Case Studies
Real-world tasks. Autonomous agents.
Each case study documents a real task that the Eidos system completed autonomously — the problem, the approach, the outcome, and what it reveals about how AI systems actually work.
These are not demos. They are production systems where our team of Eidos agents read structured data, plan their own work, and produce results — from ordering groceries to securing enterprise software approval.
Grocery ordering
A GitHub repo with YAML preferences and JSON Schema contracts — 3 contracts, 5 preference files, zero lines of code. An AI reads them and orders groceries weekly.
Enterprise software approved by Eidos
Eidos read the organization's contracts, planned a 10-stage approval pipeline, executed nine stages, and presented one decision to a human. No one drew the workflow. The graph emerged from the constraints.
Coming next
Voice notes
A Mac app records voice, transcribes locally with Whisper, classifies via Claude Agent SDK, and stores in recording buckets that a search engine indexes.
Project improvement
A forge scores any project across 10 dimensions, fixes the highest-impact gap, and records a snapshot. The snapshot schema is the contract.
Learning capture
A forge that extracts learnings from sessions, routes project-level fixes directly and proposes forge-level improvements to the overseer.
The pattern across every case study: structured data goes in, an agent figures out how to produce the result, and the system gets better each time it runs.
How it works: The DAG of AI