Search Infrastructure

Pinpoint accuracy
with Hybrid Search.

Combining keyword matching, full-text search (BM25), vector similarity, and recency ranking. Surface the most relevant results across your entire engineering knowledge base instantly.

Experience the speed
"how to configure hybrid search with reciprocal rank fusion"
41ms
RRF Fusion Engine
PR #401: Implement Hybrid Search core
Adds the core RRF merging logic to combine BM25 text scores and vector similarity cosine distances...
Architecture: Search Pipeline
Overview of the 6 data sources queried simultaneously by the hybrid engine...

How it works. Most competitors use 1-2 search methods. We query your entire stack simultaneously using a tripartite approach.

Full-Text Search (BM25)

Perfect for exact matches. If you search for an exact error code like "ERR_TCP_404", BM25 ensures it surfaces immediately.

Vector Similarity

Understands the semantic meaning of your query. If you search "how to reset passwords", it finds docs that say "authentication recovery".

Reciprocal Rank Fusion (RRF)

The magic layer. RRF mathematically blends the scores from both BM25 and Vector models to give you the most accurate result possible.

6 data sources in one search

Code, tasks, meetings, commits, Slack messages, and external docs — all indexed and searchable in milliseconds.

Explore all integrations
Fenlo

Strictly permission-aware

Every single query strictly respects Google Drive, Confluence, and Notion access controls. You never see what you aren't authorized to see.

Read our security policy

Questions and answers

Common questions about Fenlo Hybrid Search.

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instant answers?

See how Fenlo connects your tools and turns scattered knowledge into a single, searchable intelligence layer.

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