RAG Development Services
When you need your LLM to answer from your own documents — policies, manuals, contracts, internal wikis — rather than from general training data, that's a RAG system (retrieval-augmented generation). A RAG developer designs the full pipeline: ingestion, embeddings, vector storage, retrieval, re-ranking and grounded generation. We build those pipelines end-to-end, ship them as APIs and monitor them in production.
- RAG development services — custom pipelines, not off-the-shelf chatbot SaaS
- LangChain · LlamaIndex · pgvector · Weaviate · Pinecone · Chroma
- Hybrid search (keyword + semantic), re-ranking, citation to source chunks
- POPIA-aware data handling — what is stored, where, who can see it
- Remote-first scoping, handoffs and ongoing support
We work with South African businesses every day — teams in Cape Town, Johannesburg, Durban and elsewhere in the country. Delivery is remote-first; we align on POPIA and your internal processes regardless of where you are based.
We also take on international projects when the fit is right: the same engineering discipline, with contracts, data residency and meeting cadence scoped per engagement.
Not sure if this is the right page?
- This page (RAG developer): you know you need document Q&A, a knowledge base assistant, or grounded generation over proprietary data — and you want engineers who ship pipelines, not a vendor chatbot.
- AI development: broader LLM engineering — classification, automation, fine-tuning decisions, production ML; RAG is one pattern within it.
- AI agency: not yet sure what to build — strategy, roadmaps and prioritisation first.
Production RAG
Shipped into real systems
4+ Vector Stores
pgvector · Weaviate · Pinecone · Chroma
POPIA-Aware
Data handling from day one
Multi-Industry
Insurance · finance · education · legal
See RAG in production first
Browse industry-specific RAG examples — document Q&A for insurance, curriculum assistants for education, and extraction pipelines for finance. Then come back here to scope your build.
What a RAG Developer Builds
Concrete deliverables — not slide decks. A RAG engineer at Zenovah designs, builds and ships the pipeline end-to-end: ingestion to generation, with evaluation, monitoring and human review where the business needs it.
Document Q&A Systems
Chat with your PDFs, contracts, manuals and policies. We ingest, chunk, embed and index your documents — then wire retrieval to a generation step that cites its sources. Answers stay grounded; hallucinations are measurable and controlled.
Knowledge Base AI
Internal wikis, SOPs and product catalogues turned into a knowledge base assistant your team can query in plain language. Role-gated access, topic guardrails and audit logs so enterprise requirements are met from the start.
RAG Chatbot Development
Customer-facing or internal RAG chatbots grounded in your approved content — not generic web training data. Chat UI, API or embedded widget; WhatsApp and web channel integrations available.
Semantic & Hybrid Search
Replace keyword-only search with semantic search (vector similarity) or a hybrid that combines keyword + vector for best precision and recall. Built on pgvector, Weaviate, Pinecone or Chroma depending on your infrastructure.
RAG API & LMS Integration
A RAG pipeline exposed as a clean API or integrated into your LMS, CRM or web app. FastAPI backend, async jobs for indexing, streaming endpoints for chat UX — wired to your existing systems, not a new silo.
RAG Pipeline Implementation
End-to-end RAG implementation: ingestion and pre-processing, chunking strategy, embedding model selection, vector store setup, retrieval tuning, re-ranking, prompt engineering and generation — evaluated before go-live and monitored after.
RAG vs Fine-Tuning — Which One Do You Need?
The most common question before scoping a build. Short answer: most business document Q&A needs RAG, not fine-tuning. Here's why — and when to reconsider.
RAG — use when:
- Your documents change often (policies, products, legal updates) — retrieval reflects the latest without retraining
- You need answers traceable to a source document — citations are a first-class feature
- Your corpus is large — thousands of pages; embedding and retrieval beats shoving it all into context
- You need to control what the model can and cannot answer — retrieval scope is a guardrail
- Budget and timeline — RAG ships faster than a fine-tuning cycle and is cheaper to iterate
Fine-tuning — consider when:
- You need a specific tone, format or domain vocabulary baked into the base model — not just injected at prompt time
- Your corpus is static and small enough that retrieval adds more complexity than it removes
- Latency is critical and you can't afford retrieval round-trips in your UX
- You have labelled task-specific examples and evaluation sets ready — fine-tuning without these is guesswork
Many real systems combine both: a fine-tuned base model with RAG on top. We scope which combination makes sense for your problem, data and budget.
Production RAG Pipeline Engineering
Every step in a RAG pipeline is an engineering decision that affects answer quality. A RAG engineer thinks about retrieval, not just generation.
Ingestion & chunking
Bad chunking = bad retrieval. Full stop.
- Document pre-processing: PDF extraction, OCR for scans, table handling, metadata preservation (source, date, section)
- Chunking strategy: fixed-size with overlap, recursive by heading, semantic boundaries — chosen per document type, not one-size-fits-all
- Embedding model selection: OpenAI embeddings, open-source sentence-transformers, multilingual models — tradeoffs in cost, quality and data residency
Retrieval & re-ranking
Retrieval is half the product — generation is only as good as what you feed it.
- Vector search: approximate nearest neighbour (ANN) in pgvector, Weaviate, Pinecone or Chroma — choice driven by your infra and scale
- Hybrid search: combine BM25 keyword with vector similarity — better precision on exact terms (product codes, names, numbers)
- Re-ranking: cross-encoder or LLM-based re-rank of top-k candidates before generation — measurably improves relevance without increasing vector store cost
Generation & citations
Grounded answers with source traceability.
