ProxFall 2025Active☆Watch⊞Compare

Prox is one of 207 Climate / Cleantech companies tracked from San Francisco, CA, USA, on record since 2025. By capital raised it ranks mid-pack (ahead of 41% 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 technical support for complex physical products

Every product ever sold needs support at some point. That support falls into one of two buckets. Bucket 1: Simple stuff. T-shirts, screen protectors, keyboards. You buy it, it shows up, maybe you ask “where's my order?” once. This is solved. Zendesk and a hundred other horizontal companies solved it. Bucket 2: Hard stuff. $20,000 industrial heaters. HVAC systems. CNC machines. Car parts. Products where buying wrong means your building doesn't have heat or your manufacturing line is down. Support for these products can only be performed by highly trained domain specialists and there aren't enough of them. If you're selling EV charging stations, your support person needs to be a certified electrician who understands local power grids, installation codes, and compatibility matrices. You can't hire this off the street. You can't outsource it overseas. You'd think LLMs would have solved this by now. They haven't. Three years into the LLM era, penetration in this industry is very low & the reason is twofold. First, off-the-shelf models don't actually understand these products. The knowledge lives in 48-page technical manuals buried on some manufacturer's website in terrible formatting — wiring schematics, compatibility matrices, installation diagrams that can only make sense visually. A general-purpose LLM can't draw you the diagram showing how to connect terminal A to terminal B. It doesn't have the spatial understanding or the product-specific reasoning to be a real technical advisor. So companies still rely entirely on human experts. Second, even if the models were good enough, there are no harnesses to make them useful in the business. No engine to capture deep technical knowledge about complex physical products and keep it updated. No way for a company to offload tribal knowledge from their senior technicians into a system. No way to see what questions customers are actually asking and feed that back into the knowledge base. No generative multimodal presentation and no expressive voice support. Prox is building the best technical product expert for extremely complicated physical products. A multimodal agent that can draw wiring schemes, share CAD models, process incoming videos from a technician in the field, and support people over the phone with voice that can pass the Turing test. To get there, we're solving multimodal knowledge graph building at a very deep level. A huge portion of your work will be developing SOTA knowledge engines that can truly understand complex physical products.

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 207 companies in Climate / Cleantech. 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 41% of sector peers (real $). Modeled value above 41% 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.

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.

Prox 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.

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.

See where Prox sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.

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