RAG, retrieval-augmented generation, grounds AI responses in your actual documents, databases, and knowledge rather than relying on what the model memorised during training. The result is an AI assistant that answers from your data, cites its sources, and doesn't make things up when it doesn't know.
We build production RAG pipelines covering the full stack: document ingestion and chunking, embedding and vector storage, hybrid search and re-ranking, context assembly, and the evaluation framework that tells you whether retrieval quality is actually good.
Full-stack RAG, ingestion, embedding, search, re-ranking, and evaluation
Works with Pinecone, Weaviate, pgvector, Qdrant, and other vector databases
Hybrid search combining dense embeddings and keyword retrieval for better accuracy
Evaluation framework to measure and monitor retrieval quality in production
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1Recognition
AI assistant giving confident but wrong answers because it can't access your actual documents?
Built a RAG prototype that works on simple queries but fails on real-world questions from your users?
In short
RAG (retrieval-augmented generation) is a technique that grounds AI responses in external documents or databases by retrieving relevant content at query time and including it in the model's context. It is the right choice when you need an AI system to answer questions from your existing knowledge base, documentation, or data without retraining the model. Fine-tuning is better suited to teaching the model a specific style or task format, not to injecting factual knowledge that changes over time.
Trusted by
RAG pipelines solve a specific problem: your organisation has valuable knowledge locked in documents, wikis, databases, and files, and a general AI model has no access to any of it. The model can only answer from what it learned during training, which doesn't include your product documentation, your contracts, your policy manuals, or your support history.
Retrieval-augmented generation closes that gap. At query time, the system retrieves the most relevant content from your knowledge base and includes it in the model's context. The model answers from your data, not from its training. That is the difference between an AI assistant that is useful for your business and one that is a sophisticated autocomplete.
Capabilities
Ingestion pipelines that process your documents at scale: PDFs (including scanned PDFs via OCR), Word files, HTML pages, markdown, spreadsheets, database exports, and structured JSON/CSV data. Chunking strategy is one of the highest-leverage decisions in a RAG pipeline, the wrong chunking approach degrades retrieval quality regardless of the model quality. We implement fixed-size chunking with overlap for uniform documents, semantic chunking (using embedding similarity to find natural topic boundaries) for long-form content, hierarchical chunking (parent-child relationships) for documents where summary context matters, and document-structure-aware chunking that respects section headings, tables, and lists. Metadata extraction at ingest (document type, author, date, department, access control tags) enables filtered retrieval that combines semantic search with hard constraints. Incremental update pipelines re-index only changed or new documents, not the full corpus, keeping your knowledge base current without the cost of full re-processing.
Vector database selection, setup, and configuration based on your scale, infrastructure preferences, and query latency requirements. Pinecone for fully managed, serverless vector search at any scale without infrastructure overhead. Weaviate for multi-modal retrieval (text and images) or when you need built-in BM25 alongside vector search. pgvector for teams already on PostgreSQL who want vector search without adding a new infrastructure dependency. Qdrant for on-premises deployments with strict data residency requirements. Embedding model selection is based on benchmark performance on your specific domain. OpenAI text-embedding-3-large for highest quality on English text, Cohere Embed for multilingual requirements, or open-source models (BGE, E5, GTE) when data privacy or cost constraints preclude sending content to a third-party API. Multi-tenant index architecture with namespace or tenant ID filtering for platforms serving multiple customers from shared infrastructure.
Hybrid retrieval combines dense vector search with BM25 sparse keyword search, consistently outperforming either approach in isolation. Vector search retrieves semantically similar content that doesn't contain the exact query terms; BM25 catches exact-match keywords that vector search may miss (product codes, names, acronyms). The two signals combine via Reciprocal Rank Fusion or a learned weighting function. Re-ranking using a cross-encoder model (Cohere Rerank, BGE reranker) or LLM judge scores each retrieved chunk against the original query for actual relevance, not just similarity, the top-20 vector search results are re-ranked and the top 3-5 are selected for context. Query expansion generates multiple reformulations of an ambiguous query and retrieves for each, increasing recall for short or underspecified questions. HyDE (Hypothetical Document Embeddings) generates a hypothetical answer to the query and uses its embedding for retrieval when the query structure doesn't match the document structure.
RAG evaluation built before the system goes to production, not added after users start complaining about answer quality. Evaluation dataset built from your actual query distribution (not synthetic examples that don't match real user behavior), paired with expected answers and the source documents that should ground them. Automated scoring covers four dimensions: context recall (did retrieval find the relevant chunks?) and context precision (are the retrieved chunks actually relevant to the query?). The other two are faithfulness (does the generated answer reflect what's in the retrieved context?) and answer relevancy (does the answer address the user's actual question?). RAGAS framework or custom evaluation pipeline depending on your tooling preferences. LLM-as-judge scoring for qualitative dimensions that don't have a ground-truth answer. Regression test suites run in CI/CD so that prompt changes, chunking modifications, and embedding model updates are validated against the quality baseline before deployment.
