Sources and ingestion
Connect documents, databases and SaaS systems, clean and chunk content, and keep the index current as sources change.
Retrieval-augmented generation
RAG development services: AI answers grounded in your own data, with sources your team can check.
We design, build and operate retrieval-augmented generation systems on Azure OpenAI and Azure AI Search: ingestion, permission-aware retrieval, citations and evaluation.
The initiative
Move from a difficult technology challenge to a system your teams can use.
For product and technology leaders who need AI answers grounded in company documents, records or product data. We begin with the business context and the systems already in place, then shape a delivery path around what needs to work.
- Document and data ingestion
- Hybrid vector and keyword search
- Permission-aware retrieval
- Answers with citations
- Evaluation and monitoring
When this solution fits
Start with the work your team needs to make possible.
A RAG system is only as good as the content it retrieves. Before choosing models or vector stores, we map the questions users actually ask, the sources that hold the answers, who is allowed to see what, and how answers will be checked.
For product and technology leaders who need AI answers grounded in company documents, records or product data. The first useful step is to define what needs to change, how the current environment shapes the work and which outcome the team needs to be able to assess.
Typical workstreams
What the engineering may involve.
The exact scope depends on your systems and constraints. These workstreams describe common parts of this solution area.
Retrieval and answering
Combine vector and keyword search, re-rank results, enforce permissions and return answers with the passages behind them.
Evaluation and operations
Score answers against real questions, track cost and latency, and review failures so quality improves after launch.
What we build
RAG systems for real documents, real users and real permissions.
Retrieval-augmented generation lets a language model answer from your own content instead of its training data. The model is the easy part. Most of the engineering is in getting the right content to the model, for the right user, and proving the answer is correct.
Internal knowledge assistants
Answer staff questions from policies, procedures, contracts, tickets and wiki pages, with links to the source passage.
Customer-facing product search
Add natural-language search and answers to a SaaS product, scoped to each customer’s own data.
Document question answering
Ask questions across large sets of PDFs, scanned files and spreadsheets, including tables and structured fields.
Support and service copilots
Draft replies grounded in product documentation and past cases, with a person approving what is sent.
RAG inside agents
Give agents a reliable retrieval tool so the actions they take are based on current, approved information.
Rescue of an existing RAG prototype
Review a RAG system that answers inconsistently, find where retrieval fails and fix the pipeline.
The RAG pipeline
Every stage that decides answer quality.
Poor answers usually come from one weak stage in the pipeline, not from the model. We engineer and test each stage separately so problems can be found and fixed.
Ingestion
Connectors for SharePoint, file storage, databases and SaaS APIs. Parsing for PDFs, Office files, HTML and scanned images, with change detection to keep the index current.
Chunking and metadata
Chunk size and structure chosen per document type, with titles, sections, dates, owners and access labels stored alongside each chunk.
Embeddings and indexing
Embedding model selection, vector and keyword indexes in Azure AI Search or another store, and re-indexing when models change.
Retrieval
Hybrid vector and keyword search, filters on metadata, re-ranking and query rewriting, so the most relevant passages reach the model.
Permission-aware access
Retrieval filtered by the signed-in user’s rights, typically mapped from Microsoft Entra ID groups or your application roles, so nobody sees content they should not.
Generation and citation
Prompts that keep the model to the retrieved content, answers with citations, and a clear “I don’t know” or hand-off when the sources do not cover the question.
Quality and operations
How we know the answers are right.
A RAG demo can look correct on ten documents and fail on ten thousand. We measure quality before launch and keep measuring after it.
Evaluation sets
A set of real questions with expected answers and sources, built with your subject experts.
Retrieval and answer scoring
Separate scores for whether the right passages were found and whether the answer used them correctly.
Regression checks
The evaluation set runs on every change to prompts, models, chunking or retrieval settings.
Cost and latency tracking
Tokens, search calls and response time measured per question, with budgets and alerts.
Feedback loops
User ratings and flagged answers routed back for review and used to improve the index and prompts.
Checklist
RAG readiness checklist
Use these questions to judge whether a RAG project is ready to build, or why an existing one is underperforming.
Request an assessmentA clear path from scope to operation
Keep decisions connected through delivery.
Move from a defined initiative to an operating system through focused, reviewable stages.
Understand the workflow
Agree on the users, business context, systems and constraints that define the initiative.
Shape the technical path
Connect architecture, integrations, data, security and delivery decisions to the needs already identified.
Build in useful increments
Deliver and review the system in stages so the work can respond to product and operational feedback.
Prepare to operate
Consider ownership, monitoring, support and the next improvements as part of the delivery plan.
FAQ
Questions about rag development
What are RAG development services?
RAG development services cover the design and engineering of retrieval-augmented generation systems: connecting your content sources, indexing and searching them, passing the right passages to a language model, returning answers with citations and measuring quality. The goal is AI answers grounded in your data rather than the model’s general knowledge.
When should we use RAG instead of fine-tuning?
Use RAG when answers must come from content that changes, must be cited, or must respect user permissions. Fine-tuning changes how a model writes or behaves, but it is a poor way to keep facts current. Many production systems use RAG alone; some combine both.
Do you build RAG on Azure OpenAI?
Yes. Azure OpenAI with Azure AI Search is our most common foundation, together with Microsoft Entra ID for access control and Azure storage and functions for ingestion. We can use other model providers or vector stores when there is a reason to.
Can a RAG system respect who is allowed to see which documents?
Yes, and it should. We store access information with each chunk and filter retrieval by the signed-in user’s groups or roles, so the model never receives content that user is not allowed to see.
How do you reduce hallucinations in RAG?
By improving retrieval first, keeping the model to the retrieved passages, requiring citations, returning “not found” when sources do not cover a question, and measuring answer accuracy against an evaluation set on every change.
Can you fix our existing RAG system?
Yes. We start by testing it against real questions, find whether failures come from ingestion, chunking, retrieval, permissions or the prompt, and fix the weakest stages first. A rebuild is rarely the first step.
What content can a RAG system use?
Most text-based content: PDFs, Office documents, web pages, wikis, tickets, emails, database records and SaaS data through APIs. Scanned documents and tables need extra processing, which we plan for during discovery.
How long does a RAG project take?
It depends on the number of sources, access rules and integrations. A short discovery defines the scope, the evaluation set and a realistic delivery plan before build work begins.
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