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🪽⚡🦞 LLM full bore
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🪽⚡🦞 LLM full bore

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irvinbuffalo/README.md

Joel Banta

Applied AI systems engineer building at the intersection of enterprise Microsoft architecture, LLM product systems, workflow automation, identity/security, and forward-deployed customer environments.

Most of my active product work lives in private organization repositories for EchoSoul.my and TheBuildOff.ai. Public repos here show representative architecture, product, and deployment patterns without exposing proprietary code, user data, prompts, or secrets.

  • LLM product architecture
  • RAG / memory workflows
  • Agentic QA and GitHub Actions automation
  • Enterprise AI deployment patterns
  • Microsoft / IAM / workflow systems

Current focus: EchoSoul.my, TheBuildOff.ai, LLMOps-style evaluation loops, and forward-deployed AI systems for enterprise environments.

Public proof artifacts

  • EchoSoul.my Architecture Brief — LLM memory companion platform with native ingestion, SMS-native interaction, human feedback loops, and memory-aware response orchestration.
  • TheBuildOff.ai Product Brief — AI-enabled leadership workshop and business simulation product with customer portal, admin tooling, commerce, fulfillment, and organization-specific AI customization patterns.
  • Enterprise AI Deployment Playbook — synthetic Microsoft-heavy enterprise AI deployment pattern covering identity, permission-aware RAG, evals, observability, auditability, human review, and rollout.

Pinned Loading

  1. echosoul-my/echosoul-architecture-brief echosoul-my/echosoul-architecture-brief Public

    Public architecture overview for EchoSoul.my, an LLM memory companion platform with SMS-native interaction and consent-sensitive workflows.

  2. theBuildOff-ai/thebuildoff-product-brief theBuildOff-ai/thebuildoff-product-brief Public

    Public product and technical overview for TheBuildOff.ai, an AI-enabled leadership workshop and business simulation platform.

  3. enterprise-ai-deployment-playbook enterprise-ai-deployment-playbook Public

    Synthetic enterprise AI deployment patterns for Microsoft-heavy environments: identity, RAG, workflow automation, evals, auditability, and human review.