The RAGAS framework is our default evaluation harness: it exposes the four core metrics as a structured pipeline that runs against your evaluation dataset after each pipeline update. For teams that need full traceability, LangSmith tracing captures every retrieval call, every re-ranking step, every prompt sent to the LLM, and the final response. Latency breakdowns at each stage let you pinpoint exactly where quality degrades on failing queries. Embedding model benchmarking is run at project start on a sample of your actual documents: text-embedding-3-large (OpenAI) vs. BGE-M3 (BAAI, open-source) vs. Cohere Embed v3 on your specific domain corpus, because the model that performs best on MTEB benchmarks does not always perform best on your domain vocabulary. Chunking strategy evaluation tests fixed-size (512 tokens with 64-token overlap) vs. semantic chunking (embedding-similarity boundary detection) vs. hierarchical (parent document with child chunks) on your document types, with retrieval recall measured at each configuration before a strategy is selected. Query transformation evaluation tests whether HyDE (generating a hypothetical answer and using its embedding for retrieval) or step-back prompting (broadening the query to the underlying concept) improves recall for your specific query failure modes. All evaluation results are stored and versioned so quality trends over time are visible, not just the current snapshot.
Production Q&A systems built on RAG for use cases where staff or customers need accurate answers from your specific documents rather than general knowledge. That covers internal policy and procedure lookup, product documentation search, compliance and regulatory reference, customer support knowledge bases, and sales enablement research. Answer generation with source citations displayed alongside the answer, not as footnote numbers but as linked document excerpts that users can verify in one click, addressing the user trust problem that kills adoption when answers can't be traced to their source. Multi-turn conversation handling maintains context across follow-up questions within a session. Confidence scoring and fallback responses for queries outside your knowledge base scope ("I don't have information about this in our documentation") rather than confidently wrong answers. User feedback (thumbs up/down, answer corrections) flows into the evaluation dataset and the continuous improvement cycle.
Enterprise RAG for organizations with large, heterogeneous, and access-controlled document collections. That means legal databases where only certain departments may access certain matter files, compliance repositories where audit access controls must be maintained, technical manual libraries with complex product hierarchies, and multi-department knowledge bases where a sales rep shouldn't retrieve HR or finance documents. Role-based access control at the retrieval layer, users' retrieval is filtered by their permissions before any content is included in the model context, enforcing the same document access rules in the AI system as in the source document management system. Audit logging records every query, every retrieved chunk, and every answer for regulated industries where AI-assisted access to sensitive documents must be traceable. Multi-tenant deployment serves multiple business units or external customers from shared infrastructure with strict data isolation between tenants.
We build RAG pipelines that retrieve accurately, stay current as your documents change, and include the evaluation infrastructure to prove they are working.
AI Development, overview of all AI development capabilities
AI Agents, AI agents using RAG for knowledge retrieval in multi-step workflows
Machine Learning, ML models built alongside RAG for prediction and classification
Computer Vision, computer vision for image and document analysis
Generative AI Development, generative AI applications built on top of RAG pipelines
Vector Database Development, vector database infrastructure for search and retrieval
How it works
The same four steps on every engagement. A 6-week voice AI deployment runs the same shape as a 16-week enterprise build.
We spend the first week understanding the problem, not presenting a solution. Discovery session, interviews with the people closest to the work, workflow mapping, and a technical audit of what you already have. You leave knowing exactly what's broken and why previous attempts didn't fix it.
Low-fidelity wireframes before any code is written. You see the product before we build it. Scope, timeline, and fixed price locked at this stage. No surprises after work starts.
Bi-weekly agile sprints. Weekly progress calls. Direct access to the team and project management tools. Working software at the end of every sprint. Not a big-bang delivery at the finish line.
Production deployment, QA sign-off, load testing, and team handover. You own the full codebase from day one. We stay on for post-launch iteration and support. Nothing gets thrown over the wall.
RAG (retrieval-augmented generation) retrieves relevant content from your knowledge base at query time and passes it to the model as context. Fine-tuning bakes patterns into the model weights during training. Use RAG when your knowledge base is large, changes frequently, or when you need the AI to cite sources, RAG handles all three well. Use fine-tuning when you need the model to learn a specific output style, tone, or task format, not factual knowledge. Most enterprise document Q&A, internal knowledge base, and customer support use cases are better served by RAG than fine-tuning.
We build evaluation datasets from representative queries, the types of questions real users will ask, paired with expected answers and the source documents those answers should come from. We measure retrieval recall (does the right document chunk appear in the top results), context precision (is the retrieved context relevant rather than noisy), and answer faithfulness (does the final answer stick to what was retrieved). We use LLM-as-judge for qualitative evaluation and run regression tests when chunking strategies, embedding models, or prompts change. You get a quality dashboard rather than a subjective sense that it seems to work.
We work with Pinecone (managed, fast, simple to operate), Weaviate (self-hosted or cloud, multi-tenancy support), pgvector (Postgres extension, right for teams already on Postgres who want fewer infrastructure components), and Qdrant (high-performance, good for large-scale retrieval). Database selection is driven by your data volume, query latency requirements, multi-tenancy needs, and existing infrastructure. We don't have a preferred vendor, we use what's right for your context and document the trade-offs before you decide.
A focused RAG pipeline, single document type, one vector database, hybrid search, and basic evaluation, typically runs $20,000--$60,000. Complex enterprise RAG systems with multiple document sources, multi-tenant architecture, advanced re-ranking, and full evaluation infrastructure run $60,000--$150,000. Cost depends on document volume and variety, retrieval complexity, integration requirements, and evaluation depth. We scope before pricing and deliver a fixed-cost proposal.
Work with us
We scope RAG Pipeline Development in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.