Isn't this just RAG?
No. BuildBetter doesn't use traditional RAG or embeddings. We've built a proprietary pipeline that understands conversations the way humans do.
Why RAG and embeddings fall short
Semantic similarity misses context
When you ask "What are the top customer issues?", RAG searches for content similar to "issues" but misses nuanced phrases like "This creates friction in our workflow" or "We had to find a workaround."
No contextual understanding
RAG can't distinguish who's speaking (customer vs. team member), what they're discussing (your product vs. a competitor's), why it matters (bug vs. feature request), or when it's relevant (current vs. resolved).
Performance degrades at scale
Works for 3-10 transcripts. Quality drops significantly beyond that. "Needle in haystack" queries work, but knowledge-based questions like "What were the 10 most common problems Alice had?" fail.
BuildBetter's proprietary pipeline
Our pipeline is expensive to run because we prioritize accuracy and context over simple semantic matching. This investment means you get insights that actually drive business decisions.
Proprietary signal processing
Multi-stage pipeline: transcription, speaker diarization, context enrichment from CRM, signal extraction and classification, pattern recognition, and report generation with citations.
Contextual intelligence
Custom models trained on B2B conversations. Understands business terminology, speaker roles, sentiment, severity, bias, and business impact — not just keyword similarity.
Scale without compromise
Analyze your entire conversation history without degrading quality. Our pipeline is expensive to run because we prioritize accuracy and context over simple semantic matching.
Intelligent filtering
Automatic noise reduction, relevance scoring, temporal awareness, and business impact assessment. Every insight links back to the original conversation with citations.
Side by side
“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 is 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