- Prompt engineering: system prompt, retrieved context injection, citation formatting — versioned and in source control like code
- Citations: answer references back to specific chunks and source documents — auditable by humans
- Confidence & fallback: low-retrieval-score responses flagged or routed to a human queue rather than silently hallucinating
Evaluation & monitoring
A RAG system you can't measure is a demo.
- Retrieval evaluation: recall@k, MRR, NDCG — do the right chunks come back?
- Generation evaluation: faithfulness to retrieved context, answer relevance, hallucination rate — measured on golden test sets
- Production monitoring: latency, token usage, retrieval score distributions and failure rates — alerts before users complain
RAG Engineering Stack
We choose tools based on your data, infra and team — not hype. Below is what we regularly use when building RAG development services.
Orchestration & retrieval
- LangChain — chain composition, tool use, memory patterns
- LlamaIndex — document loading, index types, query engines
- Custom pipelines (no framework lock-in when it adds complexity)
- OpenAI · Azure OpenAI · Anthropic · open-source LLMs
Vector databases
- pgvector — Postgres extension; lowest ops overhead for existing Postgres users
- Weaviate — hybrid search (BM25 + vector) built in; good for large corpora
- Pinecone — managed, low-latency; good when ops burden is a constraint
- Chroma — local and lightweight; ideal for prototypes and smaller deployments
Backend & APIs
- Python — primary language for all RAG and LLM backend work
- FastAPI — async-first REST / streaming endpoints
- Celery + Redis for background indexing jobs
- Docker, cloud-agnostic; AWS, GCP and Azure deployments
Data & POPIA
- Data minimisation — only embed what needs to be retrievable
- Retention and deletion on request (right to erasure patterns)
- Subprocessor scoping (which LLM provider sees what data)
- Access controls, logging redaction and secrets in vaults
RAG in Production — Examples
These are live patterns we've shipped — document Q&A and retrieval pipelines in different industries. The underlying RAG engineering is the same; the domain, corpus and guardrails differ.
Insurance
RAG over policy wordings, claim documents and underwriting submissions. Extraction + retrieval + structured handoff — not a generic chatbot on your website.
Insurance AI deep dive →Education
Curriculum Q&A grounded in your syllabus and course packs. Answers cite source documents; topic guardrails keep the assistant on-topic. POPIA-aware learner data handling.
Education AI deep dive →Finance & documents
Extraction pipelines over bank statements, invoices and financial documents. Retrieval + extraction with a human validation queue for low-confidence results.
Bank statement extraction →How We Deliver a RAG Build
Scoping
Document types, corpus size, access patterns, downstream systems and POPIA requirements — defined before any code
Baseline & eval
Build a retrieval baseline, create a golden test set, measure recall and faithfulness — before building anything fancy
Pipeline build
Ingestion, chunking, embeddings, vector store, retrieval tuning, re-ranking, generation, API layer — shipped in testable increments
Deploy & monitor
Production deploy with latency, token, retrieval-score and error monitoring — alerts before users notice quality drops
Frequently Asked Questions
What is retrieval-augmented generation (RAG)?
RAG is a pattern where a language model's response is grounded in documents retrieved at query time rather than relying solely on its training data. Your query is embedded, the closest matching chunks are fetched from a vector store, and those chunks are passed to the LLM as context before it generates an answer. The result: answers are specific to your data, traceable to a source, and updated the moment your documents are.
Do I need a RAG developer or can I use an off-the-shelf tool?
SaaS document chat tools work for simple personal use. If you need custom access controls, multi-tenant isolation, domain-specific chunking, integration with your existing systems, POPIA compliance documentation, or production monitoring — you need a RAG engineer building a real pipeline. Off-the-shelf tools can't be evaluated, audited or tuned to your specific document types and retrieval quality bar.
Which vector database should I use — pgvector, Weaviate, Pinecone or Chroma?
It depends on your infra and scale. If you already run Postgres, pgvector is lowest-ops and usually sufficient. For large corpora needing hybrid search out of the box, Weaviate is strong. For a fully managed service with minimal ops overhead, Pinecone. For local development or smaller deployments, Chroma. We scope this decision with you in discovery — it's not a hype-driven choice.
How is POPIA handled in a RAG pipeline?
POPIA affects what you embed (minimisation), where it's stored (data residency), which LLM provider sees it (subprocessor agreements), how long it's retained (deletion on request), and who can query it (access controls). We scope these requirements in discovery and engineer them in — not as an afterthought. This matters especially for education, insurance, finance and HR corpora.
Should I use LangChain or LlamaIndex for my RAG pipeline?
LangChain is broader — chains, agents, tool use, memory. Good when you need more than retrieval. LlamaIndex is retrieval-first — better document loading primitives, index types and query engines. For straightforward document Q&A, LlamaIndex often leads to simpler code. For complex multi-step agents that also retrieve, LangChain. For many pipelines, neither is strictly required — a thin custom retrieval layer is less magic and easier to debug.
How long does it take to build a RAG system?
A focused pipeline over a single document type with a clean corpus can be production-ready in 4–8 weeks. Multi-tenant systems, complex document types (tables, scans), custom re-ranking and deep integrations typically take 2–3 months. We quote after a scoping call where we see your actual documents and access patterns — not before.
Related Services
Scope Your RAG Pipeline
Looking to hire a RAG developer or a RAG engineer for a document Q&A, knowledge base or retrieval system? Share your document types, corpus size and where answers need to land. We reply with a scoped approach — not a generic demo. South African businesses: we understand POPIA constraints from the start.
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