Trusted answers for the work your team does every day

BlackIQ brings connected knowledge, source grounding, and enterprise control together so teams can move faster without guessing.

Teams use BlackIQ to answer recurring internal questions faster and with better source grounding. The biggest win is less time spent hunting across tools.

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

Benchmarks

BlackIQ Wins Head-to-Head Against Every Major Competitor

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

Head-to-head win rates

vs ChatGPT64% win rate
BlackIQ64%
ChatGPT36%
vs Claude68.1% win rate
BlackIQ68.1%
Claude31.9%
vs Notion AI76% win rate
BlackIQ76%
Notion AI24%

About this benchmark

99 real workplace questions. 220K documents from Slack, Google Drive, GitHub, Gmail, and more. BlackIQ 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
BlackIQ
36.2s
Claude
45.4s
ChatGPT
46.7s
Notion

See the difference for yourself

Start with BlackIQ Cloud or book a guided demo.

Under the hood

Why BlackIQ finds what others miss

Most enterprise AI tools run a single search and hope for the best. BlackIQ 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. BlackIQ 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 BlackIQ, the better it gets.

Talk to us about the retrieval walkthrough ->

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Today

Start with the BlackIQ workspace your team can use right away, then expand into more workflows, controls, and rollout paths as needs grow.

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Secure

Deployment, access, and security expectations are reviewed up front so the workspace fits the environment your team actually operates.

Flexible Rollout

Start with the workflows that matter now, then expand connectors, controls, and integrations over time.

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Engineering
Operations
Support
Sales
Success
IT
Product
RevOps
Research
Leadership
Security
Compliance
Finance
Enablement