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Decryptogen FAQ

Generative AI FAQ

GenAI engine optimization is the process of enhancing the performance, speed, and cost-efficiency of Generative AI models during inference and deployment. For enterprises, this means lower latency, higher throughput, reduced compute costs, and scalable deployment of large models. Decryptogen specializes in optimizing LLMs and diffusion models to work efficiently in production environments across AWS, Kubernetes, and serverless stacks.

We apply advanced techniques including:
  • Model distillation to shrink model size
  • Dynamic batching and token streaming
  • Quantization (INT8, FP16) for lower memory use
  • Auto-scaling GPU/CPU infrastructure on Amazon SageMaker, EKS, or Lambda

    • These enable our clients to achieve 40–60% faster response times and 30–50% cost reduction on inference workloads.

    Absolutely. Decryptogen combines GenAI optimization with cloud FinOps strategies — identifying cost bottlenecks, implementing serverless inference (SageMaker endpoints or Lambda), and using memory-efficient models. We’ve helped clients save up to 56% on AWS costs while improving AI output fidelity.

    Virtually every industry with AI use cases benefits. Decryptogen has delivered optimized GenAI systems in:
  • Education – emotion-aware student-teacher platforms
  • Sustainability – GenAI for waste composition analysis
  • Agriculture – vision-based tea leaf quality scoring
  • HR Tech – candidate intelligence platforms using LLMs
  • IT Ops – agentic AI replacing DevOps and L1 support teams

    • VirOur portfolio includes:
  • GearGenie – an AI-powered rental marketplace with personalized recommendations
  • Image-based GenAI models – classifying tea leaf quality in Sri Lanka’s plantation sector
  • GenAI for waste classification – identifying recyclables in real time
  • Agentic AI platforms – replacing DevOps engineers and customer support staff
  • LLM-powered candidate intelligence – for recruiting efficiency

    • We leverage:
  • AWS Services: SageMaker, Bedrock, Lambda, EKS, Fargate
  • Vector DBs: Pinecone, FAISS, Weaviate
  • Orchestration: LangChain, AutoGen, ReAct agents
  • Monitoring: MLflow, Amazon CloudWatch, ClearML
    • This ensures scalable, secure, and production-grade GenAI performance.

    By combining:
  • Multi-GPU or multi-node parallelism
  • Serverless autoscaling (Lambda or Fargate)
  • Edge deployment when needed (CDN + AI)
  • Batch inference + streaming token outputs

    • These techniques reduce TTFB (time to first byte) and allow GenAI applications to serve millions of users simultaneously.

    gentic 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.