RAG & LLM Development
Knowledge applications that retrieve approved information and evaluate model responses against the work they need to support.
Use cases worth assessing
- 01
Knowledge assistants over approved documents and internal sources.
- 02
Search and answer workflows with traceable supporting material.
- 03
LLM features that need controlled context and structured outputs.
What the engineering scope includes
- 01
A review of source quality, access rules, and retrieval requirements.
- 02
Ingestion, indexing, retrieval, and answer-generation components.
- 03
Grounding, citation and fallback behaviour where appropriate.
- 04
An evaluation set, operational visibility, and update procedures.
Build, evaluate, deploy, improve.
Each phase answers a different risk: business fit, data readiness, system design, AI behaviour, and operational reliability.
Discuss your AI projectDiscover
Define the users, workflow, data, constraints, and what success should mean.
Architect
Plan the application, model integrations, data access, permissions, and evaluation approach.
Prototype
Test a focused workflow with representative inputs before committing to a full build.
Build
Implement the product, integrations, interfaces, and observable AI workflow.
Evaluate
Check task quality, failure cases, access controls, latency, and running cost.
Deploy & improve
Release with monitoring, operational documentation, and a plan for iteration.
Questions about this service
RAG & LLM Development
What is retrieval-augmented generation?
RAG retrieves relevant information from a defined knowledge source and supplies it as context to a model. It is an application architecture, not a guarantee that every answer will be correct.
Can RAG eliminate hallucinations?
No system should promise that. Retrieval quality, source quality, model behaviour, and evaluation all matter. We can design grounding, source references, abstention, and human escalation for the workflow.
RAG or fine-tuning: which is better?
They solve different problems. Retrieval often suits changing factual knowledge, while fine-tuning may suit specialised behaviour or output patterns. We assess data, rights, cost, and measured benefit before recommending either.
How do you keep knowledge current?
We plan source updates, ingestion, access changes, indexing, and monitoring. Update frequency depends on the source systems and the business workflow.
Can users see only the information they are allowed to access?
Access controls should be applied across ingestion, retrieval, and application layers. We assess the identity and permission model instead of assuming a vector database alone enforces it.
Related AI services
- 01
AI Agent Development
Tool-using AI agents for bounded tasks, approved data sources, and multi-step workflows.
- 02
AI Integration Services
Add LLM, retrieval, and assistant capabilities to the software and workflows your team already uses.
- 03
AI Workflow Automation
Connect business tools and automate repetitive operational work with visible decisions and human checkpoints.
Discuss your AI project.
Share the workflow, product, or operational problem you want to improve.
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