Companies · Energy
Remote · Energy · founded 2026 · https://inviscidai.com/
Diligence memoA one-page analyst read on Inviscid AI — recommendation, valuation, rhythm, risks.→Inviscid AI: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Inviscid AI is one of 69 Energy companies tracked from Remote, on record since 2026. By capital raised it ranks mid-pack (ahead of 54% of sector peers), and mid-pack by modeled valuation est..
Ranking is computed against this company's own sector cohort — reported capital is fact; valuation tiers are modeled.
AI analyst read est. — model-extracted from this company's public description, not a verified fact. 30%
operates a technology-led product inferred from public copy
Grounded in: “You are a venture analyst”
Real-time Physics Simulations for Industrial Facilities & Data Centers
Inviscid AI builds physics-informed AI solutions that transform how buildings and data centers operate. By combining real-time IoT sensor data with computational fluid dynamics (CFD) modeling, we create digital twins that simulate building performance in real time and autonomously optimize operations. Our platform optimizes airflow patterns and ventilation strategies to eliminate dead zones, improve air distribution, and reduce the load on mechanical systems. On the energy side, we minimize HVAC power consumption, reduce cooling costs, and lower overall operational expenses while maintaining optimal thermal comfort and indoor air quality. Beyond immediate operational efficiency, we optimize equipment scheduling and maintenance cycles by predicting system behavior under different conditions, allowing facilities managers to proactively address issues before they become problems. Our physics first approach ensures that we're not just optimizing against historical patterns, but optimizing based on a deep understanding of how air, heat, and energy actually move through your building, enabling us to find solutions that traditional rule-based or purely data-driven systems would miss.
As reported in public records reported — not modeled.
Solid bars are reported offering amounts reported; hatched bars are the modeled post-money valuation est. — both on one shared scale so you can read raise-vs-worth at each round directly. Use the toggles to overlay data labels and the niche-peer / market average value lines.
No round amounts on record to chart.
No staged rounds to sequence.
Round size and date are reported; the stage label is inferred from round size. Valuation is modeled from stage benchmarks. Directional, not a quoted figure.
Not enough modeled valuation points to chart a trajectory.
Benchmarked against 69 companies in Energy. Each bar is a median (the middle company, not an average — outliers don't skew it). Two yardsticks: real money raised (reported on Form D) and modeled value (our estimate est.). These are whole-sector medians across all stages, except the per-stage row.
Raised more than 54% of sector peers (real $). Modeled value above 54% of peers (estimate).
Stage is inferred from round size est., not reported on the filing — a round's dollar size maps to a bucket: Pre-Seed <$1.0M · Seed $1.0M–$4.0M · Series A $4.0M–$15M · Series B $15M–$40M · Series C $40M–$100M · Series D+ $100M–$400M · Growth/Late >$400M.
| Stage | Amount · real | Announced | Post-money · est | Value · est | Conf. |
|---|---|---|---|---|---|
| No rounds recorded. | |||||
Predictive signals are modeled est. from this company's own cadence and step-up, plus sector benchmarks — directional, not advice. Peer set and a CSV export live in your analyst workspace.
Inviscid AI is an official record sourced from the U.S. Securities and Exchange Commission (SEC). U.S. data is aggregated from SEC Form D filings.
Nearest neighbours across the whole database — matched on sector, stage and capital scale, and on shared operators (officers or directors named at both companies in public filings). A discovery shortlist, not a valuation cohort — verify before acting, the same way modeled figures are directional.
| Company | Sector | Stage | Raised · real | Value · est | Why similar |
|---|---|---|---|---|---|
| Apollo Atomics, Inc. | Energy | — | — | — | same sector |
| Aravolta | Energy | — | — | — | same sector |
| Archimede | Energy | Seed | $1.6M | $3.1M | same sector |
| Arnergy | Energy | Series A | $9.0M | $95.1M | same sector |
| Atomic Alchemy | Energy | — | — | — | same sector |
| Atomos Nuclear & Space Corp | Energy | Series B | $19.0M | $115.9M | same sector |
| Aurabeat | Energy | — | — | — | same sector |
| BlueLine Grid, Inc. | Energy | Series A | $10.3M | $25.9M | same sector |
Matched by meaning, not labels — a local language model reads each company's name, sector and description and ranks the closest in that learned space. This catches look-alikes that cross sector boundaries; the structured list above explains its matches, this one trusts the text. Directional, like every modeled signal here.
| Company | Sector | Stage | Value · est | Match |
|---|---|---|---|---|
| Navier AI Agent-Driven Engineering | AI / ML | — | — | 76% |
| Piris Labs Inference at Light Speed | AI / ML | — | — | 76% |
| Mesh Intelligent Technologies, Inc. | AI / ML | Series A | $91.3M | 76% |
| Axross Pte Ltd AI-driven HVAC control system for improved energy performance | AI / ML | — | — | 76% |
| Overshoot AI Infra for real-time vision applications | Public Safety & Security | — | — | 75% |
| Paces Agentic AI for Power Projects and AI Infrastructure | Proptech / Real Estate | — | — | 75% |
| nCompass Technologies Optimize performance on GPUs - 10x faster | AI / ML | — | — | 75% |
| Industrial Intelligence,Inc | AI / ML | Pre-Seed | $5.8M | 74% |
See where Inviscid AI sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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