# US Brand Offer Evidence Normalizer (`zinin/us-brand-offer-evidence-normalizer`) Actor

Normalize supplied US-brand offer evidence into verified, deterministic machine-readable rows.

- **URL**: https://apify.com/zinin/us-brand-offer-evidence-normalizer.md
- **Developed by:** [Tim Zinin](https://apify.com/zinin) (community)
- **Categories:** MCP servers, Automation, Marketing
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## US Brand Offer Evidence Normalizer

Turn supplied US brand ad, offer, coupon, and landing-page evidence into closed,
deterministic JSON for research, marketing operations, and automated workflows.
Send inline evidence rows or one authorized Apify Dataset. The Actor validates
source proof and terms; it does not browse, render pages, use proxies, call
other Actors, or infer ownership from a name, ticker, slug, or free text.

### What you send

Use exactly one source: `rows` or `datasetId`.

| Field | Required | What it means |
|---|---:|---|
| `schemaVersion` | yes | `1.0` for this input contract. |
| `requestId` | yes | A bounded correlation ID. Reusing it in a later run does not deduplicate or combine purchases. |
| `rows` | one source | One to 100 closed evidence rows supplied in the request. |
| `datasetId` | one source | One Apify Dataset selected with `READ` permission. V1 reads at most 10 rows and probes one additional row to detect truncation. |
| `options` | no | `labels: off` by default, or caller-funded BYOK labels with an explicit model. `maxItems` is 1 to 100. |
| `openrouterApiKey` | BYOK only | Your own OpenRouter key when `labels` is `byok`. It is never returned or persisted. |

This is a complete synthetic inline request that can be pasted into the input
editor. It uses a first-party-domain proof so the row is eligible for a result.

```json
{
  "schemaVersion": "1.0",
  "requestId": "offer-normalize-demo-001",
  "rows": [
    {
      "entity": {
        "entityId": "brand:demo-outfitters",
        "brandName": "Demo Outfitters",
        "legalName": null,
        "domain": "demo.example",
        "ticker": null,
        "aliases": [],
        "country": "US",
        "sourceEntityIds": ["prefill:demo-outfitters"]
      },
      "evidence": {
        "evidenceType": "landing_page",
        "sourceName": "synthetic-example",
        "sourceRecordId": "offer-001",
        "sourceUrl": "https://demo.example/offers/spring",
        "observedAt": "2026-08-04T08:00:00Z",
        "rawTitle": "Spring offer",
        "rawTermsText": "20% off orders $50+ with code SPRING20. Ends 2026-09-30.",
        "terms": {
          "offerKind": "percent_off",
          "percentOff": 20,
          "minPurchaseAmount": {"value": 50, "currency": "USD"},
          "couponCode": "SPRING20",
          "endAt": "2026-09-30T23:59:59Z"
        },
        "entityAttribution": {
          "status": "verified_by_source",
          "proofType": "first_party_domain",
          "proofValue": "demo.example",
          "sourceEntityId": "prefill:demo-outfitters",
          "proofUrl": "https://demo.example/",
          "proofDomain": "demo.example",
          "merchantId": null,
          "adAccountId": null,
          "officialSourceId": null
        },
        "additionalUrls": []
      }
    }
  ],
  "options": {"labels": "off", "maxItems": 100}
}
```

For Dataset input, omit `rows` and send `datasetId` instead. The Dataset must be
authorized for this run; arbitrary URLs, pagination, hosts, tokens, proxy
settings, and source credentials are not accepted.

### What you get

Each fully verified, non-conflicting offer becomes one paid Dataset item. The
row preserves source evidence and includes normalized terms, a deterministic
`offerId`, `offerKind`, `dedupeKey`, attribution proof, conflict information,
and `needsReview`. Ambiguous, conflicting, rejected, or unavailable evidence is
reported in the non-paid `OUTPUT` summary and is not emitted as a paid fact.

