About Clarissi
Embedded engineering for AI-native operations. We assess, build, and operate custom AI for your business — with a named embedded engineer accountable for the outcomes.
Why we built this as embedded engineering
Most AI automation tools sell you a platform and walk away. You're left with a canvas, a subscription, and the full operational burden of figuring out what to build, building it, and keeping it running as your business changes.
We think that's the wrong model. The value of AI in operations comes from deployed automation that's been modeled against your business rules, built with generative AI tools, and operated by an embedded engineer who understands the context. That doesn't come from a platform you configure yourself. It comes from embedded engineering.
Clarissi is embedded engineering for AI-native operations. We model your operations as a semantic model, build custom AI workflows using generative AI tools (Claude, Codex), and embed a dedicated engineer who runs the platform and delivers monthly outcome reports. You get the outcomes. We're accountable for them.
The embedded engineer model
A Clarissi embedded engineer is a trained specialist who runs the platform on behalf of a set of clients. They're not generalist consultants — they're operations specialists who have seen how AI automation plays out across multiple customer environments and understand what good execution looks like in practice.
The embedded engineer is outcome-aligned: their Concierge fee includes an outcome component tied to verified results above the agreed baseline — scoped to your operations during the Assessment. They earn more only when your business produces more. That alignment is why embedded engineering produces sustained outcomes that self-directed automation rarely does.
This model is called "forward-deployed engineering" at Palantir. Clarissi is built around it from the ground up — for the mid-market, using generative AI tools to deliver the same architectural pattern at a fraction of the price.
Run the Assessment
Run a structured analysis of the client's environment over 1–2 weeks and deliver a four-section report covering automation exposure, what's ready to activate, custom candidates, and recommended path forward.
Build custom AI on your semantic model
Build custom AI automation against your business rules using generative AI tools (Claude, Codex). Your operations become a semantic model the AI executes deterministically against — no hallucinations, full audit trail.
Monitor and tune
Review execution logs, adjust conditions as the client's operations evolve, and activate new skills from the catalog as they ship.
Deliver monthly outcome reports
Measure verified results against the agreed baseline and deliver a clear breakdown of outcomes produced each month.
Manage the engagement
Handle credential setup, integration changes, and tier transitions — so clients never need to maintain an internal automation team.
Embedded engineer continuity
One concern we hear often: "What happens if my embedded engineer leaves?" The answer is that knowledge doesn't leave with them. Your semantic model, execution history, business logic context, and outcome baselines are all stored in the platform. If your embedded engineer transitions, Clarissi assigns a replacement who inherits full context. No re-onboarding from scratch. The platform is the memory — the embedded engineer executes against it.
Interested in becoming an embedded engineer?
We're selectively recruiting embedded engineers — people who want to build expertise in AI-native operations and run engagements with a growing client base.