Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://inth.com
Diligence memoA one-page analyst read on Inth — recommendation, valuation, rhythm, risks.→Inth: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Inth 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”
Your AI Native Compliance team
Inth is the AI-native compliance team for companies that ship fast. Privacy compliance still lives in dashboards, questionnaires, and policy docs, but the real risk is in the codebase and runtime. Consent breaks in code. Data deletion breaks in code. Vendors, logs, AI systems, and agent-written changes all move user data in code. Inth starts with c15t, an open-source consent and privacy SDK with 3M+ npm downloads, used by teams like Zed, Infisical, and Expo. From there, Inth expands into code-native workflows for consent management, data subject requests, policy enforcement, vendor oversight, and audit-ready evidence. We make compliance programmable, observable, and compliant by default.
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.
Inth 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 |
|---|---|---|---|---|
| compliant-llm Detect every data leak into third-party GenAI tools | AI / ML | — | — | 76% |
| Agency Agency replaces traditional security and compliance headcount with AI. | Professional Services | — | — | 75% |
| Arctic Health AI-native credentialing and contracting for healthcare | AI / ML | — | — | 75% |
| CodeStory Aide is an AI-native , privacy-first IDE built on top of VSCode | AI / ML | — | — | 75% |
| Snyk Ltd Snyk is the AI Security Fabric — the independent validator that makes AI-generated code, AI agents, and AI-native applications trustworthy. Unleash AI innovation securely. | AI / ML | Series D+ | $3.9B | 75% |
| Dynamo AI Compliant-Ready AI for the Enterprise | AI / ML | — | — | 74% |
| Alter Secure access control and authorization platform for agent workflows | AI / ML | — | — | 74% |
| Second AI-Native Enterprise Codebase Maintenance | AI / ML | — | — | 73% |
See where Inth sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
If you work at Inth, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.
Is this your company? Update your profile or add contact details — and choose exactly who can reach you. Reviewed before anything is published.