You will get AI Sales Call Analyzer - Transcribe, Score Leads & Extract Objections
Project details
Cadence is an AI-powered sales call analyzer and sales intelligence platform that transforms how sales teams review calls and close deals faster. Upload any sales call recording and get a complete intelligence brief in under 30 seconds: full call transcription, automatic speaker diarization, AI lead scoring (0-100), auto-extracted sales objections with timestamps, buying signals detection, real-time sentiment analysis, and personalized coaching tips.
Sales call analysis software built for solo sales operators, lean sales teams, SDRs, BDRs, and sales coaches. Seamlessly integrates with HubSpot, Salesforce, and Pipedrive CRM. Custom AI scoring, white-label branding options, API access, and bulk call processing available. Fast deployment: 5-14 days. Alternative to expensive sales intelligence tools like Gong and Chorus.
Sales call analysis software built for solo sales operators, lean sales teams, SDRs, BDRs, and sales coaches. Seamlessly integrates with HubSpot, Salesforce, and Pipedrive CRM. Custom AI scoring, white-label branding options, API access, and bulk call processing available. Fast deployment: 5-14 days. Alternative to expensive sales intelligence tools like Gong and Chorus.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
MLflow, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$1,500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 2 days | 10 days | 15 days |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | ||
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - |
Frequently asked questions
About Sami
Full Stack AI Engineer | RAG Systems & Document Automation
Rawalpindi, Pakistan - 11:05 am local time
Transforming unstructured data into enterprise-grade AI solutions.
I architect, build, and deploy production-ready AI systems across the complete ML pipeline: from data engineering and model optimization to MLOps infrastructure and full-stack deployment. Specializing in document intelligence automation, retrieval-augmented generation (RAG), and multimodal LLM applications.
CORE EXPERTISE:
🔹 End-to-End Document Intelligence & Automation
→ Advanced OCR with transformer-based models (TrOCR, PaddleOCR, Azure Vision API)
→ Intelligent PDF extraction using vision transformers and semantic segmentation
→ Layout-aware document parsing with graph neural networks (GNNs) and attention mechanisms
→ Batch processing pipelines with asynchronous job orchestration and fault tolerance
→ Handwriting recognition and synthetic data augmentation for improved generalization
🔹 Large Language Models & Retrieval-Augmented Generation (RAG)
→ Production RAG systems with dense vector retrieval and semantic reranking
→ Fine-tuning LLMs (LoRA, QLoRA, full fine-tuning) for domain-specific tasks
→ Multi-modal LLM applications integrating vision-language models (CLIP, LLaVA, GPT-4V)
→ Prompt engineering and chain-of-thought reasoning for complex reasoning tasks
→ Vector database optimisation (FAISS, Qdrant, Pinecone, Milvus) with approximate nearest neighbor search
→ Context window optimisation and token-efficient inference techniques
🔹 Full-Stack Backend Architecture & API Development
→ RESTful API design with FastAPI, async patterns, and performance optimization
→ Microservices architecture with containerization (Docker, Kubernetes)
→ Authentication, authorization, and security hardening (OAuth2, JWT, encryption)
→ Database design: relational (PostgreSQL), NoSQL (MongoDB), vector DBs, graph DBs
→ Real-time data streaming and event-driven architectures (Apache Kafka, Redis)
→ API gateway patterns, rate limiting, and traffic management
🔹 Machine Learning Operations & Production Systems
→ ML pipeline orchestration (Airflow, Prefect) with data lineage tracking
→ Model registry and versioning (MLflow, Weights & Biases, DVC)
→ A/B testing frameworks and experiment management
→ Continuous integration/continuous deployment (CI/CD) for ML models
→ Model monitoring, drift detection, and automated retraining pipelines
→ Feature engineering, feature stores, and data quality management
🔹 Advanced Deep Learning & Computer Vision
→ Object detection (YOLO, Faster R-CNN, EfficientDet) with real-time inference optimization
→ Semantic segmentation and instance segmentation (Mask R-CNN, DeepLabV3)
→ Image classification with transfer learning and domain adaptation techniques
→ Real-time video analytics pipelines (NVIDIA DeepStream, OpenCV)
→ Model compression: quantization, pruning, knowledge distillation for edge deployment
→ TensorRT optimization for GPU inference acceleration (40%+ speed improvements)
🔹 Generative AI & Creative Workflows
→ Diffusion models and latent diffusion implementation (Stable Diffusion, SDXL)
→ Workflow automation (ComfyUI) for image generation and video synthesis
→ Text-to-image and image-to-image generation with fine-tuned models
→ Multimodal generation pipelines and conditional image synthesis
🔹 Cloud Infrastructure & Deployment
→ AWS (EC2, S3, Lambda, SageMaker, RDS), Azure (Blob Storage, Cognitive Services), GCP
→ Serverless deployments (AWS Lambda, Azure Functions, RunPod Serverless)
→ GPU cluster management and distributed training (PyTorch DDP, NVIDIA NCCL)
→ Infrastructure as Code (Terraform, CloudFormation) and GitOps workflows
RESULTS & IMPACT:
✅ Production Deployments: 10+ enterprise AI systems deployed to production
✅ Performance Optimization: 40%+ cost reduction through TensorRT quantization and inference optimization
✅ Accuracy Metrics: 99%+ precision on OCR tasks through transformer-based architectures
✅ Scalability: Architected systems processing 100k+ documents monthly
✅ Time Savings: Clients report 80%+ reduction in manual data entry workflows
✅ MLOps Excellence: Zero-downtime deployments with automated monitoring and alerting
TECH STACK (Production-Grade):
AI/ML Frameworks: PyTorch, TensorFlow, JAX, HuggingFace Transformers, LangChain, LlamaIndex
NLP: Sentence-Transformers, spaCy, NLTK, Prompt Engineering, LLM fine-tuning (LoRA/QLoRA)
Vision: OpenCV, Pillow, torchvision, YOLO, Faster R-CNN, SAM (Segment Anything Model)
OCR: Tesseract, EasyOCR, PaddleOCR, TrOCR, Azure Vision, Google Vision API
Vector DBs: FAISS, Qdrant, Pinecone, Milvus, Weaviate, ChromaDB
Backend: FastAPI, Flask, Django, async/await patterns, WebSockets
Databases: PostgreSQL, MongoDB, Redis, ClickHouse, graph DBs (Neo4j)
MLOps: MLflow, Airflow, Prefect, DVC, Weights & Biases, GitHub Actions
Deployment: Docker, Kubernetes, AWS, Azure, GCP, RunPod, Railway, Render
Generative AI: ComfyUI, Stable Diffusion, ControlNet, CLIP, DALL-E
Steps for completing your project
After purchasing the project, send requirements so Sami can start the project.
Delivery time starts when Sami receives requirements from you.
Sami works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1:
Requirement gathering and CRM integration planning
Step 2:
Testing with client's sample call recordings