Providers who deliver generative ai & rag.

Generative AI and RAG is a class of system, not a product: a language model wired to a retrieval layer that grounds responses in a specific corpus, usually proprietary documents, tickets, code, or transactional records. The character of a build is set by how the retrieval pipeline (chunking, embedding, indexing, reranking, permissioning) meets the generation step, and by what the team measures before shipping.

When evaluating RAG providers, ask for the retrieval evaluation harness they run against your corpus rather than their benchmark. Ask how they build an eval set for your domain before touching a model, and how document-level access controls propagate so the model cannot answer across permission boundaries. Push on which parts of an LLM implementation they own end to end, chunking strategy, embedding choice, reranker, guardrails, observability, model-swap plan, versus which they only glue together.

Generative AI development as a category is dominated by demos that hide retrieval quality behind fluent output; a chatbot that answers well on a curated deck often collapses on a full corpus. Search public claims here with skepticism. Common anti-patterns: skipping evaluation entirely, shipping a single vector-search pass and calling it RAG, and promising agentic autonomy without the ground-truth pipeline to make it safe.

Trustgent grades providers on the L0 to L5 spectrum documented at /how-we-verify. For this capability that means moving from self-claim toward evidence, published architectures, disclosed eval methodology, referenceable deployments, artifact review. Where a provider sits reflects what is verifiable today, not what they paid for.