The only AI that
computes the fire-safety
number — and proves it.
Shibi turns a building into a defensible, standards-cited sprinkler design — a number a Professional Engineer will stamp and an authority will accept — before a competitor has finished drawing the first wall. A deterministic engine does the safety-critical math; AI only reads the messy inputs.
When the system has to work, the number has to be right — and provably so.
Licensed fire protection engineers are scarce and expensive ($150–300/hr), and the work is standards-bound — EN 12845, NFPA 13, FM Global — which makes it the most automatable specialty in MEP. But it's life-safety: a wrong number isn't a bug, it's liability. So the bar isn't “plausible,” it's defensible under scrutiny — by an AHJ, an insurer, a PE's stamp, and a subpoena. That bar is exactly why generic AI can't win here, and exactly what Shibi is built to clear.
Three things a chatbot wrapper can't copy.
A deterministic engine, not a chatbot
Every PE-stampable number — hydraulic demand, pipe sizing, pump duty, clearance — is computed by a rule-based Rust/Python engine. The LLM only reads unstructured inputs (drawings, specs, water tests). That separation is why the output is acceptable to AHJs, insurers, and the Professional Engineers who sign drawings.
Oracle-validated, not self-asserted
Shibi's hydraulics are diffed per-pipe against a real commercial tool (OmniCADD HCS) to under 0.5% — and out-converge it on edge cases. Correctness is externally attestable, locked in CI. The AI-native entrants punt the hydraulic number to external software; the incumbents have no provenance at all.
A corpus that compounds
Every design persists as a hydraulics-validated, cited, standard-labeled record — content-addressed and tamper-evident. It can't be assembled after the fact, it powers warm-start priors today, and it's the only defensible path to generative layout tomorrow: our training labels carry validated physics no one else can produce.
The hard part is already built.
Shibi is pre-launch, not pre-product. The oracle-validated engine, the two-plane product, and the enterprise controls exist and run today — and 12 companies are in private beta, with product shipping to them now. We're raising to turn that cohort into paying customers and scale beyond it.
A legacy-software category the replacement finally became economical to build.
Fire-protection design runs on multi-decade desktop tools at $5–50K a seat — defensible only because the replacement used to be uneconomical. AI changed the calculus. Our wedge is the front of the funnel: the estimating and design-basis moment that every RFP triggers, and that every incumbent forces you to draw the whole building to reach.
Back the engineering
moat, early.
We're raising a [ round · size ] to reach first paying customers and turn the validated engine into a validated business. Full metrics, technical due-diligence materials, and the team are in the data room.
Request data-room access
For qualified investors tracking Shibi ahead of launch.