents, embeddings, index, and code stay in your infrastructure, with private or zero-data-retention model options so your content never trains an external vendor model. You retain full ownership of the pipeline, the evaluation set, and the deployed system.
Fix your retrieval, then your answers
Book a 45-minute session. We start by measuring recall on your documents, so you know where the real problem is before you spend on a rebuild.
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