Toptal helps conversational AI company process high-volume of customer interaction efficiently.
A conversational AI company partnered with Toptal to process high volumes of customer interactions and give business teams direct access to insights previously dependent on technical support.
Client
A US-based conversational AI company automating customer interactions for contact centers.
Employees
Revenue
Industry
TechnologyDelivered Services
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The company needed to classify customer intent across a high volume of conversations without the latency and cost of sending every interaction to an LLM. Business teams also depended on engineers and analysts to answer questions about churn, retention, and agent performance, slowing access to customer insights.
Solution
Hybrid Intent Classification
Toptal architected an Airflow pipeline that used BERT and unsupervised clustering for routine intent classification, routing only ambiguous interactions to an LLM to maintain precision without incurring unnecessary inference costs.
Self-service Data Analysis
After benchmarking multiple AI frameworks and models, Toptal built an Agno database agent that translated natural-language business questions into SQL, giving nontechnical teams direct access to complex conversation data.
Outcome
Faster, Lower-cost Processing
The hybrid architecture maintained high classification precision and throughput while reducing inference costs by reserving LLM processing for interactions that actually required it.
Direct Business Insights
Business and operations teams could query retention, churn, service quality, and agent performance in natural language, eliminating their reliance on engineers for routine data requests.
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