Every sentence cites a real conversation.
Amdahl ingests your sales calls, support tickets, and CRM notes, enriches every utterance across 10 dimensions, and makes the result queryable by your team and your AI agents.
Every B2B company has the answer to what its buyers want sitting in call transcripts, Slack threads, and CRM notes. Amdahl reads all of it, structures it into a queryable layer with full citations, and serves that layer to your team and your AI agents through the same API.
How it works
Connect your stack
Amdahl integrates directly into your CRM, call recordings, and support tools to operate with full context of your pipeline and customer conversations.
GongHubSpotSalesforceSlack+ more64,000+ interactions syncedStructure and enrich at scale
Every utterance is processed through ML classifiers that tag sentiment, persona, quality, deal context, and competitive signals. Raw transcripts become queryable intelligence.
10M+ utterances enriched per pipeline runInterrogate and simulate
Compare segments side by side. Test messaging assumptions against real data. Slice by won vs. lost, persona, deal size, or funnel stage to see what actually resonates before you commit.
3 clusters found, 12 premium quotes, 2 segments comparedCreate content that converts
Every piece of content is grounded in real customer language. Research carries forward so future content gets faster, sharper, and more aligned with how your buyers actually talk.
Grounded in 8 customer quotes across 3 personas
The engine
Not RAG. Not keyword search. A real data pipeline.
Most AI tools search raw text. Amdahl enriches every utterance across 10 dimensions before any retrieval happens. Your agents query pre-computed intelligence, not unstructured noise.
Ingest
Gong, CRM, support tickets, onboarding notes
ML Enrichment
10 classifiers per utterance
Cluster Discovery
Recurring themes, objections, competitive patterns
Queryable Index
Hybrid semantic + keyword search, SQL, cluster drill-in
10 classifiers per utterance
Sentiment
pain, win, objection
Persona
ML-inferred role
Quality
quotability score
Deal Stage
TOFU to POST
Competitive
mention tracking
Topics
theme extraction
Psychographics
buyer context
Segment
SMB, MM, ENT
Outcome
won vs. lost
Confidence
0 to 1 scoring
Pipeline runs in BigQuery. Your data stays in your warehouse. Tenant-isolated.
Simulate and compare
Test your assumptions before you ship.
Financial tools simulate runway. Amdahl simulates messaging impact. Slice your customer data by any combination of persona, deal stage, outcome, and segment to see which messages actually land.
Enterprise vs. Mid-Market objection patterns
- Security review delays34% of lost
- Stakeholder alignment28% of lost
- Budget timing18% of lost
- Pricing vs. DIY41% of lost
- Time to value concerns22% of lost
- Champion left company15% of lost
“How does our pricing narrative perform in won vs. lost enterprise deals?”
“What language do closed-won champions use that our website doesn't?”
“Which pain points appear in SMB but never in enterprise?”
Connections
All OAuth 2.0. All read-only by default. Every integration respects source-side permissions.
Frequently asked
What it is
How it works
Trust and security
See Amdahl on your own customer data. Thirty minutes. Your own sales calls and support tickets.