Generative AI · Enterprise RAG
RAG Development Services
RAG development services build AI assistants that answer from your own documents and data, with citations and the same access rules your systems already enforce. Aaga engineers the full pipeline, from ingestion and hybrid search to evaluation and monitoring, so your knowledge base chatbot gives answers people can check.
- Answers grounded in your documents, with citations to the source passages
- Each user retrieves only what they are already allowed to see
- Accuracy measured on your real questions before and after every release
- Run on cloud model APIs or self-hosted open models in your environment
- Prove accuracy on your documents before scaling
- Pilot first
- Cloud APIs or self-hosted open models
- Model-agnostic
- Reply to your inquiry within one business day
- 1 day
What Do Our RAG Development Services Cover?
A reliable enterprise RAG system is mostly retrieval engineering. These are the parts we design, build and tune for you.
Ingestion and Chunking
Connectors for SharePoint, Google Drive, Confluence, helpdesks, databases and PDFs, with OCR, table extraction, structure-aware chunking and incremental re-indexing.
Hybrid Search and Reranking
Vector search combined with keyword (BM25) search, metadata filters and a reranking model, so exact codes and names match as well as meaning.
Vector Databases
We work with pgvector on PostgreSQL, Pinecone, Weaviate, Qdrant and OpenSearch, and recommend based on scale, hosting and what you already run.
Permission-Aware Retrieval
Document permissions are synced from the source system and enforced at query time, before any text reaches the model.
Citations and Grounded Answers
Every answer links to the passages it came from, and the assistant is designed to say it doesn't know when the sources don't cover the question.
Evaluation and Monitoring
Test sets that score retrieval recall and answer faithfulness, plus traces, feedback, latency and cost dashboards in production.
RAG vs Fine-Tuning vs Prompting
Most business knowledge assistants need RAG. Fine-tuning and prompting solve different problems, and the best systems often combine them.
| RAG | Fine-tuning | Prompting only | |
|---|---|---|---|
| Best for | Answering from your documents and data | Consistent format, tone or a narrow task | General tasks the model already knows |
| Keeps facts current | Yes, re-index when content changes | No, needs retraining | Only what fits in the prompt |
| Citations to sources | Built in | Not available | Only for text pasted into the prompt |
| Respects access rights | Yes, filtered per user at retrieval | No, knowledge is baked into weights | Depends on what you paste in |
| Upfront effort | Moderate: pipeline, index, evaluation | Higher: labeled data, training, evaluation | Low |
Fine-tuning can complement RAG, for example to teach an answer format. It should not replace RAG for facts that change.
How Does an Aaga RAG Project Run?
Scope the Knowledge Domain
We pick one domain, its sources, its users and their access rules, and collect real questions with your team.
Build the Evaluation Set
Questions with reference answers and the source passages that support them, so retrieval and answers can be scored separately.
Prototype on Your Content
Ingestion, chunking, hybrid search and two or three candidate models, compared on the evaluation set.
Harden for Production
Permission sync, PII handling, prompt-injection defenses, caching, fallbacks and spend limits.
Launch, Monitor and Expand
Release to a pilot group with feedback buttons and dashboards, fix gaps, then add the next set of sources.
Where Should Your RAG System Run?
Your RAG system can run on managed model APIs, inside your own cloud account or fully self-hosted. The right choice depends on data sensitivity, volume and the latency your users expect.
- Cloud model APIs. Models such as Claude, GPT and Gemini, called directly or through Amazon Bedrock, Azure or Google Vertex AI, under enterprise terms that exclude training on your data.
- Self-hosted open models. Open-weight LLMs and embedding models served with vLLM in your VPC or data center, when data must not leave your environment.
- Hybrid. Embeddings and the index in your cloud, generation on a managed API, or a small local model for simple questions and a larger one for hard ones.
Cost and latency levers we tune on every project: chunk and context size, the number of passages sent to the model, model routing by question difficulty, prompt caching, response caching for repeated questions and streaming answers so users see text quickly.
What Are RAG Development Services?
RAG development services are the engineering work of building retrieval-augmented generation systems: AI assistants that search your own content first, then have a large language model answer from what they found, with citations. If you are new to the concept, our guide to RAG for business explains how retrieval and generation fit together. This page covers what it takes to build one that holds up in daily use, and how Aaga delivers it.
A RAG demo over a few PDFs takes an afternoon. An enterprise RAG system that answers thousands of questions a week is different. Most of the effort goes into the unglamorous parts: parsing messy documents, keeping the index in sync, enforcing permissions and proving that answers are right.
What Makes Enterprise RAG Hard?
Teams usually hit the same problems when a prototype meets real content and real users.
- Messy sources. Scanned PDFs, tables, slide decks and wiki pages with inconsistent structure. Poor parsing produces poor answers, whatever the model.
- Outdated and duplicate content. Three versions of the same policy, and the old one ranks first. Retrieval needs dates, source-of-truth rules and deletion handling.
