Data, RAG & Knowledge Intelligence
Build retrieval-augmented generation systems that connect approved knowledge to LLM applications with measurable retrieval quality and source traceability.
Buyer Intent
For RAG Development, source quality and freshness is usually the first thing that determines scope, before any question about which model or vendor to use.
For RAG Development, the useful starting point is a concrete workflow, decision or system boundary. PlanckCyber defines the outcome, current baseline, dependencies and controls before deciding whether AI, standard software or a combination is the right implementation.
Common Questions
Cost follows the number and variety of source systems being connected for retrieval, not the generation model itself. A single, well-maintained knowledge base is inexpensive to build retrieval around; multiple sources with different formats, update schedules and permission rules require real engineering to keep retrieval accurate and current. Retrieval evaluation, built from real queries, is the other major cost driver, since it is what actually determines whether the system is ready.
A company built for RAG development treats retrieval evaluation and source traceability as the core engineering work, not an add-on after the generation piece is built. Ask a candidate how they would catch a fluent, confident answer built on the wrong retrieved context. A firm without a specific retrieval-evaluation approach is describing a demo, not a production-ready system.
Decision Questions
Look for a partner who treats retrieval quality as something to be measured, not assumed — ask how they evaluate whether the right passages are retrieved for a given query, not just whether the final answer reads well. A confident-sounding answer built on the wrong retrieved context is a common and hard-to-spot failure mode in RAG systems.
Compare source traceability across candidates: can the system show which document or passage an answer came from, and can a reviewer verify it? Ask how each partner handles source updates — stale content silently returning outdated answers is one of the more common and least visible RAG failures, and a partner should have a concrete process for content freshness.
A RAG development engagement should define the source material and its update frequency, build a retrieval evaluation process using real queries rather than hand-picked examples, and include citation or source traceability in the final answer so it can be verified. It should also define what the system does when relevant source material genuinely does not exist, rather than generating a plausible-sounding answer anyway.
The biggest risk is a system that produces fluent, confident answers built on poor or irrelevant retrieved context, which is harder to catch than an obvious error because the output reads correctly. A second risk is no defined behavior for missing information — the system should say it does not know rather than filling the gap with a plausible guess.
Implementation Factors
Practical Delivery
RAG development treats retrieval as the part that determines answer quality — the generation step can only work with what it is given. Retrieval is evaluated against real queries, not a handful of favorable examples, before the system is judged ready.
Answers carry traceability back to their source so a reviewer can verify them, and the system is explicitly designed to say it does not know when relevant source material is missing, rather than generating a plausible-sounding answer without grounding.
Related Expertise
Start with the problem
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