Frontier-quality reasoning at a fraction of the cost — for high-volume Fluents deployments.
Run DeepSeek as Your Fluents Conversation Engine
DeepSeek has produced open-weight models that match or exceed GPT-4 performance on many benchmarks at a fraction of the inference cost. For Fluents customers running high volumes of outbound calls — insurance renewal campaigns, debt collection, healthcare reminder batches — the cost-per-call economics of the conversation engine matter.
DeepSeek is available as an alternative conversation engine in Fluents for organizations where cost efficiency is the primary driver of model selection.
Frontier-quality conversation engine at significantly lower inference cost — reducing cost-per-call for high-volume outbound campaigns
Open-weight architecture allows deployment on your own infrastructure for organizations with data sovereignty or cost control requirements
Strong instruction-following and structured output capabilities support reliable intake, qualification, and CRM-writing workflows
The Cost-Per-Call Equation
For an insurance carrier making 100,000 outbound renewal calls per month, the cost of the conversation engine is a real line item. DeepSeek's inference pricing runs at a fraction of GPT-4 or Claude. If DeepSeek's reasoning quality meets the bar for your use case — and for many structured intake and qualification workflows it does — the economics make a compelling case for using it over more expensive frontier models.
Healthcare Reminders: Structured, High-Volume, Cost-Sensitive
Appointment reminder calls are high-volume, low-complexity interactions. The agent needs to confirm the patient's name, appointment time, and whether they'll attend or need to reschedule. This doesn't require the most powerful LLM available — it requires a reliable, instruction-following model that can handle the task accurately at scale. DeepSeek fits this profile well.
Open Weight, Your Infrastructure
DeepSeek's open-weight releases can be self-hosted, which matters for organizations with strict data processing requirements. Running the conversation engine on your own infrastructure eliminates third-party data transfer entirely — relevant for healthcare and legal firms with the IT capacity to support it.