Buyers have cut budgets and expect more from every vendor. Sales and marketing teams are asked to generate more revenue with fewer resources. The teams that win read their customers well and tailor every interaction to them.
Our approach starts with the conversations your team already has. Large Language Models can read those conversations and pull out what matters: the patterns, the sentiment, the buyer’s real preferences. That turns scattered call and email data into decisions you can act on.
There is a catch. Most LLMs are trained on a static snapshot, and the world keeps moving. A standard model’s knowledge stops at its training cutoff, so it falls behind on current events and sometimes states things that are simply wrong. That makes its output hard to trust. To earn a place in a sales workflow, a model has to keep up with the world inside and outside your business.
“At Airspeed we built Dynamic LLMs that keep learning and stay current with the real world.”
We spent years at DeepMind on one problem: keeping LLMs current as the world changes. We used that work to build Dynamic LLMs, which keep learning and update with new information from the real world. They also adapt fast to an enterprise setting and can be configured to make the most of a sales team’s data. That fit suits the pace of B2B sales: the models keep learning and updating while preserving the privacy of the organizations we work with.
“Dynamic LLMs let sales teams engage every customer with context, personalization, and genuine understanding.”
Dynamic LLMs let sales teams engage every customer with context, personalization, and genuine understanding. Putting LLMs to work in sales also takes the busywork off reps’ plates. The model handles lead qualification, follow-ups, and data entry, so reps spend their time building relationships, nurturing leads, and closing deals. The sales cycle moves faster, reps stay focused on selling, and the pipeline reflects what is actually happening.
