// SERVICE
RAG Pipeline Development
Connect an LLM to your documents so it answers with citations instead of inventing. We build production RAG systems that hold up under adversarial testing — on your PDFs, your contracts, your emails, your Slack archive, your codebase.
What RAG actually is (and isn't)
Retrieval-Augmented Generation is the architecture pattern where an LLM is given relevant passages from your documents as context at query time, and told to answer only from that context with citations. It is the correct answer for nearly every "AI on our company's knowledge" problem — and it is also consistently done badly.
A bad RAG system retrieves irrelevant chunks, loses cross-reference context, hallucinates confidently, and has no way to measure whether it's getting better or worse. A good one treats retrieval as an engineering problem with measurable rates, not a vibes-based prototype.
RAG vs fine-tuning — the actual tradeoff
Fine-tuning changes what a model knows. RAG changes what a model can see at query time. For 90% of real-world "AI on our docs" problems, RAG wins on every axis: cheaper, faster to update (drop a new document in, it's searchable), and preserves citations for auditability.
Fine-tuning is the right tool when the problem is about style or format — consistent voice, domain-specific output structure, low-resource languages. For factual knowledge, RAG is almost always the better answer. We will tell you when it isn't.
What a production RAG pipeline includes
01. Document ingestion
PDFs (including scans via OCR), Word, Excel, PowerPoint, HTML, Markdown, email archives (PST, MBOX), Slack exports, Confluence, Notion, SharePoint, code repositories. We handle the messy stuff — tables split across pages, footnotes, nested headers, inline figures, columnar layouts — because real documents don't look like the RAG tutorials.
02. Chunking strategy
Naive fixed-size chunking loses cross-boundary context and destroys table and list semantics. We use structure-aware chunking (heading-preserving, table-aware, code-block-aware) with overlap tuning measured against your actual retrieval task — not a default from a blog post.
03. Embedding and indexing
Embedding model selected per use case (BGE, E5, nomic-embed, domain-specific fine-tunes). Vector database based on scale and ops tolerance: Qdrant, Weaviate, pgvector, LanceDB. Hybrid retrieval (dense + BM25) for queries with rare keywords — pure vector search silently fails on specific identifiers, part numbers, acronyms.
04. Retrieval + reranking
Top-K retrieval is the first step, not the last. A cross-encoder reranker on the top 20 candidates routinely outperforms the top-5 from pure vector search by wide margins. Metadata filtering (by date, author, department, document type) cuts noise before reranking.
05. Generation with enforced citations
The LLM prompt enforces: answer only from provided context, cite every claim with a source chunk ID, refuse if the answer isn't supported. System-level validation checks that every claim maps to a retrieved chunk before the answer ships to the user. Citations link back to the source document and page.
06. Evaluation and continuous measurement
Test set of real questions with known-good answers. Retrieval accuracy (did we find the right chunks?), answer grounding (is every claim supported?), refusal correctness (did we correctly say "I don't know"?) — all measured on every change. If the numbers move the wrong way, you see it before your users do.
What kinds of documents this works on
- Legal and compliance. Contract analysis, regulatory lookup, case law search, policy Q&A. See AI for legal firms.
- Healthcare. Clinical guidelines, protocol lookup, patient education content, medical literature. HIPAA-compatible when deployed locally. See AI for healthcare.
- Financial services. Analyst reports, filings, internal research, compliance documentation. See AI for finance.
- Engineering and operations. Runbooks, design docs, code search, incident postmortems, vendor documentation.
- Sales enablement. Product documentation, pricing, proposal libraries, customer history.
Common RAG failures — and how we avoid them
Silent irrelevance. Retrieval returns chunks the LLM weaves into a plausible answer that doesn't actually address the question. Fix: retrieval accuracy measured on every build.
Citation theater. The model cites sources that don't support the claim. Fix: citation-validator middleware that rejects answers whose claims don't map to retrieved chunks.
Stale context. A document was updated but the vector index wasn't. Fix: ingestion pipeline with change detection and automatic re-indexing.
Coverage gaps. The system confidently answers questions outside its corpus. Fix: enforced refusal threshold tied to retrieval confidence.
Pricing
POC ($25K–$60K, 4–6 weeks): one document corpus, one interface, real evaluation harness. Production build ($75K+, 8–16 weeks): access control, audit logging, multi-tenant isolation, admin UI, 30-day support. All local by default — see local AI deployment.
Frequently asked questions
What is RAG and why use it over fine-tuning?
RAG (Retrieval-Augmented Generation) connects a large language model to your specific documents at query time. Instead of relying on the model's training data, RAG retrieves relevant passages from your knowledge base and uses them to generate sourced answers. It dramatically reduces hallucinations, provides citations for every claim, works with any document format, and updates instantly when documents change — no retraining required. Fine-tuning is the right answer for style/format problems; RAG wins for factual knowledge.
Which vector database do you use?
It depends on scale and ops preferences. Qdrant and Weaviate for dedicated vector workloads. pgvector when the rest of the stack is already Postgres. LanceDB for embedded or local-first deployments. We benchmark on your actual corpus and query patterns before picking.
Does RAG work with scanned PDFs?
Yes, with OCR in the ingestion pipeline. Tesseract or a commercial OCR engine handles scans; the output gets the same structure-aware chunking as native-text PDFs. Image-heavy documents (engineering drawings, medical imaging) need additional vision models if the images themselves need to be searchable.
How do you prevent RAG hallucinations?
Four layers: (1) retrieval accuracy measured on a test set so you know when irrelevant chunks are being returned, (2) prompt engineering that enforces answer-from-context-only, (3) citation-validator middleware that rejects answers whose claims don't trace to retrieved chunks, and (4) a refusal threshold tied to retrieval confidence so the system says "I don't know" instead of inventing.
Can RAG run entirely offline, on local hardware?
Yes. Every component of a RAG pipeline — embedding model, vector database, reranker, generator LLM — has a local open-source option. We run this configuration often for regulated-industry clients where cloud dependency is a non-starter. See our local AI deployment service.
Ready to start?
Book a free 30-minute AI Readiness Assessment. No pitch deck. No retainer ask. Just a working session to map your stack and surface the two or three highest-ROI AI interventions for your situation.