AI systems built with
operational discipline.
We bridge continuous-improvement practice and AI engineering — memory, retrieval, human-in-the-loop governance, and agent infrastructure — and we grade our own maturity from repository evidence, honestly.
What we've demonstrated — not what we'd like to claim
Each capability is a consulting-grade brief: a maturity ladder, architecture patterns, and the repository evidence behind its grade. Where we're partial, the badge says so.
AI Operations Engineering & Continuous Improvement
Manufacturing-grade operations discipline — CAPA, RCA, and process control — applied to building and running AI systems.
Human-in-the-Loop & AI Governance
Approval gates, fail-closed permission engines, and privacy tiers that keep humans in command of agent actions.
Agent Memory & Knowledge Systems
Persistent, queryable memory architectures — from thread context to a self-hosted bitemporal knowledge store.
Conversational AI Platforms
Production WhatsApp and Telegram systems — multi-client engines, persistent sessions, containerized transports.
Multi-Agent & Deliberation Systems
Councils of models that deliberate on real decisions, and a fleet of agent workstations deployed from one engine.
Retrieval & RAG Systems
Hybrid semantic search with reranking over owned vector infrastructure — and the judgment to know when retrieval is premature.
AI Observability & Evaluation
Trace ledgers, audit rows, and always-on session recording — with formal LLM evaluation as the honest gap in progress.
AI Product Development
Taking AI systems from concept to working software — schema-first, acceptance-criteria-driven, honestly staged.
The systems behind the claims
Me Inc OS
A personal AI operating system — communications, memory, and daily intelligence, governed like a product line.
Private Knowledge System
A self-hosted, tier-guarded knowledge store any AI can query — but only through one doorway.
Agent Workstation Platform
Persistent AI-operable desktops with a fail-closed permission engine, approval queue, and always-on flight recorder.
LLM Council — "The High Table"
A private AI boardroom — configured councils of models deliberate on real decisions and issue a synthesized ruling.
Project Manager Assistant
A WhatsApp/Telegram-native PM assistant maintaining client and project memory — in pilot with a real client.
Continuous improvement for AI systems
The same loop that runs a disciplined plant runs every system here — and the written artifacts (state files, decision logs, changelogs, runbooks) are the proof.
Scope boundaries, success criteria, written state
Baselines, logs, audit rows on everything
Root cause, not symptom-patching
Session-scoped change with write-back
Approval gates, fail-closed policy, drills
Decisions logged, lessons kept, docs truth-checked
Every badge is a claim we can defend
Status
Implemented — built and working. Partially implemented — working core, named gaps. Planned — documented intent, nothing more claimed.
Maturity
Basic → Intermediate → Advanced, graded against each capability's ladder. The grade comes from repository evidence, and the rung we haven't reached is shown, unachieved, on every ladder.
What you won't find
Invented client counts, revenue figures, uptime percentages, or logos. If a number appears on this site, there is a repository, changelog, or deployment behind it.