Gen AI sales training platform with RAG architecture
60%
Faster ramp-up time
0
Hallucination in quizzes
Multi-LLM
Architecture
Scalable
To thousands of reps
The challenge
Sales onboarding is notoriously slow and expensive — industry data shows 3–6 months to full productivity, with DePaul University estimating total ramp-up costs exceeding $100K per rep. The client, a US-based SaaS company, wanted to use generative AI to solve this industry-wide problem at scale. While they had deep expertise in corporate training methodology, they needed a specialized AI engineering partner to design and build the Gen AI core of their platform — one that could ingest diverse training materials, generate personalized curricula, and coach reps interactively without hallucinating product knowledge.
Our solution
As the core engineering partner, we developed the platform's RAG-based training engine using a modular, purpose-driven architecture. Custom Python parsers process PDF, PPTX, tables, and multimedia into a high-fidelity knowledge graph. A multi-stage retrieval pipeline with anti-hallucination safeguards and content deduplication ensures quiz questions are always accurate and never repetitive. We deployed a multi-LLM architecture — GPT-4 for complex reasoning and curriculum generation, Mistral 7B for high-throughput tasks like quiz creation and interactive coaching. Azure Service Bus handles async processing while SQL Server stores learner progress and performance analytics.
Key highlights
Custom RAG pipeline
Multi-stage retrieval prevents hallucination
Multi-LLM architecture
GPT-4 + Mistral 7B for different training tasks
Custom parsers
PDF, PPTX, tables processed into knowledge graphs
Anti-repetition logic
Content deduplication ensures fresh quiz questions
Tech stack
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