The Best Answers in Enterprise AI

Your team asks questions all day. The wrong answer wastes hours, the right one closes deals.

Onyx is answering thousands of questions a week at Ramp. We tried a variety of other AI tools but none had the same answer reliability as Onyx. It's been a huge productivity boost as we continue to scale.

Tony Rios, Director of Product Ops at Ramp

Read the Ramp case study →
30x

ROI measured by Ramp

1000+

questions answered / week

~30min

saved per user per day

Trusted by

top teams

Case study
Case study

Benchmarks

Onyx Wins Head-to-Head Against Every Major Competitor

99 real workplace questions. 220K internal documents. Onyx beat ChatGPT Enterprise, Claude Enterprise, and Notion AI in every matchup.

Head-to-head win rates

vs ChatGPT64% win rate
Onyx64%
ChatGPT36%
vs Claude68.1% win rate
Onyx68.1%
Claude31.9%
vs Notion AI76% win rate
Onyx76%
Notion AI24%

About this benchmark

99 real workplace questions. 220K documents from Slack, Google Drive, GitHub, Gmail, and more. Onyx vs. ChatGPT Enterprise, Claude Enterprise, and Notion AI, scored blind by two independent LLM judges.

Methodology

  • 99 questions spanning fact lookup, synthesis, and multi-hop reasoning

  • Blind evaluation by GPT-5.2 and Claude Opus 4.5

  • 220K documents indexed across 6 enterprise tools

Read the full benchmark →

Time to answer

34.7s
Onyx
36.2s
Claude
45.4s
ChatGPT
46.7s
Notion

See the difference for yourself

Start a 14-day free trial. No credit card required.

Try Onyx Free

Under the hood

Why Onyx finds what others miss

Most enterprise AI tools run a single search and hope for the best. Onyx runs a 6-stage retrieval pipeline that filters noise before the LLM ever sees it.

1LLM
Query GenerationLLM generates multiple parallel queries: a semantic rephrasing, keyword-heavy variants, and broad searches. Multi-part questions are split automatically.
2
Search & RecombinationEach query hits the hybrid search index (vector + BM-25). Results are combined via weighted Reciprocal Rank Fusion and adjacent chunks are merged for continuous context.
3LLM
LLM SelectionThe LLM reviews all retrieved chunks across documents and selects the most promising results. Reduces noise and downstream hallucination risk.
4LLM
Context ExpansionFor each selected document, the LLM reads surrounding chunks to decide how much context it needs. Runs in parallel per document for reliability.
5
Prompt BuildingSelected and expanded document sections are assembled into a structured prompt with citations, chat history, and keyword-matched references.
6LLM
Answer SynthesisThe LLM generates a grounded answer with inline citations linking back to source documents.

What this means in practice

  • 343ms

    median retrieval across 6.8M chunks. The full pipeline adds less than a second, even on CPU.

  • 23%

    recall improvement from adaptive query classification. Onyx detects whether your question needs keyword or semantic search and adjusts automatically.

  • Steps 3-4

    are the biggest drivers of accuracy. The LLM selects the best chunks, then expands context per document in parallel. Most RAG systems skip both.

It gets smarter over time

User upvotes, admin boosts, and time decay continuously refine ranking. The more your team uses Onyx, the better it gets.

Read the full technical deep-dive →

Get Started

Today

Start your 14-day free trial of Onyx (no credit card required)

Try Onyx Cloud

Secure

Enterprise-grade security and compliance. Flexible and secure deployment options.

Open-Source

Deep customizability optimized for your use case, supported by a large Open-Source community.

Stars

17k

Docs

Trusted by

top teams

Case study
Case study