TinfoilSpring 2025Active☆Watch⊞Compare

Tinfoil 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”

Encrypted AI with verifiable privacy

Tinfoil makes it easy to make your AI workloads secure and provably private. You get the privacy of on-prem deployments, while running on the cloud. It's like end-to-end encrypted messaging but for your AI applications. We’ve built a full-stack platform on top of the latest NVIDIA GPUs offering confidential computing capabilities, meaning that you don’t have to trade off performance for privacy. We integrate a suite of recent advances in secure hardware technologies, in particular NVIDIA’s confidential compute mode available on Hopper and Blackwell. When combined with Tinfoil’s software stack, companies can prove their security claims like "we can't see and don’t log your queries.” Tinfoil guarantees that all data always stays private and cannot be accessed by anyone other than the end user -- not even by Tinfoil or the cloud provider it’s processed on. Everything can be made end-to-end encrypted and private with Tinfoil. The founding team combines deep academic and industry experience in security and Internet protocols. Tanya was previously a systems engineer at Cloudflare, where she built Internet security protocols used by billions, and contributed to the Workers AI platform. Jules and Sacha hold PhDs from MIT, where they worked on secure hardware, cryptography, and privacy technologies. Jules has also worked at NVIDIA on their confidential computing team. Tinfoil was born from our personal frustration with the false choice between access to powerful AI and the massive data privacy implications of deeply integrated AI. AI is a universal tool that becomes part of our personal lives and business workflows. In the process, it needs access to all personal, private, and proprietary data. Right now, the only solutions are data processing agreements (i.e., "pinky promises"), band-aids like PII redaction that don't work in practice, or AI locally/on-prem, which doesn't scale. Tinfoil promises to unlock significantly deeper AI adoption and integrations by making it easy to obtain true privacy, just as TLS on the Internet enabled e-commerce to flourish by securing credit cards on the network. Today we offer several self-serve products: - A consumer-facing chatbot that keeps your chats end-to-end encrypted and provides zero-access inference with powerful open-source models like Kimi, Gemma, and GLM. You can try it out for free: https://chat.tinfoil.sh - A developer-friendly API that allows you to build AI applications where all user data is kept private and we as the inference provider cannot see any of the prompts. Our SDKs make sure that all relevant security measures are checked for you before even sending a single byte of data. Learn more:https://docs.tinfoil.sh - An advanced confidential computing platform we call Tinfoil Containers that allows you to secure any Docker application. We make it easy to deploy your own logic and applications inside secure enclaves and prove to the world what code is running inside. This enables attested workloads and publicly verifiable privacy policies for sensitive applications. Learn more: https://tinfoil.sh/containers

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.

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

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