AI-Powered Sales Intelligence Platform
An intelligent sales assistant platform leveraging fine-tuned LLMs, autonomous AI agents, and a robust ML pipeline to transform B2B sales processes.
Project Overview
The Challenge
SalesGenius AI, a promising startup, approached us with a vision to revolutionize B2B sales processes using AI. They faced significant challenges: high customer acquisition costs, inefficient lead qualification processes, and sales teams drowning in data without actionable insights. Existing CRM solutions lacked true intelligence, and traditional sales tools failed to leverage the potential of modern language models. They needed a scalable MVP to validate their concept, attract investment, and begin acquiring early customers - all within a tight 4-month timeline and startup budget constraints.
Client
SalesGenius AI
Project Timeline
4 months
Project Team
3 ML engineers, 2 full-stack developers, 1 DevOps engineer, 1 product manager
Technology Stack
System Architecture
High-level overview of the SalesGenius AI platform architecture showing model serving, agent orchestration, and integration layers.
Our Solution
We developed a comprehensive AI-powered sales platform that transforms how B2B sales teams qualify, engage, and convert prospects.
Fine-tuned Language Models
Specialized LLMs trained on sales conversations, objection handling, and document analysis, delivering contextually relevant insights with enterprise-grade security and compliance.
Autonomous AI Agents
A multi-agent architecture that qualifies leads, analyzes conversations, identifies buying signals, and generates personalized follow-ups that reflect each prospect's unique communication style.
ML Pipeline & Integration
Kubernetes-orchestrated training and inference infrastructure with seamless CRM integration, continuous model improvement from feedback loops, and real-time performance monitoring.
"The key innovation is how these components work together — the models capture subtle patterns in sales data, the agents take intelligent actions, and the ML pipeline ensures the system gets better with every interaction."
Key Features
Intelligent Lead Scoring
AI agents that analyze prospect data and interactions to predict conversion likelihood with 78% accuracy, prioritizing high-value opportunities.
Conversation Intelligence
Real-time and post-call analysis of sales conversations, identifying key moments, objections, and sentiment shifts with actionable insights.
Automated Follow-up Generation
Personalized, context-aware follow-up messages that reflect the prospect's specific needs, objections, and communication style.
Document Analysis
AI-powered analysis of RFPs, contracts, and technical documents with automatic extraction of requirements, decision criteria, and potential red flags.
Dynamic Knowledge Retrieval
Context-aware retrieval system pulling relevant product information, case studies, and competitor analysis during live conversations.
Continuous Learning System
Self-improving models that learn from successful and unsuccessful sales interactions, constantly refining recommendations.
Technical Highlights
Custom Model Architecture
Fine-tuned Llama 2 model with sales-specific adapters, optimized for efficient inference on GPU clusters.
RAG Implementation
Advanced Retrieval Augmented Generation system using Weaviate for vector storage and hybrid search capabilities.
Kubernetes Orchestration
Multi-zone Kubernetes deployment with automated scaling, load balancing, and fault tolerance for production reliability.
MLOps Pipeline
Automated CI/CD pipeline for model training, evaluation, and canary deployments using Kubeflow and Seldon Core.
Development Process
Requirements & Data Strategy
We conducted intensive workshops with the SalesGenius team and potential end-users to define MVP requirements and KPIs. Simultaneously, we developed a data acquisition strategy, combining public datasets with synthetic data generation to overcome the cold-start problem.
Model Selection & Fine-tuning
We evaluated several base models (Llama 2, Mistral, etc.) against requirements for accuracy, latency, and cost. We then fine-tuned the selected models on sales-specific datasets, implementing RLHF (Reinforcement Learning from Human Feedback) to optimize for sales-specific outcomes.
Agent Architecture Design
We designed a multi-agent architecture using LangChain to create specialized agents for different sales functions: lead qualification, conversation analysis, and follow-up generation. Each agent had access to specific tools, retrieval systems, and reasoning capabilities.
Infrastructure & Scalability
We implemented a Kubernetes-based deployment architecture on GCP to ensure scalability and reliability. This included containers for model serving, vector stores, and application logic, with autoscaling capabilities to handle varying loads.
ML Pipeline Development
We built a comprehensive ML pipeline for automated retraining, evaluation, and deployment of models. This enabled continuous improvement as more customer data became available, with automated checks to prevent regression.
Integration & User Interface
We developed API integrations with popular CRM platforms and created an intuitive React-based interface. Special attention was paid to explaining AI decisions and recommendations to build user trust and adoption.
Results & Impact
+65%
Lead Qualification Efficiency
-32%
Sales Cycle Reduction
+41%
Conversion Rate
12.5 hrs/week
Time Saved per Rep
< 250ms
Model Inference Latency
$2.7M
Seed Funding Secured
"The MVP we built with Quirkybit exceeded all our expectations. Not only did it validate our core business thesis, but the technical architecture and ML pipeline they developed gave us a rock-solid foundation to build upon. The system's ability to provide genuine sales intelligence rather than just analytics helped us secure our seed funding round. Most importantly, our early customers are seeing dramatic improvements in their sales processes, with one reporting a 41% increase in conversion rates within the first month."
Sarah Chen
CEO & Co-founder, SalesGenius AI
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