Companies · Gaming
San Francisco · CA, USA · Gaming · founded 2026 · https://codewisp.ai/
Diligence memoA one-page analyst read on CodeWisp — recommendation, valuation, rhythm, risks.→CodeWisp: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
CodeWisp is one of 344 Gaming companies tracked from San Francisco, CA, USA, on record since 2026. By capital raised it ranks mid-pack (ahead of 47% 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”
Anyone can create real games with AI
CodeWisp lets anyone build and publish web games using plain-English prompts. Traditional game development requires learning complex engines, managing assets and code, and following rigid workflows, making it slow and inaccessible for most people. With CodeWisp, users describe the game they want. CodeWisp generates the game’s code, structure, and assets, and users can refine it with follow-up prompts. This makes it possible to go from idea to a playable web game in minutes, without prior game-development experience.
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 344 companies in Gaming. 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 47% of sector peers (real $). Modeled value above 47% 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.
CodeWisp 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 |
|---|---|---|---|---|---|
| 13th Bridge Games Studio, Inc. | Gaming | Pre-Seed | $100K | $1.1M | same sector |
| 5x5 Gaming, Inc. | Gaming | Seed | $1.8M | $21.1M | same sector |
| 9gag | Gaming | — | — | — | same sector |
| African Game Lodge | Gaming | — | — | — | same sector |
| A-Game Beverages, Inc. | Gaming | Series B | $19.3M | $247.9M | same sector |
| Airport Game Lodge | Gaming | — | — | — | same sector |
| Allstar Gaming, Inc. | Gaming | Series A | $21.2M | $90.8M | same sector |
| Almar Exclusive Game Ranch | Gaming | — | — | — | 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 |
|---|---|---|---|---|
| Star Make games with AI - no coding required | Gaming | — | — | 77% |
| GameWisp, Inc. | Other Technology | Seed | $44.6M | 74% |
| co.dev Turn your ideas into full-stack apps | AI / ML | — | — | 73% |
| Floot The easiest way for non-coders to build apps with AI. | AI / ML | — | — | 73% |
| Codeball AI-Powered Code Review | AI / ML | — | — | 73% |
| Context.dev We give AI agents realtime web context. | AI / ML | — | — | 73% |
| MagnaPlay AI-powered game translation. | AI / ML | — | — | 72% |
| Kinect The AI-Native Commerce Stack | AI / ML | — | — | 72% |
See where CodeWisp sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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