Webomage designs and operates production-grade AI/LLM infrastructure that connects models, data, and applications with robust cloud and DevOps practices.

Focus Areas

  • Multi-provider LLM integrations (OpenAI-like, Anthropic, Hugging Face, custom models)
  • Orchestration with LangChain (Python & JS), LangGraph, Crew.AI, Flowise, Hatchet
  • Retrieval-Augmented Generation (RAG) pipelines with domain-specific data processing
  • Evaluation and routing using benchmarks like SWE-bench and custom metrics
  • Secure, observable deployments on AWS, Kubernetes, and serverless platforms

Example Work

  • Merchant & chatbot platforms (Agentsmith)
    Integrated Hugging Face models, Oogabooga, LangChain, Flowise, and merchant-focused assistants into a unified system with Docker and CI/CD. Designed workflows for fine-tuning, evaluation, and safe rollout.

  • YC-backed AI startup (Hong Kong)
    Connected multiple AI tools and LLM providers to production infrastructure, enabling advanced debugging, experimentation, and gradual deployment of new capabilities.
    See the case study: YC-backed AI Startup – AI Infrastructure & Tooling.

  • US law firms association – AI CRM
    Built AI-based CRM automation that analyzes emotional context in client responses, integrating AI calls into existing workflows with observability and safe failure modes.

  • Healthcare AI services
    Architected AI services in healthcare contexts with HIPAA, SOC 2, PCI, and GDPR readiness, emphasizing data protection, auditability, and robust monitoring.

  • Long-form psychiatric session voice analytics
    Designed and operated AI pipelines for 1.5h+ psychiatric voice sessions, combining STT, LLM post-processing, and compliance-aware storage. See the case study: Long-Form Psychiatric Session Voice Analytics.

Typical Deliverables

  • End-to-end AI/LLM pipelines from prompt to production, including data prep and retrieval
  • Infrastructure-as-code (Terraform / Serverless Framework) for reproducible environments
  • Kubernetes- and serverless-based deployment architectures with CI/CD
  • Monitoring, logging, and tracing for model performance and reliability
  • Documentation and onboarding materials for engineering and operations teams

➡️ If you need AI/LLM infrastructure that is reliable, observable, and compliance-aware, start a conversation.