Today we're announcing @lightfld's $47M Series A led by @a16z to reimagine CRM as a world model of a business.
For agents to do customer-facing work, they need to understand how your business actually works.
Salesforce wasn't designed for this. It was built 20+ years ago for
@sfcompute runs growth on a business world model built with Lightfield.
Lightfield automatically captures technical requirements like GPU model, GPU count, requested start date, and other details directly from customer interactions.
The team can then query demand across its
Lightfield's plugin for @ChatGPT and Codex is now live.
Ask any question about your company or customers, and get answers grounded in a world model of your business.
Install it in ChatGPT: chatgpt.com/plugins/plugin…
Lightfield now supports native integration with @meetgranola.
Connect your personal or team account to have transcripts and enhanced notes automatically added to meeting records.
Lightfield's Chief AI Officer @vesko_st tested whether an agent can build and maintain the classifiers to do its repetitive work, with Claude Opus writing the classifiers and TypeSafe's Jev running them.
When given the same training data, agent-built classifiers matched or beat
Jev matches frontier models on classification at a fraction of the cost. The obvious follow-up: can an agent build and maintain classifiers?
I tested on six tasks. Opus built classifiers matched or beat fine-tuned RoBERTa on all six.
