RAG vs BuildBetter: Understanding the Difference

Learn how BuildBetter differs from traditional RAG and embedding-based tools

How is BuildBetter different from RAG/Embeddings tools?

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

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.

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?”

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.

Our Technology Stack

Proprietary Signal Processing

We’ve developed a custom dataset called “Signals” that:

• Processes data contextually, not just semantically
• Understands business context and relationships
• Maintains accuracy across hundreds of hours of content
• Provides accurate citations for every insight

Contextual Intelligence

Our models understand:

• Speaker identification: Automatically distinguishes customers, team members, prospects
• Topic relevance: Filters out irrelevant mentions (e.g., “weather complaints”)
• Temporal context: Knows if something is a current issue or historical reference
• Business impact: Prioritizes based on severity, frequency, and business value

Scale Without Compromise

Unlike RAG systems:

• Analyze hundreds of hours of transcripts with consistent quality
• Generate comprehensive reports, not just answers
• Identify patterns across massive datasets
• Maintain citation accuracy throughout

Real-World Comparison

RAG/Embeddings Approach

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.

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.

Query: “What are our customers’ main issues?”

Process:

1. Identifies all customer conversations
2. Extracts actual problems (even if not called “issues”)
3. Filters by relevance and recency
4. Groups by theme and impact

Result: Comprehensive report with:

• Ranked list of actual customer problems
• Citations to specific conversations
• Severity and frequency analysis
• Actionable insights with context

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

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.

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

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.”

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.