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Private Share

Prepare a support transcript for sharing: find personal details, choose what to remove, and keep the useful context.

Powered by/entities

Run the examples for free. Use your API key for your own inputs. Get a free key

Performance & cost

This page session · since page load

Ready to run
Typical delivery time
Live input tokens
0
Estimated inference cost · USD
$0.00
How these numbers work · 0 successful requests
Live model compute

Average per measured successful live request.

Successful requests
0

Run an example for free to see its measurements.

Delivery time is the median browser time for successful requests, including queue, network, and retries. Cached delivery speed is not inference speed. Model compute can exceed browser time when one request processes many inputs.

Tokens come from response headers. Inference costs use $0.04 per million input tokens, before plan credits. The estimate includes live and recorded tokens at the same rate; cached examples are not charged again. Missing usage is unavailable or a partial total (≥). Demo resets keep these totals; reload to start a new session.

Try it

Detect details in the stock transcript free, review redactions, then use your key for your own text.

Build with the SDKTypeScript · Python · dm1

Use these API building blocks in your own app. Each sample is one request from this demo’s supplied inputs. Your code owns the surrounding workflow, validation and actions.

Copied samples call the live API with MS_API_KEY and use your quota. Keep the key on your server. The free demo cache is only for this site.

Install · set your key
npm install @cloudraker/milliseconds
export MS_API_KEY="your-api-key"
example.ts
import { DecisionMachine } from "@cloudraker/milliseconds";

// Run on your server; reads MS_API_KEY from the environment.
const dm = new DecisionMachine();

const { result, usage } = await dm.entities(
  "Customer: Hi, I’m Maya Chen. The weekly export stops at 80%.\n\nAgent: I can send a reproduction to our integration vendor. Which account is affected?\n\nCustomer: My email is maya.chen@example.com and my account is NW-2048. You can call +1 202 555 0148.\n\nAgent: Thanks. We can reproduce this when the date filter is set to last month. The workaround is to export one week at a time.",
  {
    "person": "A person’s full name",
    "email": "An email address",
    "phone": "A telephone number",
    "account_id": "A customer account, order, or ticket identifier",
    "address": "A street or postal address"
  }
).withUsage();

console.log(result);
console.log({ inputTokens: usage.inputTokens, modelMs: usage.inferenceMs });

Keep the context. Review the details.

The entities endpoint looks for names, emails, phone numbers, account or order IDs, and street addresses. The demo checks every returned span against the original text before highlighting it. Your selections are replaced with literal [REDACTED] markers; overlapping selections are merged.

Detection is a starting point

Models can miss sensitive details or mark harmless text. Review the entire transcript, add missed text, and remove unnecessary selections before exporting. This demo does not certify that text is anonymous. Live detection sends the transcript to the milliseconds API; sample review and manual redactions run in your browser.