Every run also writes a compact closed `OUTPUT` record. A successful run has the
following shape; the Dataset item contains the detailed evidence row.

```json
{
  "schemaVersion": "1.0",
  "requestId": "offer-normalize-demo-001",
  "requestDigest": "sha256:2d5173a6d8d0c18d2d8aa0de1dd7afcdb4f35f7c70b2e7d44d5ce0f9279f9887",
  "status": "found",
  "resultFound": true,
  "counts": {"input": 1, "deduped": 1, "verified": 1, "eligible": 1, "rejected": 0},
  "delivery": {"confirmedDatasetWrites": 1, "confirmedResultEvents": 1, "confirmedCharges": 1, "unknownOperations": 0, "lastReceiptId": "example-receipt"},
  "source": {"kind": "inline", "datasetId": null},
  "limits": {"maxItems": 100, "truncated": false},
  "analysisStatus": "skipped_no_key",
  "analysisModel": null,
  "usage": null,
  "sourceStats": {"datasetRequests": 0, "decodedBytes": 0, "elapsedMs": 12},
  "errors": []
}
```

The machine flow is: submit one closed JSON request, inspect `OUTPUT`, then
consume only Dataset items with verified attribution and complete, non-conflicting
normalization. Terminal statuses include `found`, `no_match`, `invalid_input`,
`dataset_not_found`, `dataset_forbidden`, `dataset_truncated`, `source_unavailable`,
`partial`, `budget_stopped`, `pricing_misconfigured`, and `unknown_delivery`.

### API example

After the Actor is available in your Apify account, call it with the same JSON
object through the standard Apify API:

```bash
curl -sS -X POST \
  'https://api.apify.com/v2/acts/zinin~us-brand-offer-evidence-normalizer/runs?waitForFinish=60' \
  -H 'Authorization: Bearer YOUR_APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  --data @input.json
```

An Apify client or an agent can use the same closed input and output contracts.
The request ID is correlation metadata only: each separate Actor run is a new
purchase, and V1 has no cross-run replay, ledger, or idempotency state.

### Verification and limits

- Attribution requires source-specific proof, a source entity ID, original source
  fields, and exact comparable domain or identity agreement.
- Structured terms must agree with raw terms. A conflict is retained and is not
  billed as a verified result.
- Inline input is limited to 100 rows; a Dataset source is limited to 10 rows.
- Canonical serialized input is limited to 512 KiB; decoded Dataset data is
  limited to 2 MiB; row, URL, evidence-link, and text limits are enforced before
  delivery.
- One bounded BYOK request can label at most 10 rows. There is no model fallback,
  no browser, no login, no arbitrary URL fetch, and no cross-run state.
- A delivered result is charged only after the Actor confirms the exact platform
  event counter delta. Uncertain delivery stops remaining work and is never
  silently retried.

### Pricing and discounts

Apify resolves pricing from the caller's subscription tier before the run and
supplies the matching event-price pair to the SDK. The contract charges that
tier's run-start event plus one `result-found` event for each fully delivered
verified, non-conflicting row. There is no input-row, Dataset-item, rejection,
deduplication, or BYOK charge.

| Apify subscription tier | Discount | Run start | `result-found` |
|---|---:|---:|---:|
| FREE | 0% | $0.00500 | $0.01000 |
| BRONZE | 5% | $0.00475 | $0.00950 |
| SILVER | 10% | $0.00450 | $0.00900 |
| GOLD | 15% | $0.00425 | $0.00850 |
| PLATINUM | 18% | $0.00410 | $0.00820 |
| DIAMOND | 20% | $0.00400 | $0.00800 |

Only these six exact start/result pairs are accepted. Mixed pairs, missing or
extra event names, non-finite values, and any
`apify-default-dataset-item` event fail closed as `pricing_misconfigured`. The
result event is emitted only with the corresponding delivered row.

The per-run `maxItems` option only caps how many rows this run may return. It is
independent of the caller's subscription tier and never selects a price tier.

### Bring your own key (BYOK)

Set `options.labels` to `byok`, provide an explicit OpenRouter model, and supply
your own `openrouterApiKey` when you want optional `offerCategory` and
`audienceLabel` values. Your key pays the model provider directly. The key is
redacted from logs, output, persisted state, and request digests. Provider
errors, timeouts, malformed responses, and bounded limits leave the deterministic
offer facts, proof, deduplication, and billing eligibility unchanged. With
`labels: off`, no model or key is used.

### Machine and agent usage

The Actor is designed as a small machine-to-machine processor: closed JSON
schemas, deterministic identifiers, bounded execution, explicit terminal
statuses, and a one-row result billing unit make it suitable for scheduled jobs,
MCP wrappers, and autonomous research or marketing agents. The metadata is
machine-first, but this package does not promise a separate MCP server, x402
endpoint, payment facilitator, or catalog registration.

