When it comes to AI-powered product discovery, vector search is the usual suspect. It’s powerful, and it’s great at understanding semantic meaning. But at Alfred, we recognized a critical truth: relying on vector search alone leaves crucial insights buried. To truly understand customer feedback and drive your product forward, you need a more sophisticated approach: hybrid search.

The Blurry Picture: Limitations of Pure Vector Search

Vector search excels at finding content that "feels" similar. For example, if a customer says, “It took forever to pay,” vector search is smart enough to match that with feedback like, “The checkout flow was slow,” even without shared keywords. This is a huge leap forward for understanding user sentiment.

However, for product managers and founders who need actionable data, vector search alone has significant blind spots:

• Misses a Bullseye: Need to find every mention of a specific feature flag, SKU, or error code? Vector search often struggles with precise, literal matches.
• Can't Filter by Detail: Want to isolate feedback from VIP customers or conversations that occurred after a specific release date? Vector search isn’t designed to handle these structured filters on its own.
• Lacks Nuanced Control: Trying to exclude irrelevant topics (e.g., “Show calls mentioning onboarding but NOT ads”)? This kind of Boolean logic is not a native strength of vector search.

For teams that need to move from fuzzy patterns to precise, actionable insights, these gaps are too important to ignore.

The Power of Precision: Where Keyword Search Still Wins

This is where traditional keyword search shines. It thrives on precision. If you need to find every Zendesk ticket that mentions feature_flag_X after June 2025, keyword search is the perfect tool. It’s built for filtering conversations that include or exclude specific, exact terms.

But the rigidity of keyword search is also its downfall. It can't catch the synonyms, misspellings, or varied language that customers use, leaving a wealth of contextual insights on the table.

Alfred’s Solution: Combining the Best of Both Worlds with Hybrid Search

Alfred’s mission is to help teams understand their customers by unifying messy, varied data from sources like Gong calls, Salesforce notes, and Zendesk tickets. An insight might be buried in a vague complaint or a precisely tagged support ticket. To analyze this spectrum of feedback, you need a search model that is both exploratory and exact.

Hybrid search is that solution. It seamlessly combines:

1. Semantic Discovery (Vector Search): Finds conversations where customers hint at issues like "setup difficulty," even if they use dozens of different phrases to describe it.
2. Precise Refinement (Keyword Search): Narrows the results to include or exclude specific terms, like a feature name or competitor.
3. Structured Filtering: Applies metadata like customer segment, date range, or revenue tier to sharpen the focus of your insights.

An Example in Action

Imagine a product manager asks Alfred: "Which high-value customers had trouble integrating Shopify, but didn't mention ads?"

Here’s how hybrid search delivers a precise, nuanced answer in seconds:

• Vector search first identifies all conversations semantically related to "integration pain," "setup trouble," or "connection issues."
• A keyword filter then refines this pool, keeping only the conversations that explicitly mention "Shopify" and excluding any that mention "ads."
• Finally, a structured filter is applied to limit the results to customers tagged as "high-value" (e.g., >$200K ARR).

What would have taken hours of manual searching and cross-referencing is now an instantaneous, accurate, and complete set of insights.

The Takeaway

By moving beyond the limits of a single search method, hybrid search empowers Alfred to be both exploratory and precise, uncovering hidden patterns without sacrificing accuracy. As publications like TigerData have noted in their analysis of why platforms like Cursor are moving away from pure vector search, the future is hybrid.

At Alfred, we’re already there—and we’re seeing our customers make faster, more confident product decisions as a result.

Ready to stop choosing between context and precision? Discover what you've been missing with Alfred.