Companies · Food & Beverage
San Francisco · CA, USA · Food & Beverage · founded 2026 · https://voygr.tech
Diligence memoA one-page analyst read on VOYGR — recommendation, valuation, rhythm, risks.→VOYGR: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
VOYGR is one of 567 Food & Beverage companies tracked from San Francisco, CA, USA, on record since 2026. By capital raised it ranks in the long tail (ahead of 34% of sector peers), and in the long tail 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”
Real-world place intelligence for AI apps and agents
VOYGR is a better maps API every agent and AI app will need. Google says '4.2 stars, open till 10 – VOYGR knows the chef left, wait times doubled, and locals moved on. Our intelligent local search combines accurate place data with fresh web context – news, articles, and events. We expand beyond 10-15 map attributes into infinite, queryable place profiles, enabling agents to reason and act in the real world. Maps are no longer browsed – LLMs/agents use them to reason and act. 20M+ apps/websites, up to 40% of search queries, and 20% of LLM prompts need local context. 10-15 attributes per place are not enough for AI. Places change constantly - restaurants close, hours change, businesses move. LLMs struggle with semantic queries “Specialty coffee shops in SF with Wi-Fi, popular with YC founders”. Agents cannot reliably “parse half the internet” - they need real-world ontology We’re experienced builders in maps, search, and ML to solve this. Vlad knows this space inside and out – he worked on the Google Maps APIs GTM, plus firsthand customer experience from building in ridesharing and travel. Yarik has spent over a decade leading ML/search teams at Apple, Google, and Meta. Google Places alone is a $XXB/Yr monopoly. Agents turn it into a transactional layer opportunity for local commerce and ads.
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 567 companies in Food & Beverage. 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 34% of sector peers (real $). Modeled value above 34% 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.
VOYGR 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 |
|---|---|---|---|---|---|
| 44 on Ennis Guest Lodge and Restaurant | Food & Beverage | — | — | — | same sector |
| 4 Guys Beverage Co. | Food & Beverage | Pre-Seed | $500K | $11.5M | same sector |
| All or Nothing Brewhouse | Food & Beverage | — | — | — | same sector |
| Anna Mae's Bakery and Restaurant | Food & Beverage | — | — | — | same sector |
| Arcadia Guesthouse & Restaurant | Food & Beverage | — | — | — | same sector |
| Artisan Pizza Pies of Stillwater, LLC | Food & Beverage | Pre-Seed | $413K | $9.5M | same sector |
| Artisan's Cellar, LLC | Food & Beverage | Pre-Seed | $800K | $6.9M | same sector |
| Ashton Brewing Company | Food & Beverage | — | — | — | 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 |
|---|---|---|---|---|
| Voker The Agent Analytics Platform | AI / ML | — | — | 79% |
| Moss Real-time semantic search for Conversational AI | AI / ML | — | — | 77% |
| Cheers Win local search on ChatGPT & other LLMs | AI / ML | — | — | 76% |
| Rankai AI-Native Agency for Organic Growth (SEO & GEO) | AI / ML | — | — | 76% |
| Mireye Infrastructure for Physical World AI Agents | AI / ML | — | — | 76% |
| Intelligent Locations, Inc. | AI / ML | Seed | $124.5M | 75% |
| RefineTrain AI AI agents rewriting & optimising your internal documentation for LLMs | AI / ML | — | — | 75% |
| WorldQL Custom AI harnesses for enterprises | AI / ML | — | — | 75% |
See where VOYGR sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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