Enterprise RAG Development
Build retrieval pipelines around your documents, user workflows and existing business systems.
RETRIEVAL-AUGMENTED GENERATIONRAG Development
RAG Development
Services
Connect your business knowledge to AI with custom RAG applications, enterprise search and source-linked answers.
OUR SERVICES
RAG development services
From knowledge preparation to application delivery, built around your business.
RAG Chatbot Development
Create assistants that search your content and present answers with source references.
AI Knowledge Base Development
Prepare, structure and index content from selected documents, websites and connected repositories.
Vector Search Development
Combine semantic retrieval, keyword search and reranking around your content and query patterns.
LLM Integration
Connect retrieval to model providers, response workflows and the applications your teams already use.
RAG Evaluation & Optimization
Measure retrieval relevance, answer grounding, latency and cost using representative questions and documented checks.
OUR PROCESS
From knowledge to application
A practical RAG development process, shaped around
your data and users.
Define the use case
Map users, content sources, access requirements and measurable evaluation goals.
Prepare & retrieve
Design connectors, chunking and retrieval using a representative sample of your knowledge.
Build & evaluate
Integrate the application and assess relevance, grounding, answer quality and failure cases.
Deploy & improve
Monitor queries, source updates, latency and usage costs as your system evolves.
CORE CAPABILITIES
Designed around your data
The foundations of a useful enterprise RAG application.
Content ingestion & refresh
Clean and chunk selected content, create embeddings where useful and plan scheduled refreshes.
Hybrid search & reranking
Combine keyword and vector search, then evaluate reranking against real user questions.
Source references & fallback
Link responses to source passages and define fallback behaviour when supporting evidence is insufficient.
Access control integration
Map source permissions, apply identity-based retrieval filters and test access as content changes.
USE CASESRAG applications for
RAG applications for
everyday work
Put relevant business knowledge within reach of customers
and teams.
Internal Knowledge Search
Help teams find project notes, onboarding materials and process documentation across selected internal sources.
Product Support Assistants
Answer product questions from help articles, manuals and troubleshooting guides, with links to relevant sources.
Developer Documentation Search
Retrieve API references, setup instructions and code examples from your maintained technical documentation.
Product Discovery Assistants
Help shoppers compare products using catalogue descriptions and specifications, with references to relevant product pages.
WORK WITH TENSORBLUE
Hire RAG Developers
Add RAG expertise to your team or build a complete application with Tensorblue.
Dedicated developer
Work with a RAG developer alongside your product and engineering teams.
Delivery team
Bring retrieval, backend and application development together for a defined project.
RAG audit & optimization
Review an existing system, identify retrieval gaps and prioritize measurable improvements.
START YOUR RAG PROJECTBuild AI around
Build AI around
your business knowledge.
Tell us about your content, users and goals.
We’ll help define the right RAG development scope.
COMMON QUESTIONS
RAG development FAQs
What is the difference between RAG and fine-tuning?
RAG retrieves relevant source content at query time; fine-tuning adapts model behaviour through training. The approaches can be combined when requirements justify both.
Can you connect our private data and systems?
We connect approved documents, knowledge bases and business systems, designing ingestion, refresh schedules and access checks around your data permissions and requirements.
How do you assess answer accuracy?
We evaluate retrieval, grounding and answer quality with representative questions. RAG can still produce errors, and citations require review against the source.
How much does RAG development cost?
Data volume, connectors, access rules and evaluation needs shape development costs; model usage, retrieval infrastructure and ongoing updates affect operating costs.
WORK WITH TENSORBLUE
Hire RAG Developers
Add RAG expertise to your team or build a complete application with Tensorblue.
Dedicated developer
Work with a RAG developer alongside your product and engineering teams.
Delivery team
Bring retrieval, backend and application development together for a defined project.
RAG audit & optimization
Review an existing system, identify retrieval gaps and prioritize measurable improvements.