The problem
Sales reps spend 28% of their working time on CRM administration — logging calls, updating contacts, recording outcomes, moving opportunities through stages. That's more than a day a week per rep lost to data entry.
Generic transcription tools exist, but they fail on two counts: domain-specific language (product names, deal terms, customer-specific context) produces inaccurate transcription, and they don't integrate natively with Salesforce custom objects.
What we built
Maven: a mobile app combining bespoke domain-specific LLMs with native Salesforce API integration. Reps record a voice note after a call. The system transcribes, interprets, and populates Salesforce — contacts, opportunities, activities, custom objects — in real time.
- Bespoke LLMs trained on sales domain language — significantly outperforming generic transcription on domain accuracy
- Native Salesforce API — auto-populates contacts, opportunities, and custom objects without manual data entry
- Multi-language voice recognition — built for global sales teams across geographies
- Gamified productivity scoring — team leaderboards tracking call activity, conversion rates, and CRM hygiene
- Mobile-first design — iOS and Android, designed for field reps and remote teams
The outcome
30%+ of sales team time reclaimed — measured against baseline. Real-time Salesforce updates from voice without any manual data entry step.
| Area | Before | After |
|---|---|---|
| CRM updates | Manual post-call data entry | Voice-to-Salesforce automated |
| Domain accuracy | Generic transcription errors | Bespoke LLM domain precision |
| Integration | Copy-paste or manual entry | Native Salesforce API |
| Language support | English only | Multi-language |
| Adoption | Zero gamification | Leaderboards driving engagement |
| Time on CRM admin | 28% of working time | 30%+ reclaimed |
Salesforce AppExchange
A listing on the Salesforce AppExchange is planned — which would make Maven available to the full Salesforce customer base as a native app rather than a custom integration. The bespoke LLM approach creates a defensible accuracy advantage that generic transcription tools cannot match without domain-specific fine-tuning.
