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How is BuildBetter different from RAG/Embeddings tools?

Understanding why BuildBetter’s approach delivers superior results for B2B teams analyzing conversations at scale.

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The Limitations of RAG and Embeddings

Traditional RAG (Retrieval-Augmented Generation) and embedding-based tools rely on semantic similarity searches. While this approach works for simple information retrieval, it falls short when analyzing complex business conversations.

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Common Problems with RAG/Embeddings:

Semantic Similarity Misses Context

When you ask “What are the top customer issues?”, RAG searches for content semantically similar to “issues” but misses nuanced phrases like:
  • “This creates friction in our workflow”
  • “It took me longer than expected”
  • “We had to find a workaround”
  • “The team struggled with this”

No Contextual Understanding

RAG can’t distinguish:
  • Who’s speaking: Customer vs. team member vs. prospect
  • What they’re discussing: Your product vs. a competitor’s
  • Why it matters: Bug report vs. feature request vs. general feedback
  • When it’s relevant: Current issue vs. resolved problem

Performance Degrades at Scale

  • Works adequately for 3-10 transcripts
  • Quality drops significantly beyond that
  • “Needle in haystack” queries work, but knowledge-based questions fail
  • Can’t handle questions like “What were the 10 most common problems Alice had with our product?”

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BuildBetter’s Proprietary Approach

BuildBetter doesn’t use traditional RAG or embeddings. Instead, we’ve built a sophisticated pipeline that understands conversations the way humans do.

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Our Technology Stack

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Real-World Comparison

Query: “What are our customers’ main issues?”Process:
  1. Searches for semantic matches to “issues”
  2. Returns fragments containing similar words
  3. No understanding of context or speaker
  4. Quality degrades with more data
Result: Incomplete, often irrelevant results mixing customer issues with internal discussions, competitor mentions, and resolved problems.

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Why This Matters for B2B Teams

Quality Over Quantity

Get meaningful insights from every conversation, not just keyword matches

Scale Without Limits

Analyze your entire conversation history without degrading quality

Actionable Intelligence

Receive reports you can act on, not just search results

Trust Through Citations

Every insight links back to the original conversation

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Technical Deep Dive

Our pipeline is expensive to run because we prioritize accuracy and context over simple semantic matching. This investment in infrastructure means you get insights that actually drive business decisions.

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What Makes Our Pipeline Different:

  1. Multi-Stage Processing
    • Initial transcription and speaker diarization
    • Context enrichment from CRM and internal data
    • Signal extraction and classification
    • Pattern recognition across conversations
    • Report generation with citations
  2. Custom Models
    • Proprietary models trained on B2B conversations
    • Understanding of business terminology and context
    • Continuous learning from usage patterns
    • No reliance on generic embeddings
  3. Intelligent Filtering
    • Automatic noise reduction
    • Relevance scoring
    • Temporal awareness
    • Business impact assessment

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Customer Success Story

“We had an AI-native enterprise customer with 5 engineers spend 6 months trying to build a similar solution using RAG and embeddings. They couldn’t get anywhere close to what BuildBetter produced in our reports. Their contract with us was 50x cheaper than what they’d already spent trying to build it themselves.”

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The Bottom Line

RAG and embeddings are great for:
  • Simple semantic search
  • Finding specific mentions
  • Basic Q&A systems
BuildBetter is built for:
  • Comprehensive conversation analysis
  • Pattern recognition at scale
  • Actionable business intelligence
  • Quality insights from massive datasets

Ready to see the difference?

Start your free trial and experience how BuildBetter transforms your conversation data into actionable insights.