# Unblocked Context Engine vs. building it yourself
URL: https://getunblocked.com/benchmarks/context/
Unblocked scores 88.5 on EnterpriseRAG-Bench, Onyx's RAG benchmark of company data: 83% higher than Azure AI Search, the leading build-your-own tool, across 511,962 documents.
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Unblocked beats build-your-own options in correctness, completeness, recall and overall score.
**83%** higher score than the leading build-your-own tool. Overall score on EnterpriseRAG-Bench:
- Unblocked: 88.5
- Azure AI Search: 48.4
- Vertex AI Search: 41.9
- LlamaIndex: 27.2
- LangChain: 25.0
## Build it yourself?
We tested Unblocked on 500 questions from [EnterpriseRAG-Bench](https://github.com/onyx-dot-app/EnterpriseRAG-Bench). Each question requires company knowledge from docs, conversations, tickets, or meetings, not just the code.
| System | Overall | Correctness | Completeness | Recall | Extra docs |
| --- | --- | --- | --- | --- | --- |
| Unblocked | 88.5 | 91.2 | 93.2 | 94.7 | 0.5 |
| Azure AI Search | 48.4 | 56.4 | 57.6 | 64.3 | 3.3 |
| Vertex AI Search | 41.9 | 49.2 | 55.5 | 61.8 | 4.0 |
| LlamaIndex | 27.2 | 32.4 | 37.8 | 30.6 | 1.5 |
| LangChain | 25.0 | 31.0 | 35.6 | 36.4 | 3.1 |
Overall = correctness × completeness, averaged per question. Extra docs = irrelevant documents cited per answer, lower is better.
## Inside the dataset
500 questions in 10 categories over 511,962 documents from nine enterprise tools. Off-topic threads, half-finished drafts and near-duplicate pages are mixed in on purpose, the clutter a real retrieval system has to ignore.
Documents per source, and how often Unblocked cited the gold documents from each.
| Source | Documents | Our recall |
| --- | --- | --- |
| Slack | ~275K | 88.9% |
| Gmail | ~120K | 91.7% |
| Linear | ~35K | 96.8% |
| Google Drive | ~25K | 90.6% |
| HubSpot | ~15K | 97.1% |
| Fireflies | ~10K | 80.4% |
| GitHub | ~8K | 91.7% |
| Jira | ~6K | 91.7% |
| Confluence | ~5K | 90.8% |
## Methodology
- **How the data was built**: Onyx scaffolded a synthetic AI-inference company, generated documents across nine tools with shared projects, people and codenames, then added noise: misfiled files, stale facts and near-duplicates with conflicting details.
- **How it's scored**: LLM judges grade each answer for correctness and completeness against the gold answer.
Benchmark: Sun et al., "[EnterpriseRAG-Bench: A RAG Benchmark for Company Internal Knowledge](https://arxiv.org/abs/2605.05253)," arXiv:2605.05253, MIT-licensed. All scores, including Unblocked's, are from Onyx's [published leaderboard](https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard), all 500 questions, Nov 3, 2026.
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