RAG Development Services
Properly implementing Retrieval-Augmented Generation involves using ingestion pipelines, fine-tuning the retrieval process, ensuring that answers are supported by citations and have been verified through evaluation frameworks—all of this being deployed within your own cloud tenant.
When this is the right answer.
If there is no retrieval grounding, the models will have to improvise. In an enterprise environment, improvisation is a disadvantage.
Real corpora have tables, versions, duplicates, and permissions. Chunk-and-pray retrieval falls apart in practice.
If there is no evaluation framework, then any discussion of quality amounts to nothing but personal accounts, and regressions are only noticed by users.
What we build.
Retrieval pipeline engineering
The chunking strategies, the embeddings, the hybrid search, and the re-ranking have all been optimized on your actual corpus since retrieval quality is the key factor in determining whether RAG succeeds or fails.
Ingestion at scale
The system includes connectors for wikis, drives, tickets, and databases; it also features parsing capabilities for PDFs, tables, and scans; versioning and freshness are dealt with as part of the design.
Grounded answers with citations
All answers can be traced back to their sources, and a refusal is displayed when the corpus does not include the question.
Permission-aware retrieval
The answers take your access controls into account — in testing it has been verified that users only see what they are permitted to see.
Evaluation frameworks
Groundedness, correctness, and completeness are scored on a test set built from your real questions — and every release must pass it.
Own-tenant deployment
The vector store, pipeline, and models run inside your own GCP or AWS tenant. Your documents never leave your perimeter.
Where this earns its keep.
Every engagement runs spec-first, agent-accelerated, human-owned.
A written specification that has been approved by the client prior to any coding taking place — covering the architecture, the edge cases, and the acceptance criteria.
The engineers arrange for the AI agents to carry out the specification in parallel, taking weeks rather than quarters.
The evaluations, tests, and security checks are carried out together with the build, and humans examine every decision that is important.
The system has been deployed to your GCP or AWS, complete with monitoring, production evaluations, and retraining loops.
RAG Development is one discipline within our Generative AI & RAG practice.
Explore the parent service for the full capability set, or go straight to a conversation about your use case.
Questions, answered.
How is this different from just using an LLM with a big context window?
Which vector database will you use?
How do you keep answers current as documents change?
RAG or fine-tuning?
How do you measure whether the answers are actually right?
Have an AI idea? Let’s pressure-test it — free.
A 30-minute call with our engineers, not a salesperson. You'll receive an honest assessment of feasibility, a preliminary architecture, and a clear next step.