### Related Actors

| Actor | How it fits | Availability in this series |
|---|---|---|
| [Shopify Store Intelligence](https://apify.com/zinin/shopify-store-intelligence) | Add public Shopify store and catalog signals before normalizing a merchant's supplied offer evidence. | Public |
| [Shopify Price Change Monitor](https://apify.com/zinin/shopify-price-change-monitor) | Pair offer evidence with observed store-level price and catalog changes for competitor monitoring. | Public |
| [Structured Extract](https://apify.com/zinin/structured-extract) | Extract typed page fields and JSON-LD from a landing page before passing its evidence to this Actor. | Public |
| [Intent Signal Aggregator](https://apify.com/zinin/intent-signal-aggregator) | Add public hiring and news intent context when prioritizing normalized brand offers. | Public |
| [Social Preview Checker](https://apify.com/zinin/social-preview-checker) | Check the social metadata of a landing page whose offer evidence is being evaluated. | Public |

These links are public neighboring tools, not required sibling calls. A downstream
workflow may join their output with this Actor's closed offer facts without assuming
that any Actor runs another Actor internally.

### Store text

**SEO title:** US Brand Offer Evidence Normalizer | Verified Offer Evidence

**Meta description:** Normalize supplied US brand ads, offers, coupons, and
landing-page evidence into deterministic, attribution-aware JSON without
browsing or sibling Actor calls.

**Keywords:** offer evidence normalization, coupon normalization, brand offer
extraction, verified attribution, Apify Dataset processor, BYOK labels

# Actor input Schema

## `schemaVersion` (type: `string`):

Required V1 contract version.

## `requestId` (type: `string`):

Correlation ID for this run. Reusing it in another run does not deduplicate purchases.

## `rows` (type: `array`):

One to 100 closed evidence-row objects. Leave empty when selecting a Dataset.

## `datasetId` (type: `string`):

One resource selected with READ permission. Dataset V1 accepts at most 10 rows and probes 11 in one SDK call. Leave empty when using inline rows.

## `openrouterApiKey` (type: `string`):

Optional caller-owned key for BYOK labels. The runtime must redact and never persist it.

## `options` (type: `object`):

Labels are off unless an explicit model and caller key are supplied in BYOK mode.

## Actor input object example

```json
{
  "schemaVersion": "1.0",
  "requestId": "offer-normalize-20260804-001",
  "rows": [
    {
      "entity": {
        "entityId": "brand:demo-outfitters",
        "brandName": "Demo Outfitters",
        "legalName": null,
        "domain": "demo.example",
        "ticker": null,
        "aliases": [],
        "country": "US",
        "sourceEntityIds": [
          "prefill:demo-outfitters"
        ]
      },
      "evidence": {
        "evidenceType": "landing_page",
        "sourceName": "demo_fixture",
        "sourceRecordId": "offer-001",
        "sourceUrl": "https://demo.example/offers/spring",
        "observedAt": "2026-08-04T08:00:00Z",
        "rawTitle": "Spring offer",
        "rawTermsText": "20% off orders $50+ with code SPRING20. Ends 2026-09-30.",
        "terms": {
          "offerKind": "percent_off",
          "percentOff": 20,
          "minPurchaseAmount": {
            "value": 50,
            "currency": "USD"
          },
          "couponCode": "SPRING20",
          "endAt": "2026-09-30T23:59:59Z"
        },
        "entityAttribution": {
          "status": "verified_by_source",
          "proofType": "first_party_domain",
          "proofValue": "demo.example",
          "sourceEntityId": "prefill:demo-outfitters",
          "proofUrl": "https://demo.example/",
          "proofDomain": "demo.example",
          "merchantId": null,
          "adAccountId": null,
          "officialSourceId": null
        },
        "additionalUrls": []
      }
    }
  ],
  "options": {
    "labels": "off",
    "maxItems": 100
  }
}
```

