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@automatiqa-lab

Automatiqa Lab

Open-source experiments where operations meet the algorithm. A solutions lab making physical operations legible to algorithms.

Automatiqa Lab

Open-source experiments where operations meet the algorithm.

This is where I build and publish small, sharp tools for the parts of supply chain and operations that software still walks past - the physical work, the tribal knowledge, the decisions nobody ever wrote down. Each project is MIT-licensed, built in public, and small enough to read in one sitting.

The lab, with every project and its status, lives at automati.qa. The build logs and the thinking behind each one are on alxsidr.io.

Projects

Project What it does Status
oodaa A small, readable self-improving agent loop - Observe, Orient, Decide, Act, and the second A, Adjust live
risk-navigator Multi-agent operational risk monitoring across freight, fuel, labour, weather, and geopolitics, turned into briefings and a live dashboard live
flowtwin Watch an operational process once, get editable process maps and runbooks back work in progress
orchestrator The execution layer - turn signals into decisions, policy, and a traceable record work in progress
calibri Agentic coordination of sample lifecycle management for agri-food and soft commodities pipeline
synthax A synthetic assistant for supply chain - voice-first and context-aware pipeline

The idea behind the lab

Most agentic AI assumes a level of digital maturity that real operations do not have. The process lives in someone's head, the exception gets handled by instinct, and nothing downstream can be automated because nothing upstream is described. The lab works the other side of that gap: capture what actually happens, make it legible, and only then let an algorithm act on it.

Everything here is open. Fork it, break it, send it back better.

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  1. flowtwin flowtwin Public

    Watch an operational process once, get editable process maps and runbooks. The capture layer for tribal knowledge.

  2. orchestrator orchestrator Public

    The execution layer for supply chain operations: turn signals into decisions, policy, and a traceable record.

  3. synthax synthax Public

    A synthetic assistant for supply chain: voice-first, context-aware, built to advise.

  4. calibri calibri Public

    Agentic coordination of sample lifecycle management for agri-food and soft commodities.

  5. risk-navigator risk-navigator Public

    Multi-agent operational risk framework - freight, fuel, labour, weather, geopolitics - turned into weekly reports and a live dashboard. Model-agnostic via LiteLLM. MIT.

    Python

  6. oodaa oodaa Public

    Self-Improvement Framework for Supply Chain Agents

    Python

Repositories

Showing 7 of 7 repositories

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