There have always been three ways to solve an operational problem: build it yourself, buy a tool, or have someone embed in your business and build it for you. That third option — the model Palantir built on — fits the work that’s too specific to buy and too costly to own. It was also the one almost no company could afford. AI just changed that math.
Build and buy are familiar. The third one isn’t, because it was priced for governments and the Fortune 100. A forward-deployed engineer embeds in your operation, learns how it actually runs, models it, and builds custom software on that model — software shaped to your business and operated by someone accountable for the result. It works precisely because the work is too specific to buy off the shelf. It also cost $5M or more a year, which is why you’ve probably never been offered it.
The reason it cost that much is no longer true. Generative AI just changed the math on what it takes to embed an engineer in your business — and that changes who gets to have one.
Why embedding wins when the operation is too specific
When your operation is genuinely specific, the first two options fail you. The generic tool is generic — it does what most companies need, and the gap to how you work is left for you to close. Building it yourself closes that gap, but now you own an engineering function you didn’t want and can’t easily staff.
The embedded-engineer model inverts that. Instead of handing you a generic system and a configuration manual, it puts a person inside your operation whose job is to:
- Model how your business actually works — the real entities, states, and rules, not a vendor’s idea of them.
- Build custom software on that model — fitted to your operation instead of approximated by it.
- Own the result — operate it, watch it, fix it, and answer for it.
That’s the whole reason Foundry-style engagements command the prices they do. You’re not buying software. You’re buying a modeled understanding of your business plus an accountable owner. The problem was never that the model was wrong. The problem was that it took a team of expensive engineers months to build, which priced everyone but the Fortune 100 out.
What generative AI actually changed
Here’s the economic shift, and it’s the same one reshaping engineering everywhere: AI collapses the cost of the build, so the same person produces far more.
Across the industry, engineers using AI tooling are reporting large throughput gains — routine work that used to eat the bulk of a project now compresses dramatically. But the part that doesn’t compress is the valuable part: understanding the domain, modeling the system correctly, deciding what should run automatically and what shouldn’t, and owning the outcome. AI didn’t make engineering judgment cheaper. It made everything around judgment cheaper.
For the embedded-engineer model, that’s decisive. The expensive part of a Foundry engagement was never the insight — it was the labor of turning insight into working software. When that labor compresses from months of a team to weeks of one engineer with AI tooling, the whole engagement re-prices. The thing that cost $5M because it required a team for half a year can now be delivered by one accountable engineer in a fraction of the time.
The pattern survives. The price doesn’t. That gap — same architecture, a fraction of the cost — is the entire opening.
What Clarissi is
Clarissi is that engagement, rebuilt for companies between $5M and $500M in revenue. Same architectural pattern as the enterprise version. Roughly $9K to $60K a year instead of $5M.
Five pieces, and each one maps directly to why the enterprise version works:
- A semantic model of your operation. We model your business the way it actually runs — the real objects, states, and rules. Everything else is built on this, which is what keeps it fitted to you instead of approximated.
- A deterministic runtime. The work that should be predictable runs predictably, as defined steps — not as free-form model output you have to trust blindly every time. A model gets called when judgment is genuinely required, and not otherwise.
- An embedded engineer who owns it. A real person responsible for the result — who built it, runs it, and answers for it. Not a support queue. Not a configuration wizard you operate yourself.
- AI applied with judgment. We bring the expertise to know when a model belongs in the loop and how to use it — so AI is deployed where it adds real leverage, not sprinkled everywhere as a liability.
- Outcome pricing. You pay for the result we deliver and prove, not for seats or a tool you have to make work.
This is the same structure that made the enterprise version effective. We didn’t invent a new model. We made the existing one affordable.
Why this matters now and not three years ago
You could not have built this in 2023. The embedded-engineer model only re-prices for the mid-market once the build itself gets cheap enough that one accountable engineer can deliver what used to take a team. Generative AI tooling is what crossed that line. The architecture was always right; the economics finally caught up.
There’s a deeper consequence in that. Once the cost of building fitted operational software collapses, the differentiator stops being who can afford to build it and becomes who models the operation correctly and stands behind the result. That’s a judgment-and-accountability business, not a headcount business — which is exactly why a single embedded engineer with the right platform can now do what a team used to.
If you’ve been told the choice is between an expensive enterprise platform you can’t justify and a generic tool you have to operate yourself, that’s the old math. There’s now a third option: the embedded-engineer model, priced for a company your size.
The way to find out what it would model in your operation is an Assessment — we map where the highest-leverage outcomes are before anyone commits to building one.