Frequently Asked Questions

Agentic AI is a new paradigm where autonomous AI agents plan, reason, and execute actions. Decryptogen builds and optimizes agentic workflows with memory management, long-context support, and multi-step task planning. Our agentic platforms have successfully replaced traditional DevOps engineers and L1 support teams using LLM + LangChain + AWS.

Yes. We help clients fine-tune foundational models (e.g., Llama 2, Mistral) and implement RAG (Retrieval-Augmented Generation) pipelines. This ensures contextually relevant outputs with domain-specific knowledge, improved coherence, and minimal hallucinations.

Reach out via decryptogen.com for a free consultation. We’ll assess your AI stack, optimize model performance, and deliver a GenAI roadmap tailored to your technical and budgetary needs.

We combine domain understanding, data exploration, and model architecture planning to tailor solutions per client. Whether it’s a CV model for tea leaf quality, a GenAI model for waste classification, or LLMs for document parsing, we prioritize:

• Problem-to-architecture fit
• Low-latency, scalable deployments
• MLOps for reliability

We use Amazon SageMaker, Bedrock, Vertex AI, and open-source stacks like PyTorch and TensorFlow.

Decryptogen has delivered:

• Tea Leaf Quality Detection AI – CV model to assess leaf grades in real time
• GenAI Waste Classifier – AI to identify recyclable vs. non-recyclable waste from image input
• Student-Teacher Emotion Analysis – Facial emotion mapping & learning assistance platform
• Candidate Screening AI – Resume and behavioral score prediction
• Agentic AI for DevOps & L1 Support – Autonomous remediation & task execution

We apply:

• Quantization (INT8, FP16) and pruning to compress models
• Knowledge distillation to train smaller, faster models from large ones
• Batching & caching for API inference
• ONNX conversion + GPU/TPU acceleration
• HPO with SageMaker, Weights & Biases

We use:

• Bias mitigation algorithms during training
• SHAP/LIME explainability tools
• Class rebalancing & synthetic oversampling (SMOTE)
• K-fold cross-validation
• Continuous validation post-deployment

Our toolkit includes:

• Frameworks: PyTorch, TensorFlow, HuggingFace, Scikit-learn
• Cloud: SageMaker, Bedrock, Vertex AI, Lambda, Fargate, ECS
• Monitoring: MLflow, Amazon CloudWatch, Prometheus
• CI/CD: GitHub Actions, CodePipeline, Step Functions

We follow:

• Version-controlled training pipelines
• Automated retraining with triggers from Glue or S3
• Model Registry via SageMaker or MLflow
• Rollback strategy if performance drops
• Drift detection & anomaly alerts using CloudWatch

• GearGenie – AI-powered equipment rental with GenAI-based demand prediction
• Agentic DevOps Assistant – Self-healing, LLM-integrated infra bot
• Candidate Insight Engine – LLM-based personality and skill matcher
• Emotion-Aware Learning Bot – Real-time engagement and frustration detector for e-learning
• Waste Analyzer GenAI – Visual waste type classification for smart cities

We do both.

• For clients with small data, we fine-tune open models like BERT, YOLOv8, or LLaMA.
• For highly customized domains, we build models from scratch or apply few-shot prompting using Amazon Bedrock or Claude APIs.

• Serverless endpoints (Lambda, Fargate)
• Containerized GPU services via Amazon EKS
• Batching + real-time pipelines
• Edge deployment with optimized ONNX/TensorRT models
• Auto-scaling, CI/CD, and observability built-in

You can start by reaching out for a free AI strategy consultation. We’ll assess your business goals, data readiness, and propose a phased AI/ML implementation plan.
Email us: sales.smith@decryptogen.com
Contact form: https://decryptogen.com/contact