Pricing

from $8.50 / 1,000 delivered deterministic evidence chronology reports

Auditable Competitive Evidence Timeline Builder

Turn 1–30 buyer-supplied brand evidence rows into one deterministic, digest-verifiable chronology with stable ordering, timestamp warnings, source links, gaps, and a manual-review action. Offline: no source fetch or identity, truth, rights, or legal verification. One paid report per run.

Pricing

from $8.50 / 1,000 delivered deterministic evidence chronology reports

Rating

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Developer

Tim Zinin

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Monthly active users

16 days ago

Last modified

Categories

Automation

Marketing

You can access the Auditable Competitive Evidence Timeline Builder programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9    "schemaVersion": "1.0",
10    "asOf": "2026-08-05T09:00:00Z",
11    "rows": [
12        {
13            "evidenceId": "nike-offer-001",
14            "eventType": "offer",
15            "effectiveAt": "2026-08-04T12:00:00Z",
16            "observedAt": "2026-08-04T12:30:00Z",
17            "sourceUrl": "https://www.nike.com/news/example",
18            "fact": "A submitted source reports a US offer change.",
19        },
20        {
21            "evidenceId": "nike-award-001",
22            "eventType": "award",
23            "effectiveAt": "2026-08-04T12:00:00Z",
24            "observedAt": "2026-08-04T13:00:00Z",
25            "sourceUrl": "https://www.usaspending.gov/award/example",
26            "fact": "A submitted source lists an award associated with the brand.",
27        },
28    ],
29}
30
31# Run the Actor and wait for it to finish
32run = client.actor("zinin/us-brand-evidence-chronology-builder").call(run_input=run_input)
33
34# Fetch and print Actor results from the run's dataset (if there are any)
35print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
36for item in client.dataset(run.default_dataset_id).iterate_items():
37    print(item)
38
39# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Brand Evidence Timeline & Chronology Builder API in Python

The Apify API client for Python is the official library that allows you to use Auditable Competitive Evidence Timeline Builder API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Auditable Competitive Evidence Timeline Builder API in JavaScript

Auditable Competitive Evidence Timeline Builder API through CLI

Auditable Competitive Evidence Timeline Builder OpenAPI definition

Auditable Competitive Evidence Timeline Builder API

Competitive Intelligence Summary by Brand

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Summarize buyer-supplied signal rows by brand in one deterministic report. Preserve event, severity, status, and recorded-source counts with stable digests, evidence gaps, and a human review action. No source fetching or external verification.

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Competitive Evidence Snapshot Change Detector

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Compare two buyer-supplied evidence snapshots in one deterministic report. Separate added, removed, changed, and unchanged records with stable digests, evidence gaps, human review actions, and current-run billing truth. No source fetching.

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Turn 1–50 buyer-supplied signal rows into one deterministic severity brief. Get complete CRITICAL/HIGH/MEDIUM/LOW sections, ordered projections, stable digests, explicit evidence gaps, and a human review action without source fetching or external verification.

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Turn buyer-authorized signal rows into one deterministic, prioritized human-review queue. Retain conflicts, rule traces, evidence gaps, owner routing, stable digests, and current-run billing truth. No source fetching or autonomous action.

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Turn 1–100 buyer-supplied brand evidence rows into one deterministic citation-table report with preserved fields and HTTPS URLs, bounded Markdown, source and host counts, verification gaps, and SHA-256 digests. Sources are never fetched or verified; run start is separate.

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Group buyer-supplied signal rows by brand, event type, severity, or analysis status. Get one deterministic grouping report with complete counts, stable digests, evidence gaps, and a human review action. It does not create separate Datasets.

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Aggregate buyer-supplied signal rows into one deterministic metrics report. Get complete closed-enum counts, source totals, integer confidence basis points, review warnings, evidence gaps, and a human action without fetching external sources.

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Turn buyer-supplied normalized signals and ordered rules into one deterministic routing plan. Preserve matched routes, unmatched signals, warnings, stable digests, evidence gaps, human review action, and current-run billing truth without contacting destinations.

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Brand Signal Co-Occurrence Analysis Tool

zinin/us-brand-signal-cooccurrence-analyzer

Count which opaque signals appear together across buyer-supplied groups. Get one deterministic report with support, Jaccard overlap, stable digests, evidence gaps, and a review action. No source fetching, identity inference, or causality claim.

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Company Hiring Radar

zinin/company-hiring-radar

Turn buyer-supplied Greenhouse, Lever, or Ashby board tokens into evidence-backed public hiring observations: open-role mix, posting recency, remote signals, source evidence, confidence, identity gaps, and a review-ready next action. No login or ATS API key.

Tim Zinin