Answers Grounded In Your Actual Data
We build retrieval-augmented generation pipelines that pull from your documents, databases, and knowledge bases — so your AI stops guessing and starts citing.
Retrieved Section 4.2 of SLA Contract: "Guaranteed < 15 minute resolution window."
Source: Enterprise_SLA_2026.pdf (Chunk #84)About the RAG Pipelines
Retrieval-augmented generation is the difference between an AI that sounds confident and one that's actually right. We connect a model to your real documents, data, and knowledge base — grounded, cited, and verifiable instead of statistically plausible. Our RAG for a 120-page refrigerator product catalog turns pricing, dimensions, and specs into a natural-language Q&A assistant a customer can actually query.
The hard part isn't calling an embedding API — it's chunking strategy, retrieval quality, and keeping the index current as source content changes. We treat that as the real engineering problem and build source citations in from day one, so every answer traces back to where it came from.
Fits teams with a real body of documents a model keeps getting wrong. Not the right starting point if that content doesn't exist yet — no pipeline can ground answers in documentation that isn't written.
Everything You Get With RAG Pipelines
Custom Knowledge Base Retrieval
Document Ingestion & Chunking
Vector Database Setup & Optimization
Hybrid Search (Keyword + Semantic)
Reranking for Retrieval Precision
Continuous Data Sync
Hallucination Reduction & Citations
Accuracy Evaluation Harness
Your Cloud or On-Prem Option
Full Evaluation with Modern Frameworks
Where RAG Pipelines Fits
Documentation-Heavy Teams
That 100-page manual becomes a system anyone can just ask — answers in seconds, not searches.
Support & Sales Teams
Every rep pulls the same accurate answer from the same source, every single time.
Data-Heavy Products
Pricing, specs, and inventory update live — nothing goes stale because nobody remembered to update it.
Internal Knowledge Bases
Your wiki becomes genuinely searchable in plain language, not just technically searchable.
Production AI Systems In Action
RAG Pipelines case studies from real engagements, published as projects complete.
RAG Pipelines
Bertos Refrigerators Company RAG
A production-grade RAG system for Bertos that turns a 120-page product catalog into a natural-language Q&A assistant for pricing, dimensions, and specs.
RAG Pipelines
Microsoft Annual 2025 Financial Report Rag
A RAG system that answers questions directly from Microsoft's 2025 annual financial report, with source citations, saving hours of searching.
Voice Agents
Real Time Avator Voice Agent
A real-time conversational voice agent with a live AI avatar that answers spoken questions from a 200+ page Microsoft annual report, grounded in the source.
How We Build It
Data Audit & Chunking Strategy
We audit your source documents and design a chunking/metadata strategy that preserves context instead of shredding it.
Embedding & Vector Store Setup
We set up the embedding pipeline and vector database, with a re-indexing process for content that changes over time.
Retrieval + Generation Pipeline Build
We build the retrieval and generation pipeline, tuned for your query patterns rather than a generic off-the-shelf template.
Evaluation & Accuracy Tuning
We build an evaluation harness against real questions and tune retrieval and prompting until accuracy holds up under real usage.
Real Tools, Not a Buzzword List
Common Questions
Quick answers about our rag pipelines services.
File upload tools aren't built for large, changing document sets, don't give you control over chunking/retrieval quality, and don't integrate into your product or internal tools — a custom pipeline does all three, and you own it.
We build a re-indexing process tied to how your content actually changes — scheduled, event-triggered, or manual, depending on your source systems.
Yes — the pipeline can be deployed within your own cloud environment or a compliant hosting setup when data residency or security requirements demand it.
We build an evaluation set from real questions your team actually asks and score retrieval relevance and answer correctness against it before and after tuning.
How We Handle Your Data
Your Data Stays Yours
Your data, conversations, and documents are used only to build and run the system you're paying for - never to train third-party models, and never shared outside your project.
You Own What We Build
The code, prompts, and configurations built for your engagement belong to you, not licensed back to you after the contract ends.
Documented Upfront, Not Boilerplate
Hosting region, data retention windows, and who has access are scoped to your specific engagement and documented during discovery, not left as generic fine print.
RAG Pipelines Packages
Starting points for a rag pipelines engagement. Every project is scoped precisely after a discovery call.
Starter
A focused, single-scope rag pipelines build
starting at, one-time project
- PDF / DOCX / TXT ( upto 20 pages )
- Website ingestion ( 5 pages Website )
- Citation / source references
- Hallucination controls
- Modern Evalution Framework ( Ragas etc )
- Clean UI/UX
- 3 Revision
- Build, testing, and launch
- 30 days support
Growth
RAG Pipelines plus ongoing iteration and support
starting at , one-time project
- PDF / DOCX / TXT ( upto 50 pages )
- Website ingestion ( 10 pages Website )
- Citation / source references
- Hallucination controls
- Modern Evalution Framework ( Ragas etc )
- Clean UI/UX
- 5 Revision
- Build, testing, and launch
- 30 days support
Enterprise
Complex, multi-system builds and dedicated teams
scoped after discovery
- PDF / DOCX / TXT ( upto 100+ pages )
- Website ingestion ( Multiple Website )
- Citation / source references
- Hallucination controls
- Modern Evalution Framework ( Ragas etc )
- Clean UI/UX
- 7 Revision
- Build, testing, and launch
- 90 days support
Illustrative starting points, not fixed quotes. Final pricing depends on the specific integration complexity and timeline.
Related Services
Learn More About RAG Pipelines
RAG vs. Fine-Tuning: Which Should You Use?
What RAG and fine-tuning each actually do, when to use one over the other, and why combining both is sometimes the right architecture.
Read the articleWhat Is RAG? A Practical Guide to Retrieval-Augmented Generation
Retrieval-augmented generation grounds AI answers in your real documents instead of a model's training data. Here's how it actually works and when you need it.
Read the articleReady to talk about rag pipelines?
Tell us what you're trying to build - we'll scope it and get back to you.