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The questions below are grouped by topic. Every answer is written so it can stand alone — the first sentence is the answer.
An AI operations studio is a company that runs AI operations for other companies — hands-on staff training, systems integration, and AI agents built, deployed, and governed inside the client's real environment. It differs from a consultancy in that it builds and runs rather than recommends, and from a software vendor in that the products come from operating practice. Shiny coined the term for itself and runs its own company the same way.
Shiny is an AI operations studio at shiny.sh that embeds with mid-market companies to train their people, integrate their systems, and put governed AI agents into production — proving the value with evidence rather than projections. The studio runs itself on AI agents with human approval gates, and its products, Miranda and Shadow, grew out of that practice.
Shiny was founded by Nicholas Hetlinger, an operator who builds and runs AI systems inside real companies — training rooms, enterprise integrations, and governed agents in production pilots — and runs his own studio the same way. He works with specialist collaborators who, by the studio's own privacy rules, are not named publicly.
Yes — Shiny delivers in medical-device and FDA-adjacent environments, with a hard data gate: no PHI and no regulated production data enters AI workflows without written approval and a security review. The discipline extends to publication: client work appears only de-identified, with sector descriptors instead of names.
By checking permissions before the agent acts, not after: a broker verifies the requesting person's identity, role, and permissions before any system call is made, and the model never holds raw credentials. Add data classification that blocks sensitive data from leaving by default, and human approval for high-impact actions, and the agent can only do what the person asking could be approved to do. This is how Shiny builds agents, and how its Miranda platform works.
Human-in-the-loop approval means an AI agent pauses before a high-impact action — a write, a send, an export, a permission change — and a human decides whether it proceeds. Done well, the approval arrives where the approver already works, states the exact action and scope, and is re-verified at the moment it's clicked so a stale approval fails closed. In Miranda, approvals are cards in Microsoft Teams.
Yes, if the platform records evidence for every meaningful action: who asked, what was checked, what ran, what was touched, and who approved it. The strongest form is an append-only record that can't be quietly edited after the fact. Shiny treats auditability as a design requirement, not a feature — an agent that can't produce evidence shouldn't be acting in your systems.
Miranda is Shiny's enterprise AI agent platform: governed AI agents that live in Microsoft Teams, answer from your company's own documents and systems with citations, and act only through a permission boundary — identity checked before every call, human approval cards for high-impact actions, and an append-only audit trail. It is in active development, working with design partners.
Permission checks happen before tool execution, not after model planning: a connector broker verifies the requesting person's identity, role, and permissions before any call is made, and the model never holds raw credentials. High-impact actions pause for a human approval card in Teams, re-verified at click time, and sensitive changes enforce separation of duties.
Miranda is in active development — Shiny is working with design partners, and early access is by conversation rather than sign-up. If you want to be one of the companies shaping it, email hello@shiny.sh and say so; the early-access queue gets read first.
Hands-on, in the tools people actually use, department by department — not seminar slides. Training sessions work on real tasks from the trainee's own queue, so the skill transfers the same day. Shiny delivered 66+ hours of staff AI training in one month for a single client this way, and the curriculum behind it is productized in Shadow, its operator learning platform.
With evidence, not projections: track what actually shipped, validate what it actually saved, and keep value classes separate — hard one-time value, recurring savings, and freed capacity are different things and should never be summed into one impressive number. Ranges stay ranges. Shiny publishes only validated figures, like $55k–$80k of hard, one-time value validated for a single mid-market client in its first delivery cycle — covering fees 1.4–2.0×.
Shadow is Shiny's AI-operator learning platform at shadow.shiny.sh: a 150-atom library of concepts, patterns, anti-patterns, workflows, and agent skills drawn from real operating practice, plus downloadable agent-skill kits for Claude Code and Codex. It's live as an invite-only preview — invites by email at hello@shiny.sh.
Yes — Shadow is agent-native by design. Every page has a raw-markdown sibling, the site publishes an llms.txt, and a hosted MCP service lets an agent query the library natively mid-task. Downloadable skill kits install Shadow's practices directly into Claude Code or Codex, so your agent doesn't just read the library, it works from it.