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Aeonik

Operational intelligence for live events and AI teams: memory, sources, boundaries, and experience that compound.

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Aeonik

Operational intelligence for work that has to compound.

Aeonik builds AI systems with memory, sources, boundaries, and a track record — so teams can see what happened, understand why decisions were made, and carry hard-earned context into the next cycle.

Website · Ingrain · Contact


Groundscore

Live operational intelligence for event teams.

For festivals, productions, venues, and temporary cities, Groundscore turns scattered operational context into a source-linked record of:

  • what happened
  • what is still open
  • who owns it
  • why decisions were made
  • what the next event should remember

It starts in replay and shadow mode: plans, maps, schedules, debrief notes, incident reports, staffing docs, vendor notes, chat exports, radio transcripts, and workflow interviews become a searchable operational record and next-cycle planning brief.

No autonomous dispatch. No rip-and-replace. No new hardware required. Human operators stay in control.

Open source foundation

A local-first learned-experience layer for AI agents.

Most memory tools answer: what did we talk about?
Ingrain answers: what should the agent do differently next time?

It turns agent run history into source-linked practice cards — corrections, decisions, stale-plan warnings, lessons, and completed outcomes — then hydrates future sessions with the few notes that should change behavior.

pipx install aeonik-ingrain
cd your-project
ingrain init
ingrain remember --type correction "Do not push without running tests."
ingrain hydrate --query "about to push"

The Aeonik substrate

Aeonik systems are built around four trust primitives:

  • Memory — durable context that compounds across runs, events, and teams.
  • Sources — every important claim links back to where it came from.
  • Boundaries — systems assist; humans approve consequential action.
  • Experience — corrections, approvals, handoffs, outcomes, and decisions become a visible track record.

What we are building toward

Aeonik is focused first on live operations and agent reliability: environments where context fragments across tools, decisions happen under pressure, and the cost of forgetting is real.

The goal is not to replace expert operators. The goal is to give them a living operational record that is queryable in the moment, accountable after, and reusable next cycle.


Queryable in the moment. Accountable after. Reusable next cycle.

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  1. ingrain ingrain Public

    Local-first learned-experience layer for AI agents.

    Python 1

  2. .github .github Public

    Aeonik GitHub organization profile

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