Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://foaster.ai
Diligence memoA one-page analyst read on Foaster — recommendation, valuation, rhythm, risks.→Foaster: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Foaster 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”
The AI-native McKinsey
Foaster is building the AI-native McKinsey, starting with AI transformation. We deploy AI agents that interview a company's employees at scale, map how work actually gets done, identify bottlenecks and AI opportunities, and draft a transformation roadmap in just 2 weeks. Expert consultants review the final outputs, add judgment where it matters, and feed their corrections back into the system. We believe current AI models already have the capabilities required to automate most of the work consultants do. But traditional consulting firms use them to improve the old model: helping consultants work faster. Foaster is instead rebuilding a new model from scratch, where agents do the work that needs scale and humans review the final outputs, bring expertise, and improve the system overtime. We are starting with AI transformation, then progressively expanding to more and more consulting projects using the same approach. Every mission creates structured knowledge from interviews, workflow maps, recommendations, and expert corrections, so the system gets better with every client engagement. The $500B consulting market will eventually be run by our AI agents and a small number of expert humans.
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.
Foaster 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 |
|---|---|---|---|---|
| Wordware AI agents you can rely on | AI / ML | — | — | 79% |
| Risely AI AI agents that automate administrative work across college campuses. | AI / ML | — | — | 78% |
| Hessian Forward-deployed AI Agents | AI / ML | — | — | 77% |
| Imagine AI We reverse engineer B2B growth, starting with LinkedIn. | Insurance | — | — | 77% |
| throxy Vertical AI agents that run the sales funnel on autopilot | Fintech | — | — | 77% |
| RefineTrain AI AI agents rewriting & optimising your internal documentation for LLMs | AI / ML | — | — | 77% |
| Chamber The AIOps Agent for ML Teams | AI / ML | — | — | 76% |
| Concourse AI Agents for corporate finance teams | Fintech | — | — | 76% |
See where Foaster sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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