Tinfoil GTM brain

category

market profile

• text: Sensitive AI applications need scalable inference while limiting infrastructure providers' access to confidential prompts and workloads.
• text: The confidential inference market includes several hardware-attested API providers with differing architectures and verification evidence.
• text: Legal AI, consumer assistants, research and enterprise AI are represented in Tinfoil's published customer stories.

personas

• title: AI application engineer
  pains:

  • Sensitive prompts must reach useful models without being visible to an inference provider
  • Building enclave infrastructure and verifying each connection adds engineering work
• title: Security or privacy leader
  pains:

  • Contractual no-retention promises alone can be hard to verify
  • Needs evidence of what code and hardware handle customer data
• title: Infrastructure lead for regulated AI
  pains:

  • Private GPU clusters require expensive capacity and operations
  • Custom workloads need verifiable isolation beyond a standard inference endpoint

icp

• text: AI application vendors handling confidential conversations, legal material or proprietary enterprise data, with engineering capacity to integrate an API.
• text: Security research and ML teams that need custom Docker workloads in attested environments; organization size is not specified.

message pillars

• text: Combine local-style privacy with cloud capacity.
• text: Make security claims verifiable.
• text: Fit an existing inference workflow.
• text: Secure custom container workloads.

objections

• objection: Can we independently verify the privacy boundary?
  response: Review hardware attestation, SDK verification, open code and the service's documented limitations against your threat model.
• objection: Will integration require a rebuild?
  response: The inference API is OpenAI-compatible; test model behavior, performance and the verifying SDK for the intended workflow.
• objection: How predictable is spending?
  response: API inference is metered by tokens, containers by resources and time, with a monthly GPU commitment and separately billed persistent storage.

competitors

• name: NEAR AI
  why: Offers private verifiable AI inference using confidential computing.
• name: Enclave42
  why: Offers hardware-attested confidential inference through an OpenAI-compatible interface.
• name: Privatemode
  why: Offers confidential AI inference for privacy-sensitive workloads.

keywords

• confidential AI inference
• private LLM API
• GPU confidential computing
• verifiable AI privacy
• secure enclave Docker
• OpenAI compatible private inference

voice

• Technical
• Privacy-focused
• Assured

signals

• text: An AI vendor is selling to regulated enterprises that require evidence of how sensitive data is processed.
• text: A team is considering local GPUs to process confidential inference workloads.

stack

• Docker
• OpenAI-compatible API
• NVIDIA confidential computing
• secure enclaves

pricing

motion

channels

• documentation
• customer stories
• research collaborations
• developer self-service

confidence

Founders

• name: Tanya Verma
  title: Founder/CEO
  role: CEO
  bio: Co-founder @ Tinfoil | Systems, Cryptography and AI @ Cloudflare | CubeSats & CS @ UIUC
• name: Jules Drean
  title: Founder
  role: Founder
  bio: Jules has an MIT PhD in secure hardware and confidential computing. He has industry experience working at Microsoft Research and NVIDIA on the technologies underlying Tinfoil. During his PhD, Jules has built secure enclaves from the ground up and studied how to attack and defend these systems against advance microarchitectural attacks. His work also looked at using trusted hardware to securely deploy advanced cryptographic primitives such as FHE and MPC.
• name: Sacha Servan-Schreiber
  title: Founder
  role: Founder
  bio: Co-founder of Tinfoil | PhD at MIT in cryptography Personal website: sachaservanschreiber.com