You have 2,847 testimonials. You can find six.
Praise piles up faster than anyone can read it. Marketing wants quotes about pricing. Sales wants Head-of-Ops voices. Product wants the objection nobody’s solving. Without structure, every request becomes a 90-minute Cmd-F session.
Sentiment Analysis reads every quote the moment it lands and tags 12 dimensions — sentiment, emotion, themes, features, personas, objections, outcomes, jobs-to-be-done, and more. The library becomes searchable proof, not a graveyard.
Every quote, x-rayed across twelve dimensions.
One pass, twelve answers. Re-runs incrementally on edits — no re-tagging the world.
Polarity from −100 to +100. Captures intensity, not just positive/negative.
Plutchik's eight emotions with confidence scores per quote.
Topic clusters auto-discovered across your library and labeled in plain English.
Maps to your product taxonomy. Pulls from your docs and feature names.
Inferred role and seniority. Cross-checked with import metadata when present.
What the customer pushed back on — pricing, setup, learning curve, fit.
The problem the customer was trying to solve before they switched.
Quantified results when present, qualitative when not. Numbers extracted.
What hire your product made — discoverability, time-saving, status, or skill.
0–100 rating of fitness for marketing use. Considers length, specificity, sentiment, and authenticity.
The strongest 8–14 word fragment, ready for a hero or banner.
Detects authenticity markers: specificity, named numbers, before/after structure.
Quote in, twelve tags out — under two seconds.
The whole pipeline runs the moment a testimonial lands. No batch jobs, no nightly cron, no waiting room.
{ source: 'typeform' }
len: 412 chars
model: text-embed-3
dim: 1536
passes: 12
parallel: true
flagged: 1
auto: 11
ms: 312
v: 4.2.1
Themes discover themselves. You stay in charge.
The model clusters quotes into themes, names them in plain English, and shows you the count plus average sentiment. Rename, merge, split, or pin themes — the next quote respects your taxonomy.
Ask in plain English. Get the right quotes in 0.3s.
The dimensions become a query language. Save filters, share with the team, drop them straight into a widget or a wall.
Correct it once. It learns your taste.
Every override — a renamed theme, a re-scored sentiment, a corrected persona — feeds back into your workspace’s tuning. The model gets sharper on your domain without ever leaving it.
Tagged once. Useful everywhere.
Built for enterprise paranoia.
Your customer voice never trains a shared model. Inference runs in your region. Audit logs cover every decision.
| Models | Anthropic Claude 3.5 + OpenAI text-embed-3 (regional) |
| Privacy | Workspace-isolated tuning · zero shared training |
| Regions | us-east-1, us-west-2, eu-west-1, ap-southeast-1 |
| Latency | Median 1.4s · p95 2.8s end-to-end |
| Languages | 32 supported · auto-detected per quote |
| Audit | Every decision logged with model version + confidence |
| Compliance | SOC 2 Type II · GDPR · HIPAA-eligible (Enterprise) |
| Throughput | Up to 50k quotes/day · burst to 5k/min |
| Versioning | Pin model version per workspace · re-run on upgrade |