What is RAG and when should I use it instead of fine-tuning?

RAG (retrieval-augmented generation) retrieves relevant content from your knowledge base at query time and passes it to the model as context. Fine-tuning bakes patterns into the model weights during training. Use RAG when your knowledge base is large, changes frequently, or when you need the AI to cite sources -- RAG handles all three well. Use fine-tuning when you need the model to learn a specific output style, tone, or task format, not factual knowledge. Most enterprise document Q&A, internal knowledge base, and customer support use cases are better served by RAG than fine-tuning.

How do you measure RAG pipeline quality?

We build evaluation datasets from representative queries -- the types of questions real users will ask -- paired with expected answers and the source documents those answers should come from. We measure retrieval recall (does the right document chunk appear in the top results), context precision (is the retrieved context relevant rather than noisy), and answer faithfulness (does the final answer stick to what was retrieved). We use LLM-as-judge for qualitative evaluation and run regression tests when chunking strategies, embedding models, or prompts change. You get a quality dashboard rather than a subjective sense that it seems to work.

What vector databases do you use?

We work with Pinecone (managed, fast, simple to operate), Weaviate (self-hosted or cloud, multi-tenancy support), pgvector (Postgres extension, right for teams already on Postgres who want fewer infrastructure components), and Qdrant (high-performance, good for large-scale retrieval). Database selection is driven by your data volume, query latency requirements, multi-tenancy needs, and existing infrastructure. We don't have a preferred vendor -- we use what's right for your context and document the trade-offs before you decide.

What does RAG pipeline development cost?

A focused RAG pipeline -- single document type, one vector database, hybrid search, and basic evaluation -- typically runs $20,000--$60,000. Complex enterprise RAG systems with multiple document sources, multi-tenant architecture, advanced re-ranking, and full evaluation infrastructure run $60,000--$150,000. Cost depends on document volume and variety, retrieval complexity, integration requirements, and evaluation depth. We scope before pricing and deliver a fixed-cost proposal.

What is RAG and when should I use it instead of fine-tuning?

RAG (retrieval-augmented generation) retrieves relevant content from your knowledge base at query time and passes it to the model as context. Fine-tuning bakes patterns into the model weights during training. Use RAG when your knowledge base is large, changes frequently, or when you need the AI to cite sources -- RAG handles all three well. Use fine-tuning when you need the model to learn a specific output style, tone, or task format, not factual knowledge. Most enterprise document Q&A, internal knowledge base, and customer support use cases are better served by RAG than fine-tuning.

How do you measure RAG pipeline quality?

We build evaluation datasets from representative queries -- the types of questions real users will ask -- paired with expected answers and the source documents those answers should come from. We measure retrieval recall (does the right document chunk appear in the top results), context precision (is the retrieved context relevant rather than noisy), and answer faithfulness (does the final answer stick to what was retrieved). We use LLM-as-judge for qualitative evaluation and run regression tests when chunking strategies, embedding models, or prompts change. You get a quality dashboard rather than a subjective sense that it seems to work.

What vector databases do you use?

We work with Pinecone (managed, fast, simple to operate), Weaviate (self-hosted or cloud, multi-tenancy support), pgvector (Postgres extension, right for teams already on Postgres who want fewer infrastructure components), and Qdrant (high-performance, good for large-scale retrieval). Database selection is driven by your data volume, query latency requirements, multi-tenancy needs, and existing infrastructure. We don't have a preferred vendor -- we use what's right for your context and document the trade-offs before you decide.

What does RAG pipeline development cost?

A focused RAG pipeline -- single document type, one vector database, hybrid search, and basic evaluation -- typically runs $20,000--$60,000. Complex enterprise RAG systems with multiple document sources, multi-tenant architecture, advanced re-ranking, and full evaluation infrastructure run $60,000--$150,000. Cost depends on document volume and variety, retrieval complexity, integration requirements, and evaluation depth. We scope before pricing and deliver a fixed-cost proposal.

AI assistant giving confident but wrong answers because it can't access your actual documents?
Built a RAG prototype that works on simple queries but fails on real-world questions from your users?

RAG Pipeline Development

RAG -- retrieval-augmented generation -- grounds AI responses in your actual documents, databases, and knowledge rather than relying on what the model memorised during training. The result is an AI assistant that answers from your data, cites its sources, and doesn't make things up when it doesn't know.
We build production RAG pipelines covering the full stack: document ingestion and chunking, embedding and vector storage, hybrid search and re-ranking, context assembly, and the evaluation framework that tells you whether retrieval quality is actually good.

Full-stack RAG -- ingestion, embedding, search, re-ranking, and evaluation
Works with Pinecone, Weaviate, pgvector, Qdrant, and other vector databases
Hybrid search combining dense embeddings and keyword retrieval for better accuracy
Evaluation framework to measure and monitor retrieval quality in production

RAG (retrieval-augmented generation) is a technique that grounds AI responses in external documents or databases by retrieving relevant content at query time and including it in the model's context. It is the right choice when you need an AI system to answer questions from your existing knowledge base, documentation, or data without retraining the model. Fine-tuning is better suited to teaching the model a specific style or task format, not to injecting factual knowledge that changes over time.

