Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://www.oddpool.com
Diligence memoA one-page analyst read on Oddpool — recommendation, valuation, rhythm, risks.→Oddpool: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Oddpool is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, 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”
Institutional infrastructure for prediction markets
Oddpool is a platform that helps quant funds and trading desks harness the power of prediction markets. We make it easy to access, search, and analyze every venue from one place. Avi and Ritesh have been roommates since freshman year at Georgia Tech. Avi has a Master's in ML and saved Microsoft $6M/year by building a more accurate model on half the hardware. Ritesh has a Master's in Distributed Systems and made buy-side trading systems at Bloomberg 40% faster. We started trading prediction markets ourselves but couldn't backtest our strategies because the tooling for this emerging asset class didn't exist. The deeper we went, the more we saw that fragmentation between prediction markets and lack of clean data was going to be a big problem. We launched a prosumer tier and grew to several thousand dollars in MRR within months. Now we're bringing it to institutions, building tooling with the rigor and depth serious desks demand. They're the fastest growing asset class in finance, and CFTC applications for new exchanges keep climbing. Each new venue makes the market bigger, the fragmentation worse, and the unified data layer more valuable. It doesn't exist yet. We're building 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.
Oddpool 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 |
|---|---|---|---|---|
| Alt-X Building the best venue for private markets exposure. | Fintech | — | — | 79% |
| Scalar Field Your Agentic Trading Desk — Building the next era of agentic… | Fintech | — | — | 78% |
| Aqua The AI-Native Turnkey Alternative Investment Platform (TAIP) | Fintech | — | — | 77% |
| goodfin Building the next generation of wealth | Fintech | — | — | 77% |
| Centauri AI The Modern ETL and Data Science Platform for Finance | Fintech | — | — | 77% |
| Totalis derivative layer for prediction markets | Fintech | — | — | 76% |
| WithAI Agent harnesses for asset managers | AI / ML | — | — | 76% |
| ValCtrl ValCtrl is building the only intelligent prediction market | Fintech | — | — | 76% |
See where Oddpool sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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