Beetony
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Every AI decision,
sealed and HIPAA-defensible.

You keep calling your own models. Beetony seals the record, so you hand a regulator receipts, not screenshots.

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Append-only audit trail Consent ledger built in 14-day trial, no card
beetony.com/audit / aud_8f3c2e91
Consent verifiedSealed
Audit IDaud_8f3c2e91d6b4a702·2026-05-24 14:32:08 UTC
AI Inputjson
{
  "context": "diagnosis_summary",
  "patient_id": "pt_7a9e",
  "prompt": "Summarize the patient's last visit
             notes for the referring physician."
}
AI Outputtext
Patient presents continued chest discomfort
following the April procedure. Recommend
follow-up echocardiogram within 7 days.
No new medications. Risk stratification: low.
Plain-English Explanation

Intake call logged for patient pt_a1b2c3. No consent record was found for this patient and context. Record sealed and flagged for review.

Sealed
3b8a91c7e02f4…7c5e9f01
SHA-256 · sealed 0.4s after write
Consent
diagnosis_summaryroutingsummary
Granted 2025-11-04 by patient pt_7a9e
Built for health-tech

For teams shipping AI to clinicians and patients who need sealed records, consent cover, and a vault they can hand a regulator.

Three pillars

Record, govern, prove.
A witness next to your models, not a proxy in front of them.

Audit Vault

Immutable log of every sealed AI call

You POST input, output, context, and an opaque patient token after your model returns. Beetony stores the evidence, evaluates consent, and seals the row with a SHA-256 hash in an append-only vault you can export for auditors.

POST /v1/log
Write path
SHA-256
Seal

Bias Monitoring

Uneven patterns across sealed traffic

Over a rolling window, Beetony flags consent bias, coverage skew, volume spikes, output-shape drift, and patient concentration. Pattern signals on how AI is used and consented, not a per-call fairness verdict on the model answer.

30 days
Window
6 types
Signals

Consent Ledger

Consent state next to every sealed call

Maintain patient consent records by context. Each /v1/log is checked against the ledger and the caller's attestation, then stored as verified, flagged, or missing so compliance can see exactly where AI ran without consent cover.

3 states
Verdicts
Per context
Scope
How it works

Your AI stays yours. Beetony seals the record.

Call OpenAI, Grok, Gemini, or anything else the way you already do. After the model returns, your backend sends one POST /v1/log to Beetony. We check consent, store input and output, and seal the row. We do not sit in front of your model.

1. Your product
Backend route, server action, agent
// Call your model as usual
const out = await grok.chat({
  messages: [{ role: "user", content: notes }],
})
your AI call
2. Your model
Grok, OpenAI, Anthropic, self-hosted
Beetony never proxies this hop. Prompts and latency stay between you and your provider.
then POST /v1/log
3. Beetony
Witness · vault · consent · bias signals
await fetch(`${BEETONY}/v1/log`, {
  method: "POST",
  headers: {
    Authorization: `Bearer ${KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    patient_id: "pt_opaque",
    context_tag: "visit_summary",
    ai_input: notes,
    ai_output: out,
    consent_verified: true,
  }),
})
Evaluate consent against the ledger
Store input, output, context, patient token
Seal with SHA-256 (append-only vault)
Plain-English explanation on every record
Bias signals run on sealed traffic over time
MCP

Beetony speaks Model Context Protocol natively.

Hosted MCP at www.beetony.com/mcp. Point Claude, Cursor, Codex, or an internal copilot at it with a Beetony API key so agents can seal calls into the vault, read consent, and search audit records. Your models still run in your stack; Beetony receives the seal after the call.

  • Runtime tools. Agents call beetony_log, beetony_consent_*, beetony_audit_*, and beetony_usage inline.
  • Integration help. Resources serve docs and framework snippets (plain HTTP helpers). Agents wire sealing into any codebase.
  • Workflows. Prompts walk an agent through setup, sealing AI calls after they return, and auditing a project's wiring.
Claude Desktop config (example)
~/.claude_desktop_config.json
{
  "mcpServers": {
    "beetony": {
      "url": "https://www.beetony.com/mcp",
      "headers": {
        "Authorization": "Bearer cba_live_..."
      }
    }
  }
}
Same JSON shape works in Claude Code, Cursor, and VS Code MCP extensions. On macOS Desktop, the file often lives under Application Support/Claude/.
Why the vault exists
When a regulator asks how your AI decided something for a patient, the answer should be a sealed row: input, output, consent, hash, timestamp. Not a Slack thread and a hope.
Export the audit log, sample hashes, and show consent verdicts. That is the review path Beetony is built for.
Pricing

Priced per sealed interaction.
Every sealed record is hashed and exportable.

Trial
$0for 14 days

Kick the tires. Real vault, real consent, real bias signals.

  • Up to 1,000 sealed calls / mo
  • Live + test environments
  • Audit export
  • Email support
Start trial
Starter
$499/ month

Small teams shipping their first clinical-AI feature.

  • Up to 50,000 sealed calls / mo
  • 1-year retention
  • Live + test environments
  • Email support
Start trial
Most teams
Growth
$1,900/ month

Production AI across multiple product surfaces.

  • Up to 250,000 sealed calls / mo
  • 3-year retention
  • Audit exports, role-based access
  • Webhooks + MCP
  • Priority support
Start trial
Enterprise
Customannual contract

Hospitals, networks, multi-region deployments.

  • Unlimited sealed calls
  • Custom retention
  • Dedicated support channel
  • Security review support
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