RAG pipelines solve a specific problem: your organisation has valuable knowledge locked in documents, wikis, databases, and files, and a general AI model has no access to any of it. The model can only answer from what it learned during training -- which doesn't include your product documentation, your contracts, your policy manuals, or your support history.

Retrieval-augmented generation closes that gap. At query time, the system retrieves the most relevant content from your knowledge base and includes it in the model's context. The model answers from your data, not from its training. That is the difference between an AI assistant that is useful for your business and one that is a sophisticated autocomplete.

What we build

Document ingestion and chunking pipelines

Ingestion pipelines that process your documents at scale: PDFs, Word files, HTML pages, markdown, spreadsheets, and structured data. Chunking strategies tuned for your document types -- fixed-size, semantic, hierarchical, or document-structure-aware chunking. Metadata extraction and tagging at ingest to support filtered retrieval. Incremental update pipelines that re-index changed documents without re-processing your entire knowledge base.

Vector database setup and embedding

Vector database setup and configuration using Pinecone, Weaviate, pgvector, or Qdrant depending on your scale, infrastructure, and latency requirements. Embedding model selection and integration -- OpenAI embeddings, Cohere, or open-source models based on your performance and cost trade-offs. Multi-tenant index architecture for platforms serving multiple customers or departments. Index management and cost optimisation for large document collections.

Hybrid search and re-ranking

Hybrid search combining dense vector retrieval with BM25 keyword search -- the combination consistently outperforms either approach alone, particularly for queries that mix conceptual and specific terminology. Re-ranking using a cross-encoder model to improve the ordering of retrieved chunks before they enter the context window. Query expansion and transformation for queries that are ambiguous or too short to retrieve well with a single embedding.

RAG evaluation frameworks

Evaluation datasets representing your real query distribution, paired with expected answers and source documents. Automated scoring covering retrieval recall, context precision, and answer faithfulness. LLM-as-judge evaluation for qualitative output quality. Regression test suites that catch quality degradation when chunking strategies, embedding models, or prompts change. Production monitoring dashboards so you know if retrieval quality drifts as your document corpus evolves.

Knowledge base Q&A systems

Production Q&A systems for internal knowledge bases, documentation portals, customer support, and policy lookup. Answer generation with source citations so users can verify where answers come from. Conversation history and follow-up question handling. Confidence signals and fallback handling for queries outside your knowledge base scope. User feedback loops that feed back into evaluation and improvement cycles.

RAG for enterprise document retrieval

Enterprise-scale RAG for organisations with large, heterogeneous document collections: legal databases, compliance repositories, technical manuals, and multi-department knowledge bases. Role-based access control on retrieval -- users only retrieve from documents they are authorised to see. Audit logging for compliance use cases. Multi-tenant deployment for platforms that serve multiple business units or customers from the same infrastructure.

AI answering from your knowledge, not its training data?

We build RAG pipelines that retrieve accurately, stay current as your documents change, and include the evaluation infrastructure to prove they are working.

Talk about your RAG project

AI Development -- overview of all AI development capabilities
AI Agents -- AI agents using RAG for knowledge retrieval in multi-step workflows
Machine Learning -- ML models built alongside RAG for prediction and classification
Computer Vision -- computer vision for image and document analysis

Generative AI Development -- generative AI applications built on top of RAG pipelines
Vector Database Development -- vector database infrastructure for search and retrieval

Frequently asked questions

: RAG (retrieval-augmented generation) retrieves relevant content from your knowledge base at query time and passes it to the model as context. Fine-tuning bakes patterns into the model weights during training. Use RAG when your knowledge base is large, changes frequently, or when you need the AI to cite sources -- RAG handles all three well. Use fine-tuning when you need the model to learn a specific output style, tone, or task format, not factual knowledge. Most enterprise document Q&A, internal knowledge base, and customer support use cases are better served by RAG than fine-tuning.
: We build evaluation datasets from representative queries -- the types of questions real users will ask -- paired with expected answers and the source documents those answers should come from. We measure retrieval recall (does the right document chunk appear in the top results), context precision (is the retrieved context relevant rather than noisy), and answer faithfulness (does the final answer stick to what was retrieved). We use LLM-as-judge for qualitative evaluation and run regression tests when chunking strategies, embedding models, or prompts change. You get a quality dashboard rather than a subjective sense that it seems to work.
: We work with Pinecone (managed, fast, simple to operate), Weaviate (self-hosted or cloud, multi-tenancy support), pgvector (Postgres extension, right for teams already on Postgres who want fewer infrastructure components), and Qdrant (high-performance, good for large-scale retrieval). Database selection is driven by your data volume, query latency requirements, multi-tenancy needs, and existing infrastructure. We don't have a preferred vendor -- we use what's right for your context and document the trade-offs before you decide.
: A focused RAG pipeline -- single document type, one vector database, hybrid search, and basic evaluation -- typically runs $20,000--$60,000. Complex enterprise RAG systems with multiple document sources, multi-tenant architecture, advanced re-ranking, and full evaluation infrastructure run $60,000--$150,000. Cost depends on document volume and variety, retrieval complexity, integration requirements, and evaluation depth. We scope before pricing and deliver a fixed-cost proposal.