HalluminateSummer 2025Active☆Watch⊞Compare

Halluminate is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2025. 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”

Data and RL environments to automate knowledge work

We help foundation model labs and enterprises train computer use AI with data and sandboxes. A knowledge worker spends 90% of their day on a computer. A productive AI agent must similarly learn how to use our computers, browsers, and software to deliver real world value. Today, browser and computer use AI is inaccurate, slow, and expensive. Model labs and enterprises who want to improve these agents are bottlenecked by two resources: 1) High quality datasets for benchmarking and evaluations 2) Realistic sandbox environments for safe and accurate testing/training (ex. a simulated version of Salesforce) Halluminate offers a suite of products and services to address both these issues. Our evaluation service combines proprietary datasets with high quality annotations to help our customers identify and prioritize the biggest failure modes of their computer- and browser- use AI. Our platform provides a catalog of fully managed sandboxes, empowering our customers to safely and accurately test/train at scale, resulting in improved performance. Our customers see - Improved browser- and computer-use agent performance - New and emergent frontier agent capabilities - Data driven prioritization leading to exponentially faster development speed - Increased revenue/sales via marketing from public benchmarks Our paying customers already include leading computer use model labs and the two largest browser agent companies in the space. Wyatt and Jerry are friends that met their first week of school studying CS at Cornell University. They’ve been working and studying together for 7+ years.

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.

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.

Halluminate 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.

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.

See where Halluminate sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.

If you work at Halluminate, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.