- Exact-match questions. Product codes, part numbers, clause numbers and people's names. Pure vector search misses them, which is why we use hybrid search.
- Permissions. A knowledge base chatbot that can quote HR files or board papers to the wrong person is a serious incident. Access rules must be enforced at retrieval, not in the prompt.
- No measurement. Without a test set, every change to chunking, embeddings or prompts is a guess. Teams end up arguing over individual answers instead of looking at scores.
What You Get From an Aaga RAG Engagement
Every enterprise RAG project we deliver includes:
- A source map of systems, owners, update frequency and access rules for the first knowledge domain.
- An ingestion pipeline with connectors, parsing, structure-aware chunking, metadata and scheduled or event-driven re-indexing.
- A retrieval layer with hybrid search, reranking, metadata filters and permission filtering, on a vector store that suits your stack.
- Answer generation with grounding instructions, citations, structured outputs where needed and a clear "I don't know" path.
- An evaluation harness that reports retrieval recall, faithfulness and citation accuracy on every change.
- Production monitoring: traces of each query, user feedback, latency, cost per question and alerts.
- Interfaces your team will actually use: a web chat, a Slack or Microsoft Teams assistant, a widget in your product or an API.
Common Enterprise RAG Use Cases
- Internal knowledge base chatbot over policies, SOPs, IT and HR documentation.
- Support agent assistant that answers from manuals, past tickets and release notes.
- Sales and proposal assistant that finds approved answers in past RFPs and security questionnaires.
- Contract and compliance search that finds clauses and obligations across agreements, with citations.
- Customer-facing assistant on your website or messaging channels, built with our AI chatbot development team.
When an assistant also needs to take actions, such as updating a ticket or a CRM record, RAG becomes one component of an AI copilot or agent.
What Should You Prepare Before a RAG Pilot?
You don't need perfect data to start, but four things speed up a pilot:
- One knowledge domain with a clear owner, such as IT policies or product support.
- Read access to the source systems, ideally through an API or a service account.
- Fifty or more real questions from users, with the answers your experts would give.
- Access rules written down: who may see which documents, and which content is out of scope.
Do You Need Fine-Tuning Too?
Usually not at first. RAG handles knowledge and freshness; fine-tuning changes behavior, such as a strict output format or a domain-specific style. We recommend starting with RAG and good prompts, measuring the gaps, and only then deciding whether LLM fine-tuning would close them.
Why Aaga for RAG Development
Aaga is an AI-native engineering company that has worked with 100+ clients across the USA, Canada, the UK, the Netherlands, Dubai (UAE) and India. You work directly with the senior engineers who build your retrieval pipeline, not a layer of account managers. We are not tied to one model vendor or vector database, so recommendations follow your evaluation results. We start with a scoped pilot on one knowledge domain. And because we build on our own platform for permissions, workflows and integrations, the application around your RAG system comes together faster and costs less.
RAG is one part of our wider generative AI development practice. Talk to us about your documents and the questions your team needs answered.
Frequently Asked Questions
RAG development services design and build systems that retrieve relevant passages from your documents and data, then have a large language model answer from them with citations. The work covers ingestion, chunking, search, access control, prompts, evaluation, monitoring and hosting, not just connecting a model to a folder.
A knowledge base chatbot is one interface built on RAG, usually answering from help articles or policies. Enterprise RAG adds what larger organizations need: many sources, permission-aware retrieval, audit logs, single sign-on, evaluation and monitoring. The same pipeline can then serve a chatbot, a Slack or Teams assistant and an API.
If you already run PostgreSQL, pgvector is often enough and keeps the stack simple. Pinecone suits teams that want a fully managed service, Weaviate and Qdrant offer strong hybrid search and self-hosting, and OpenSearch fits teams already using it for search and logs. We recommend based on data volume, filtering needs, hosting rules and your team's skills.
We sync each document's permissions from the source system, such as SharePoint or Confluence groups, and filter search results by the signed-in user's rights before anything reaches the model. Prompts are never relied on to hide content. Every query, retrieved passage and answer is logged for audit.
We score retrieval and answers separately on a test set of real questions. Retrieval recall checks whether the right passages were found; faithfulness checks whether every claim in the answer is supported by them. We also check citation accuracy and whether the assistant correctly says it doesn't know.
Yes. Open-weight LLMs and embedding models can run in your own cloud account or data center, served with tools such as vLLM, with the index stored alongside them. Quality and cost differ from top hosted models, so we test both on your questions before you decide.
A pilot on one knowledge domain can often be ready in a few weeks, depending on how clean and accessible the content is. Adding more sources, permission sync and integrations takes longer. We share a timeline and budget range after a short discovery call.
Turn Your Documents Into Answers People Trust
Bring one knowledge domain and a list of real questions. We'll show you retrieval and answer quality on your own content.
Plan My RAG Pilot