RAG Development Services For Modern B2B Products
We design and build retrieval systems that ground your AI in real data, cutting hallucinations that generic models simply cannot avoid.
RAG Development Use Cases
Accuracy, trust, and grounded answers your users can rely on.
Grounding AI Features Beyond a Prototype
Helping founders build retrieval that holds up with real users and real production data, not just a clean early product demo.
Teams Modernizing Scattered Knowledge Systems
Turning outdated wikis, file shares, and disconnected docs into retrieval-ready knowledge your AI can actually search and use.
Product Teams Reducing AI Hallucinations
Grounding AI answers in your real product data to increase user trust, reduce support escalations, and improve adoption over time.
Data-Heavy Platforms Needing Grounded Search
Structuring complex data and documents so retrieval surfaces the right answer for users, not just the most similar-looking result.
Teams Preparing to Launch an AI Feature
Making sure a new AI feature is accurate and trustworthy before it reaches real customers, investors, or a public product launch.
RAG Development Challenges
Search that returns irrelevant or outdated results.
Hallucinations caused by weak or missing retrieval.
No repeatable way to evaluate retrieval accuracy.
Chunking that breaks context and confuses answers.
Vector and embedding costs are scaling out of control.
RAG Pipeline Development Services
Everything needed to take retrieval from prototype to production.
Document Ingestion & Chunking
Structuring raw documents into chunks that preserve meaning and full context.
RAG Evaluation & Accuracy Testing
Building test sets that measure retrieval quality, not just output fluency.
Embedding & Retrieval Design
Selecting and tuning embedding models and the retrieval strategy for your domain.
Guardrails & Source Attribution
Ensuring every answer is directly traceable to a real, citable source document.
Vector Database Architecture
Choosing and configuring the right vector store for scale, cost, and speed.
Deployment & Monitoring
Shipping with observability into retrieval quality and answer accuracy live.
Our Custom AI Solutions
Dive into Our Success Stories
Legal
Conversational & Custom AI Development for Smart Document Creation
A Case Study on transforming document creation for legal & business through Conversational AI.
SaaS
AI-Enhanced CRM for Hospital Operations
A Case Study on transforming a CRM platform through AI-enhanced development.
Personal Finance
AI-Powered Personal Finance Management App
A Case Study on developing an AI-powered personal finance management app.
Trip Planner
Revolutionizing Trip Planning with AI-Powered Travel Applications
A Case Study on revolutionising trip planning through AI-powered development.
- Artists
- Strategists
- Innovators
Why Choose Code Theorem for Custom RAG
Retrieval engineering is the biggest underestimate for most teams until it's live.
We treat retrieval as its own engineering discipline, not an afterthought bolted onto a model. Chunking, embeddings, and ranking are all tuned to your actual documents, not a generic default.
We have spent 8+ years building production data systems for SaaS and enterprise teams, and we apply that same rigor to every retrieval pipeline we design, evaluate, and ship.
Industry Expertise
Retrieval work shaped by the rules of your industry.
Our RAG Development Process
A build path tuned to your documents, not a demo.
01
Discovery & Data Audit
02
Chunking & Embedding Strategy
03
Retrieval Pipeline Build
04
Evaluation & Tuning
05
Deployment & Monitoring
Boost Efficiency with Rag Development Services
Vraj Trivedi
CEO
Prem Parmar
Design Chief
What Our Clients
Say About Us
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Your Questions, Answered!
FAQs
What Is RAG (Retrieval-Augmented Generation)?
RAG is a technique that connects a language model directly to your own documents and data at answer time, retrieving relevant content first so the model answers from real, current sources instead of relying only on patterns learned during training.
How Is RAG Different From Fine-Tuning?
Fine-tuning changes a model's internal weights using your data, which is costly to update. RAG keeps the model unchanged and retrieves fresh information at query time, so updating your knowledge base doesn't require retraining anything at all, ever.
What Does RAG Development Actually Cost?
Cost depends on document volume, how many sources need connecting, and how much evaluation and tuning your accuracy requirements demand. We scope every engagement individually and offer fixed-cost, subscription, or dedicated-team models to match your needs.
How Do You Evaluate RAG Accuracy And Quality?
We build a test set of real queries with known correct answers, then measure retrieval precision and generated answer accuracy against it before launch — and keep monitoring both continuously once the system reaches real users in production environments.
