RAG Engineering

RAG Implementation Services for Enterprise

RAG (retrieval-augmented generation) implementation connects a language model to your own data so it retrieves relevant, current information before it answers. MobAppAI builds these systems end to end — ingestion, vector search, grounded generation, and evaluation — for enterprise teams.

What RAG actually does

A standalone language model can only answer from what it learned during training — it has no knowledge of your internal documents, and it can confidently invent details. RAG fixes both problems. Before the model generates an answer, the system searches your own content (documents, databases, tickets, wikis) for the most relevant passages and supplies them to the model as context. The result is grounded in your data and can cite its sources.

When to use RAG

  • Answers must reflect current or frequently-changing information.
  • Responses need to be grounded in private or internal knowledge.
  • You need citations and traceability for trust and compliance.
  • Retraining a model every time the data changes is impractical.

If instead you need a model to adopt a fixed format, tone, or narrow skill, fine-tuning is often the better tool — see RAG vs fine-tuning. Many enterprise systems combine both.

The architecture we build

  • Ingestion & chunking: connect to your sources, normalize mixed formats (PDF, HTML, DOCX, databases), and split content into clean, retrievable chunks with metadata.
  • Embeddings & vector store: convert chunks to embeddings and index them in a vector database (e.g. Qdrant, pgvector) with metadata filters for fast, precise retrieval.
  • Hybrid retrieval: combine keyword and semantic (vector) search with re-ranking so the most relevant context is selected per query.
  • Grounded generation & guardrails:prompt the model with retrieved context, return citations, and detect and refuse when the answer isn't supported by your data.
  • Evaluation: measure retrieval quality and answer faithfulness against a test set, so you ship with confidence and catch regressions before users do.

Security & compliance

Enterprise RAG lives or dies on data control. We build with least-privilege access to your sources, keep embeddings and indexes inside your environment where required, support self-hosted or private-endpoint models so prompts and documents don't leak, and enforce per-document access controls so users only retrieve what they are allowed to see. More on our security practices.

Our process

We work in two-week sprints: discovery and architecture, a pilot on a slice of your real data, an evaluation pass, then production rollout with full code and documentation handover. You review sandbox builds throughout. This page is part of our AI solutions practice; new to the topic? Start with What is RAG?

RAG Implementation FAQ

What is RAG (retrieval-augmented generation)?
RAG is a technique where an AI model retrieves relevant information from your own databases or documents before generating an answer, so responses are grounded in your data rather than the model's training alone. It's the foundation of accurate enterprise AI assistants and search.
How long does a RAG implementation take?
A focused pilot on a single data source typically ships in a few weeks; a production system across multiple sources with access controls and evaluation takes longer. We scope a fixed estimate after a short discovery session.
Is our data sent to a third-party model provider?
Only if you choose a hosted model. We also support self-hosted and private-endpoint deployments so your prompts, documents, and embeddings stay inside your own environment.

Planning a RAG system?

Tell us about your data sources and use case, and we will map an architecture and a fixed estimate.

Scope your RAG project