Germany Hospital Cybersecurity Tender Feed

Monitor TED Search API, Common Procurement Vocabulary, oeffentlichevergabe.de Open Data and return tender notice records with buyer, deadline, category, source URL, and action-ready context.

Who this is for

Vendors, suppliers, contractors, consultants, sales teams, and bid teams use this actor when they need focused germany hospital cybersecurity tender output instead of a broad generic scraper or manual checking.

Buyer outcomes

• Find relevant germany hospital cybersecurity tender opportunities faster with normalized source evidence and match context.
• Prioritize review using buyer names, deadlines, categories, source URLs, scores, and confidence.
• Route new or changed opportunities into recurring CRM, spreadsheet, bid, or alert workflows.

Sources monitored

• TED Search API
• Common Procurement Vocabulary
• oeffentlichevergabe.de Open Data
• e-Vergabe Bund

Inputs

• sourceMode: use sample for a smoke run or configured public tender source modes.
• startUrls: official tender notices, buyer pages, procurement documents, or source URLs.
• sourceIds: approved public procurement source identifiers.
• maxItems: bounded number of tender notices to return.
• sinceLastRun: emit only new or changed notices when scheduled.
• watchlistTerms: buyer, CPV/category, region, deadline, value, or requirement terms.
• webhookUrl: optional destination for bid, CRM, spreadsheet, or alert workflows.

How it transforms the input

• Input: official tender notice, procurement portal result, buyer page, or public tender document.
• Transformation: normalize the notice, preserve source evidence, and apply buyer/category/deadline filters.
• Output: tender notice with buyer, deadline, category, value when available, source URL, match reason, and confidence.

Outputs

The actor returns structured tender notice records with buyer, deadline, region, category, CPV/category hints, value when available, source URL, match reason, and confidence.

Family-specific fields to expect:

• buyerName: Public buyer or awarding authority.

• deadline: Submission or response deadline when available.

• category: Tender category or CPV/category hint.

• sourceUrl: Official notice or document URL.

• matchReason: Why the notice matched the actor focus.

• recordId: Stable record ID for exports, dedupe, and downstream joins.

• title: Human-readable record title for review and export.

• sourceName: Source identifier used to trace where the record came from.

• sourceUrl: Direct source URL for review and audit.

• dedupeKey: Stable key used for delta mode and duplicate suppression.

• retrievedAt: Timestamp showing when the actor retrieved or generated this record.

• score: Normalized field for filtering, routing, or downstream review.

• scoreReasons: Buyer-readable explanation for the score or match.

• confidence: Normalized field for filtering, routing, or downstream review.

• errors: Normalized field for filtering, routing, or downstream review.

• runSummary: Run-level summary for counts, filters, charges, and next actions.

Pricing

This actor uses Apify pay-per-event pricing. Current public listing guidance: $29-$49 / 1,000 launch validation records until public data proof is complete. Charges are tied to buyer-visible value events such as qualified-tender, attachment-evidence, dataset-processed, record-saved, enriched-record. Small validation runs are supported so you can inspect output before scaling a schedule.

• qualified-tender: Charge when Germany Hospital Cybersecurity Tender Feed produces Qualified Tender. Typical price: $0.043. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• attachment-evidence: Charge when Germany Hospital Cybersecurity Tender Feed produces Attachment Evidence. Typical price: $0.043. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• dataset-processed: Base charge when Germany Hospital Cybersecurity Tender Feed writes a non-empty default dataset. Typical price: $0.011. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• record-saved: Charge for each buyer-visible result saved by Germany Hospital Cybersecurity Tender Feed. Typical price: $0.003. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• first-run-cap: Recommended first run budget cap. Typical price: $3.820. Start with the default small run, inspect the dataset, then raise maxItems or schedule recurring runs.

API example

curl -X POST "https://api.apify.com/v2/actors/zentrafoundry~germany-hospital-cybersecurity-tender-feed/runs" \
+  -H "Authorization: Bearer $APIFY_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{"maxItems":10,"sourceIds":["TED-SEARCH","TED-CPV","OEV-OPEN"],"includeSourceUrls":true,"includeMatchReasons":true,"outputMode":"buyer-ready-records"}'

Recommended first run

{
    "maxItems": 10,
    "sourceIds": [
        "TED-SEARCH",
        "TED-CPV",
        "OEV-OPEN"
    ],
    "includeSourceUrls": true,
    "includeMatchReasons": true,
    "outputMode": "buyer-ready-records"
}

Sample output

Review the real stored sample from the latest successful quality run: https://zentra.nimblique.studio/external/actor-review/samples/germany-hospital-cybersecurity-tender-feed.json

Recommended public tasks

[
    {
        "name": "Review 10 tender notices",
        "description": "Low-cost validation run for checking buyer, deadline, category, and source fields.",
        "input": {
            "maxItems": 10,
            "sourceIds": [
                "TED-SEARCH",
                "TED-CPV",
                "OEV-OPEN"
            ],
            "includeSourceUrls": true,
            "includeMatchReasons": true,
            "outputMode": "buyer-ready-records",
            "actorSlug": "germany-hospital-cybersecurity-tender-feed"
        }
    },
    {
        "name": "Daily tender notice review",
        "description": "Recurring batch for new public procurement notices and changed opportunity details.",
        "schedule": "Daily during local business hours",
        "input": {
            "maxItems": 25,
            "sourceIds": [
                "TED-SEARCH",
                "TED-CPV",
                "OEV-OPEN"
            ],
            "includeSourceUrls": true,
            "includeMatchReasons": true,
            "outputMode": "buyer-ready-records",
            "actorSlug": "germany-hospital-cybersecurity-tender-feed"
        }
    }
]

Use cases

• Track new germany hospital cybersecurity tender opportunities without checking each tender source manually.
• Route qualified records into CRM, spreadsheets, or internal review queues.
• Monitor source links, tender details, and match reasons for faster triage.
• Schedule recurring runs to catch new opportunities while keeping first-run costs low.

Trust and compliance

• Uses TED Search API, Common Procurement Vocabulary, oeffentlichevergabe.de Open Data.
• Keeps source URLs and source identifiers in output records for auditability.
• Does not require private credentials unless a source is explicitly configured for approved authenticated access.

Limitations

• Results depend on public-source availability, source uptime, and source update cadence.
• Public sources can revise records after publication; rerun scheduled tasks for fresh evidence.
• Scores and match reasons are decision-support signals, not legal, financial, procurement, medical, safety, or regulatory advice.
• Large production runs can cost more than the default smoke run; start small, inspect output, then scale schedules.

FAQ

Can I run this without URLs? Yes. The default sample mode is designed to succeed without user-supplied URLs, and URL-backed runs can use startUrls when needed.

Can I schedule it? Yes. Use sinceLastRun, watchlistTerms, and optional webhookUrl to turn the actor into a recurring alert or report workflow.

How do I verify value before scaling? Run the recommended first-run input, review the sample output fields, then increase maxItems or schedule recurring runs after the dataset matches your use case.