Companies · AI / ML
San Francisco · CA, USA; Remote · AI / ML · founded 2026 · https://prose.md
Diligence memoA one-page analyst read on OpenProse — recommendation, valuation, rhythm, risks.→OpenProse: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
OpenProse is one of 2067 AI / ML companies tracked from San Francisco, CA, USA; Remote, on record since 2026. 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”
An open-source operating system for reliable long-running agents
OpenProse is building open-source infrastructure for long-running AI work: a declarative language plus the Reactor harness/runtime for reliable agent workflows. Our organic open-source traction (7k+ installs) has already translated into a customer at a medical research lab, starting with a $10k pilot set to roll into a $10k/month contract. YC recruited us into the current batch and we've since hired two senior founding team members (ex-Google, Forbes 30u30 harness engineer). All three founding team members are two time founders. We've raised $1.25M in the last two weeks from Jonathan Abrams 8-Bit Capital, Otis Chandler, and other angels.
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.
OpenProse 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 |
|---|---|---|---|---|---|
| Accord | AI / ML | — | — | — | same sector |
| Acely | AI / ML | — | — | — | same sector |
| Aedilic | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Affogato AI | AI / ML | — | — | — | same sector |
| Aftercare | AI / ML | — | — | — | same sector |
| Agentic Labs | 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 |
|---|---|---|---|---|
| Sourcebot Helping humans and AI agents understand massive codebases | AI / ML | — | — | 81% |
| OpenFoundry The fastest developer experience for building on open source AI. | AI / ML | — | — | 80% |
| KERNEL Crazy fast, open source infra for AI agents to use the Internet | AI / ML | — | — | 79% |
| PropRise The AI platform for CRE investment teams | Insurance | — | — | 79% |
| Evidently AI, Inc. Open-source monitoring for machine learning models | AI / ML | Seed | $24.7M | 78% |
| Talking Computers AI for AI Infrastructure | AI / ML | — | — | 78% |
| Sim Open source platform to build AI agent workflows | AI / ML | — | — | 78% |
| Plexe Open-source agents to build predictive ML models from a prompt | AI / ML | — | — | 78% |
See where OpenProse sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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