YottaDynamics
Agentic Workflow Development
We specialize in AI agent development and build production-grade agentic workflows for organizations that have a working prototype and need to turn it into something that can be operated reliably, kept secure, and scaled without runaway cost. The focus is on orchestration, guardrails, observability, LLM production deployment, RAG pipeline development, and the delivery sequence that separates a demo from a production system.
Autonomous agents and collaborative multi-agent teams using LangGraph, CrewAI, LlamaIndex, or AutoGen — with planning, tool-calling, reflection, human escalation, and self-correction loops designed for real production conditions.
Retrieval-Augmented Generation with Pinecone, Qdrant, Weaviate, Milvus, or pgvector — including chunking strategy, reranking, metadata filtering, and query rewriting to reduce hallucinations and improve retrieval quality at scale.
High-throughput inference with vLLM — continuous batching, GPU utilization, and OpenAI-compatible APIs for running Llama, Mistral, or Gemma with lower latency and lower token-serving costs.
Semantic caching and persistent memory using Mem0, Zep, or integrated LangChain stores so agents retain task state and user context across sessions without redundant LLM calls.
Input/output guardrails, RBAC, audit logging, cost monitoring, and tracing — with on-prem, hybrid, or air-gapped deployment models for organizations with strict data sovereignty or regulatory requirements.
Integrations with Slack, Microsoft Teams, Google Workspace, Jira, Salesforce, and internal event buses — plus interfaces built with Streamlit, Gradio, or React depending on who needs to use the system.
Book a 30-minute discovery call. We'll look at what you've built and identify the gaps between where it is and where it needs to be.