Enterprise RAG Platforms: What to Evaluate
Retrieval demos look similar across nearly every vendor. What actually differs—and what determines whether a platform survives contact with your real documents—shows up in five specific areas covered below.
The five checks that matter
Retrieval quality
- Hybrid search (keyword plus semantic), not vector-only
- Reranking to push the right chunk to the top
- Citations back to the source document
- Performance on your documents, not a demo dataset
Access and governance
- Per-document or per-source permissions
- Permissions that mirror your existing access rules
- Audit logs for what was retrieved and by whom
- Data residency and retention controls
Operability and exit
- Connectors for your actual data sources
- Evaluation tooling to track answer quality over time
- Exportable index and configuration
- Freedom to swap the underlying model
Test retrieval on your documents, not the vendor's demo
Nearly every RAG platform's demo looks good on clean, well-structured sample documents. Your actual document set—scanned PDFs, inconsistent formatting, tables buried in prose, duplicate versions across folders—is the real test. Before committing, build a small golden set: twenty to thirty real questions with known correct answers drawn from your own documents, and run them against the platform's retrieval pipeline.
Score two things separately: whether the retrieved chunks actually contain the answer, and whether the generated response cites them accurately. A platform that retrieves the right chunk but generates an answer that drifts from it has a different problem than one that retrieves the wrong chunk entirely—and they need different fixes.
Access control has to mirror reality, not simplify it
The single most common enterprise RAG failure isn't a bad answer—it's a correct answer sourced from a document the user should never have been able to see. Confirm the platform supports per-document or per-source permissions that map onto your existing access model (SharePoint permissions, folder-level ACLs, role-based access), not just a single index-wide toggle.
Ask specifically how permissions propagate when a document's access changes after it's already indexed, and whether there's an audit trail showing what was retrieved and by whom. Both matter more once you're past the pilot and into a rollout involving multiple departments with different access levels.
The vector database matters less than the pipeline around it
Vendors often lead with their vector database choice as a differentiator; in practice, several mature options perform comparably for most enterprise workloads. What matters more is how well the platform's whole retrieval pipeline is engineered around it: chunking strategy, hybrid search support, metadata filtering, and reranking. A well-built pipeline on a "good enough" vector store usually outperforms a poorly built one on the fastest vector store on the market.
Ask to see the chunking strategy specifically—fixed-size chunking often fragments tables and structured data in ways that hurt retrieval quality, while structure-aware chunking (by section, table, or heading) tends to hold up better on real enterprise documents.
Observability: you can't improve what you can't see
Once a RAG system is live, quality doesn't stay static—new documents get added, old ones go stale, and usage patterns surface edge cases the initial testing missed. Confirm the platform gives you visibility into which queries return low-confidence or no results, so you can identify content gaps before users notice them as wrong or missing answers.
Regression testing against your golden set after any pipeline change—a new chunking strategy, a model upgrade, a reranker swap—is what keeps quality from silently drifting. If a platform has no story for this, budget for building it yourself, because you will need it within the first few months of production use.
Plan your exit before you need one
Confirm you can export your indexed content and configuration in a usable format, that the platform doesn't require your source documents to live in a proprietary format only it understands, and that switching the underlying model doesn't require re-architecting your retrieval pipeline from scratch. None of this means you shouldn't buy a platform—see our broader build vs buy guide for that decision—it just means the lock-in questions belong in the evaluation, not discovered after a price increase or a feature deprecation forces the issue.
If retrieval and governance are solid but you're unsure whether to buy a platform outright or build the pipeline yourself, our RAG implementation checklist walks through what a from-scratch rollout actually requires, so you can compare it honestly against what a platform is offering to do for you.
RAG platform vs custom RAG
| Consideration | RAG platform | Custom RAG |
|---|---|---|
| Speed to pilot | Usually faster | Depends on scope |
| Connectors | Prebuilt for common sources | Built for your exact systems |
| Retrieval tuning | Limited to platform options | Full control of chunking, search and reranking |
| Permissions | Depends on the platform's model | Designed to mirror your access rules |
| Cost profile | Licence or usage fees | Build cost plus infrastructure you run |
| Portability | Tied to the platform | Depends on architecture choices |
Platforms suit standard document Q&A over common sources; custom RAG tends to fit when retrieval quality, permissions or integrations are unusual. Some teams start on a platform and move to custom as requirements sharpen.
Evaluation scorecard
Use the same criteria for every candidate, scored on your own content and questions:
| Criterion | What good looks like |
|---|---|
| Retrieval quality | Finds the right passages for your real questions, including acronyms and product codes |
| Answer quality | Grounded answers with accurate citations; declines when evidence is missing |
| Access control | Enforces document permissions at retrieval time, synced with source systems |
| Freshness | Updates and deletions reflected on a schedule you can control |
| Observability | Logs of queries, retrieved sources and failures you can analyze |
| Security and data handling | Clear answers on processing location, retention and training use |
| Total cost | Predictable at your expected volume, including indexing and model usage |
| Exit path | You can export content, indexes and configuration |
Running a structured proof of concept
- Pick a representative document set
- Write 50–100 real questions with expected answers
- Include users with different permissions
- Run the same test on each candidate
- Score retrieval, answers, permissions and cost
- Decide, then plan production
Keep the test set after the decision; it becomes your regression suite once the system is live. The enterprise RAG implementation checklist covers what to check before going to production.
Next steps
When comparing RAG platforms, test with your own documents and questions, verify how permissions are enforced at retrieval time, and compare total cost over the life of the system rather than the entry price. A short, structured proof of concept with two or three candidates will tell you more than any feature matrix.
Comparing RAG platforms or considering a custom build?
Srishti GenAI can help you define evaluation criteria, run a structured comparison on your own content, and decide between a platform and a custom RAG system.