Companies · AI / ML
AI / ML · founded 2025 · https://www.opusense.com/
Diligence memoA one-page analyst read on Opusense AI — recommendation, valuation, rhythm, risks.→Opusense AI: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Opusense AI is one of 2067 AI / ML companies tracked, on record since 2025. By capital raised it ranks mid-pack (ahead of 62% 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”
AI for Field Inspection Reports
Opusense AI automates field report writing for construction field inspectors. Instead of typing up notes at the end of the day, inspectors can now walk a site, speak observations aloud, snap photos, and let our AI assistant generate a detailed, organized report on-site and in real time. Combined, the founding team has over 15 years in the construction industry. Writing up these field reports used to take up at least 20% of the week. With Opusense, we cut that time by 5x. Our team combines deep construction experience with technical expertise, Roya has a PhD in civil engineering, and Michael has worked at multiple construction tech startups. Why hasn’t this been done before? Construction is slow to adopt new tech, and reporting is messy, photos, voice notes, and field notes all live in silos. Turning that into a clear, structured report requires real insight into how inspectors work and what clients need. Only now, with advances in AI, can we process unstructured data adequately to generate high-quality reports in real time. The opportunity is massive. Every inspection firm and contractor is struggling with documentation. Manual reports slow them down, create inconsistencies, and delay handoffs. We believe AI can do this better, and Opusense is proving it.
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 2067 companies in AI / ML. 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 62% of sector peers (real $). Modeled value above 62% 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.
Opusense 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 |
|---|---|---|---|---|---|
| 14.ai | AI / ML | — | — | — | same sector |
| 21st | AI / ML | — | — | — | same sector |
| Absurd | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Aether | AI / ML | — | — | — | same sector |
| AfterQuery | AI / ML | — | — | — | same sector |
| AgentMail | AI / ML | — | — | — | same sector |
| Ai Aiba | AI / ML | — | — | — | 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 |
|---|---|---|---|---|
| Fresco AI copilot for construction estimators | AI / ML | — | — | 77% |
| Structured AI AI workforce for Construction Engineering | HR / Worktech | — | — | 77% |
| Rudus AI Takeoffs for Concrete | AI / ML | — | — | 76% |
| Schemeflow Ltd AI Report Generation for Engineering & Environmental Review | AI / ML | Pre-Seed | $3.0M | 76% |
| Arden AI-native internal audit firm | AI / ML | — | — | 76% |
| Bild AI AI estimating & detailing for Division 8 doors, frames & hardware | AI / ML | — | — | 76% |
| NOSO LABS Build AI agents for field technicians to diagnose and sell 10x better | AI / ML | — | — | 76% |
| o11 Bespoke AI For Financial Firms | AI / ML | — | — | 75% |
See where Opusense 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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