# Actor output Schema

## `OUTPUT` (type: `string`):

API link to the closed OUTPUT KVS envelope.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "schemaVersion": "1.0",
    "requestId": "offer-normalize-20260804-001",
    "rows": [
        {
            "entity": {
                "entityId": "brand:demo-outfitters",
                "brandName": "Demo Outfitters",
                "legalName": null,
                "domain": "demo.example",
                "ticker": null,
                "aliases": [],
                "country": "US",
                "sourceEntityIds": [
                    "prefill:demo-outfitters"
                ]
            },
            "evidence": {
                "evidenceType": "landing_page",
                "sourceName": "demo_fixture",
                "sourceRecordId": "offer-001",
                "sourceUrl": "https://demo.example/offers/spring",
                "observedAt": "2026-08-04T08:00:00Z",
                "rawTitle": "Spring offer",
                "rawTermsText": "20% off orders $50+ with code SPRING20. Ends 2026-09-30.",
                "terms": {
                    "offerKind": "percent_off",
                    "percentOff": 20,
                    "minPurchaseAmount": {
                        "value": 50,
                        "currency": "USD"
                    },
                    "couponCode": "SPRING20",
                    "endAt": "2026-09-30T23:59:59Z"
                },
                "entityAttribution": {
                    "status": "verified_by_source",
                    "proofType": "first_party_domain",
                    "proofValue": "demo.example",
                    "sourceEntityId": "prefill:demo-outfitters",
                    "proofUrl": "https://demo.example/",
                    "proofDomain": "demo.example",
                    "merchantId": null,
                    "adAccountId": null,
                    "officialSourceId": null
                },
                "additionalUrls": []
            }
        }
    ],
    "options": {
        "labels": "off",
        "maxItems": 100
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/us-brand-offer-evidence-normalizer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "schemaVersion": "1.0",
    "requestId": "offer-normalize-20260804-001",
    "rows": [{
            "entity": {
                "entityId": "brand:demo-outfitters",
                "brandName": "Demo Outfitters",
                "legalName": None,
                "domain": "demo.example",
                "ticker": None,
                "aliases": [],
                "country": "US",
                "sourceEntityIds": ["prefill:demo-outfitters"],
            },
            "evidence": {
                "evidenceType": "landing_page",
                "sourceName": "demo_fixture",
                "sourceRecordId": "offer-001",
                "sourceUrl": "https://demo.example/offers/spring",
                "observedAt": "2026-08-04T08:00:00Z",
                "rawTitle": "Spring offer",
                "rawTermsText": "20% off orders $50+ with code SPRING20. Ends 2026-09-30.",
                "terms": {
                    "offerKind": "percent_off",
                    "percentOff": 20,
                    "minPurchaseAmount": {
                        "value": 50,
                        "currency": "USD",
                    },
                    "couponCode": "SPRING20",
                    "endAt": "2026-09-30T23:59:59Z",
                },
                "entityAttribution": {
                    "status": "verified_by_source",
                    "proofType": "first_party_domain",
                    "proofValue": "demo.example",
                    "sourceEntityId": "prefill:demo-outfitters",
                    "proofUrl": "https://demo.example/",
                    "proofDomain": "demo.example",
                    "merchantId": None,
                    "adAccountId": None,
                    "officialSourceId": None,
                },
                "additionalUrls": [],
            },
        }],
    "options": {
        "labels": "off",
        "maxItems": 100,
    },
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/us-brand-offer-evidence-normalizer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "schemaVersion": "1.0",
  "requestId": "offer-normalize-20260804-001",
  "rows": [
    {
      "entity": {
        "entityId": "brand:demo-outfitters",
        "brandName": "Demo Outfitters",
        "legalName": null,
        "domain": "demo.example",
        "ticker": null,
        "aliases": [],
        "country": "US",
        "sourceEntityIds": [
          "prefill:demo-outfitters"
        ]
      },
      "evidence": {
        "evidenceType": "landing_page",
        "sourceName": "demo_fixture",
        "sourceRecordId": "offer-001",
        "sourceUrl": "https://demo.example/offers/spring",
        "observedAt": "2026-08-04T08:00:00Z",
        "rawTitle": "Spring offer",
        "rawTermsText": "20% off orders $50+ with code SPRING20. Ends 2026-09-30.",
        "terms": {
          "offerKind": "percent_off",
          "percentOff": 20,
          "minPurchaseAmount": {
            "value": 50,
            "currency": "USD"
          },
          "couponCode": "SPRING20",
          "endAt": "2026-09-30T23:59:59Z"
        },
        "entityAttribution": {
          "status": "verified_by_source",
          "proofType": "first_party_domain",
          "proofValue": "demo.example",
          "sourceEntityId": "prefill:demo-outfitters",
          "proofUrl": "https://demo.example/",
          "proofDomain": "demo.example",
          "merchantId": null,
          "adAccountId": null,
          "officialSourceId": null
        },
        "additionalUrls": []
      }
    }
  ],
  "options": {
    "labels": "off",
    "maxItems": 100
  }
}' |
apify call zinin/us-brand-offer-evidence-normalizer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=zinin/us-brand-offer-evidence-normalizer",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WwMiW0TKaE0WQC2jg/builds/YblzxHduCD2mtSX8K/openapi.json