# RAG & Retrieval Pipeline Development Retrieval pipelines that ground LLMs in your proprietary data. Embeddings, vector stores, reranking, and evaluations | production-tuned, not demoware. Canonical: https://www.metaborong.com/services/ai/rag-retrieval-pipelines Service: ai/rag-retrieval-pipelines ## Overview RAG & Retrieval Pipelines is the engineering of production retrieval systems that ground LLMs in your proprietary data — documents, tickets, catalogues, knowledge bases. The work covers ingestion, chunking, embedding, vector storage, reranking, and evaluation, tuned for production latency. We engineer retrieval for the failure modes that matter in production — stale data, hallucinated citations, tenant leakage, cost spikes — not the demo-friendly ones. Senior engineers own the build, India + global delivery. ## What is it? RAG & Retrieval Pipelines is an engineering engagement for product teams that builds production retrieval systems grounding LLMs in proprietary data through ingestion, embedding, reranking, and evaluation. Builds typically ship in six to twelve weeks. Senior engineers own the work end-to-end, delivered from India with global reach. ## What we deliver - Deployed retrieval pipeline indexed against your corpus on a scheduled cadence - Embedding and reranking layer tuned to your domain and real query patterns - Retrieval evaluation harness measuring recall, faithfulness, and citation quality - Tenant-isolated vector storage with audit logging and rotation policies - Per-query and per-tenant cost dashboards in your observability stack - Documentation and runbook covering the data ingestion lifecycle ## How we work 1. **Corpus and query analysis** We map the data - formats, volumes, update cadence - and the actual queries the system will need to answer. Sample queries are labelled with expected source documents so retrieval quality can be measured, not guessed. Tenant boundaries, PII handling, and data-residency choices are decided here, before any embeddings get generated. 2. **Ingestion and indexing** We engineer the ingestion pipeline - chunking strategy, metadata extraction, deduplication, and re-indexing on update. Embeddings are generated against the model that fits the budget and quality target. Vector storage lands in pgvector, Pinecone, or a managed equivalent, with tenant boundaries enforced at the storage layer rather than the application. 3. **Retrieval and reranking** Retrieval combines vector search, keyword filters, and a reranker tuned to your domain. We test recall against the labelled set from phase one and iterate on chunk size, query rewriting, and reranker configuration. Hybrid retrieval is the default - pure vector search rarely wins in production. Citations are structured for downstream auditability. 4. **Evaluation and integration** The evaluation harness measures retrieval recall, answer faithfulness, and citation quality on every change. The pipeline integrates into your copilot, agent, or chat surface with latency budgets, fallback behaviour, and per-tenant rate limits engineered. Drift and cost are tracked in production. Bugs caught in production land back in the eval set. ## Tech stack OpenAI (Embeddings), Cohere (Rerankers), pgvector (Vector store), Pinecone (Managed vector), PostgreSQL (Metadata), LangChain (Pipelines), Unstructured (Ingestion), Sentry (Observability) ## When this fits ### Fits when - You have a defined corpus - docs, tickets, product data - that should ground LLM responses. - Your queries are domain-specific enough that ungrounded off-the-shelf models fall short. - You can tolerate the latency of retrieval plus generation - typically one to three seconds end-to-end. ### Does not fit when - Your corpus is tiny or queries are generic - a smaller model with a strong prompt is cheaper. - You expect retrieval to recover unstructured chat logs without a labelling pass first. - You need real-time updates with sub-second freshness - retrieval indexes on a cadence, not instantly. ## FAQ ### What does a retrieval evaluation actually measure? Three things - retrieval recall against a labelled set of expected sources, answer faithfulness against the retrieved context, and citation quality. Recall measures whether the right documents surface. Faithfulness measures whether the model uses them faithfully. Citation quality measures whether the output points to specific sources auditors can verify. ### Vector search or hybrid retrieval - which do you use? Hybrid by default. Pure vector search rarely wins on real-world queries with rare entities, exact-match requirements, or domain jargon. We combine vector retrieval with keyword filters, metadata constraints, and a reranker tuned to your domain. The recipe is tuned against your labelled queries, not benchmarks for someone else's corpus. ### What about freshness and updates to the corpus? Indexing runs on a schedule sized to your data - hourly, nightly, or event-triggered. Updates handle inserts, modifications, and deletions correctly, with deduplication and re-embedding only where content changed. Truly real-time freshness is rarely needed and is expensive; we scope it during architecture if it actually matters to the workflow. ### Can you work with our existing vector store? Yes. We work with pgvector, Pinecone, Weaviate, Qdrant, and managed alternatives. The choice depends on scale, tenancy, and operating preference. Where you have an existing store we engineer ingestion and retrieval against it; where you do not, we choose based on data size, latency budget, and your operations team's familiarity.