Socratix AISummer 2025Active☆Watch⊞Compare

Socratix AI 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”

AI coworkers for fraud and risk teams.

Socratix builds AI coworkers for fraud and risk teams so that humans don't have to spend their days drowning in alerts and spreadsheets. Today, fraud and risk teams at fintechs and banks are overwhelmed. Analysts jump between tools, manually stitch together context, and make judgment calls under pressure, often for hundreds of alerts a week. Too much of their work is slow, repetitive, and error-prone. We're here to change that. Socratix gives fraud and risk teams AI coworkers that investigate alerts in real time, replacing manual reviews with fast, explainable decisions. Socratix agents plug into existing workflows and reason across signals just like human analysts to help teams reduce fraud losses, minimize false positives, and scale operations without scaling headcount. We've seen this problem firsthand. At DoorDash, Riya helped save $30M+ a year by building smarter fraud detection and investigation tooling, and at Unit21, she built products for analysts that made them 3X faster. We're able to build and sell enterprise-grade software. At Cruise, Satya built scalable AI systems powering petabyte-scale data, and at LinkedIn, he built enterprise products from 0 to 1 generating $50M+ in incremental revenue. Now, the two of us are teaming up to eliminate the manual toil in fraud and risk. We're obsessed with fraud, LLMs, real-world automation, and the messy edge cases traditional ML has trouble handling. We believe that every fraud analyst should have an AI coworker - and the future of fraud will involve combining human-level reasoning with machine-speed execution.

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.

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

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