LLM & RAG Development Services
LLM and RAG development services for enterprise applications. Implement retrieval, integrations, access controls and evaluation using your accepted answer criteria.
RAG systems that give confident wrong answers are worse than no AI at all.
Chunking that destroys context
Documents split at arbitrary boundaries. Paragraphs cut in half. Tables separated from their headers. The retrieval system returns fragments that look relevant but miss the information the model actually needs to answer correctly.
Default embedding models that don't fit the domain
Embedding models chosen because they shipped with the framework, not because they match the content. Legal documents, medical records, and code repositories have different semantic structures. A general-purpose model misses domain-specific relationships.
No separation of retrieval quality from generation quality
The system gives a wrong answer. Nobody knows if it retrieved the wrong documents or retrieved the right documents and reasoned badly. Without measuring these independently, debugging is guesswork.
No strategy for out-of-scope queries
Users ask questions the knowledge base cannot answer. The system generates a plausible response anyway. No guardrails to detect when the model is operating outside its reliable zone. No fallback path when retrieval confidence is low.
We design the full RAG pipeline. Each component chosen for your specific domain and content type.
Document processing, chunking strategy, embedding model selection, vector store architecture, retrieval configuration, reranking, prompt engineering, and the generation layer. Built with evaluation from the start and feedback loops that improve the system over time.
Design the retrieval architecture
Chunking strategies matched to your content structure. Embedding models evaluated against your domain. Vector store architecture sized for your scale. Reranking configured to surface the most relevant results, not just the closest vectors.
Build evaluation into the system
Retrieval recall measured separately from answer correctness. Hallucination rate tracked independently. When the system degrades, you know it. When something breaks, you know exactly where.
Close the feedback loop
User signals captured and routed to review queues. Dataset improvement built into the operational workflow. A RAG system that doesn't improve over time isn't a product asset. It's a maintenance burden.
Every piece of the pipeline is chosen for your domain. Not defaults. Not what worked for someone else.
A retrieval system that finds the right information. A generation layer that uses it correctly.
Measurable quality you can track. A system that improves as your knowledge base grows. Not a prototype that worked once in a demo.
Production infrastructure with guardrails, monitoring, and cost controls from day one.
Production RAG pipeline
Document ingestion, chunking, embedding, retrieval, reranking, and generation. Each layer tuned, tested, and monitored. Deployed on your infrastructure with your security requirements.
Evaluation framework
Retrieval recall, answer correctness, and hallucination rate measured independently. Dashboards that show system health. Alerts when quality drops below thresholds you define.
Continuous improvement system
Feedback loops that capture user signals, route corrections to review queues, and improve the dataset over time. The system gets better with use, not worse.
Results
Cited
Answers retain source references, access decisions, retrieval context, and the evidence used for evaluation.
Measured
Retrieval coverage and generation quality are evaluated separately so failures can be diagnosed.
Operated
Corpus change, index freshness, access rules, quality drift, latency, and cost have named owners and alerts.
Acceptance evidence
Acceptance covers retrieval evaluation, grounded-answer quality, access enforcement, citation behavior, corpus freshness, failure handling, latency, cost, and operational ownership.
Related services
AI Readiness & Feasibility
Not sure if RAG is the right architecture for your problem? Start here. A clear-eyed evaluation of what AI can do for your business.
Agentic Workflows
When retrieval isn't enough and the system needs to plan, decide, and act. Agents built with failure handling and human escalation paths.
AI Product Integration
Embedding AI capabilities into your existing product. The model is one piece. The integration into workflows, UX, and infrastructure is the rest.
Frequently asked questions
What is retrieval augmented generation (RAG)?
RAG combines a large language model with search over your own content, so answers are grounded in relevant, up-to-date sources.
When should we use RAG instead of fine-tuning?
RAG suits knowledge that changes or needs citations. Fine-tuning suits changing a model's style or behaviour. Many systems use RAG first.
How do you improve RAG accuracy?
By improving document preparation, chunking, retrieval and ranking, prompt design and evaluation against real questions.
Can LLMs be integrated with our existing applications?
Yes. LLM integration can add search, summarisation, drafting or assistants to existing products and internal tools through APIs.
How do you protect sensitive data in an LLM application?
With access controls on retrieved content, data minimisation, approved hosting options, logging policies and guardrails on inputs and outputs.