Monitor a Google Sheets watchlist, detect competitor tech adoption via PredictLeads, and notify AEs with contextual alerts.
This AI agent continuously monitors a Google Sheets watchlist of domains, querying the PredictLeads Technology Detections API to identify instances of competitor technology adoption. When a match is found, it triggers a Gmail alert to the assigned AE with company name, domain, detected technology, and date. All actions are logged for audit and future refinement.
The AI agent automates detections and alerting so AEs act on signals quickly.
Read the CompetitorWatchlist tab to load domain, company_name, and ae_email for each row.
Query PredictLeads Technology Detections API for each domain in the watchlist.
Match detected technologies against the configured COMPETITOR_TECH (e.g., Salesforce).
If a match is found, extract the AE email and prepare a Gmail alert payload.
Send a Gmail alert to the AE with company name, domain, detected tech, and date.
Skip rows without a valid AE email and log the skip reason.
Automates monitoring and alerting to bring timely signals to the right people.
A simple 3-step flow anyone can follow.
A scheduled trigger runs daily at 8 AM and reads the CompetitorWatchlist tab from Google Sheets, loading domain, company_name, and ae_email for each row.
For each domain, the AI agent calls the PredictLeads Technology Detections API and compares results to the configured COMPETITOR_TECH variable.
If a match is found, extract the AE email and send a Gmail alert with details; otherwise continue to the next row and log outcomes.
A realistic daily run and the resulting AE alert.
At 8:00 AM, the AI agent loads the CompetitorWatchlist and finds Acme Widgets (acmewidgets.com) with AE email jane@acme.com. The PredictLeads API reports that Salesforce is detected on Acme Widgets' tech stack. The AI agent sends a Gmail alert to Jane with the company name, domain, detected tech, and date, and logs the match for auditing. If no match is found, the agent simply advances to the next row and records the pass. This enables timely follow-up and a consistent audit trail.
Roles that rely on timely competitor signals and clean data.
Receives timely alerts with exact account context to prioritize outreach.
Maintains and cleans the watchlist data and ensures AE routing is accurate.
Tracks accounts showing new competitor adoption for forecasting and coaching.
Coordinates messaging based on real-time competitive signals.
Identifies accounts that may be at risk or up for upsell based on tech shifts.
Keeps playbooks aligned with current competitive signals and outcomes.
Core tools connected to the AI agent for end-to-end data flow.
Reads the CompetitorWatchlist tab to load domain, company_name, and ae_email for each row.
Fetches technology detections for each domain and compares against COMPETITOR_TECH.
Sends alert emails to the AE with detection details and date.
Practical scenarios where real-time competitor signals drive action.
Common setup and workflow questions answered in detail.
Rows without a valid AE email are skipped and logged. The AI agent continues processing the remaining rows without sending emails. This prevents bounced alerts and keeps the watchlist clean. You can enforce data integrity by validating AE emails in the sheet. If needed, you can add a separate remediation step to alert a manager when emails are missing.
Yes. The COMPETITOR_TECH value can be updated in the AI agent's code configuration to reflect your target tool (e.g., HubSpot, Marketo, Zendesk). After updating, the agent will use the new value during detections. This keeps detection criteria aligned with current competitive signals. Be sure to reload or restart the AI agent to apply the change.
The agent is scheduled to run daily at 8 AM by default. You can adjust the trigger time to fit your time zone and workflow. The cadence determines how quickly new signals are surfaced to AEs each day. For critical campaigns, consider adding a mid-day run or a real-time check, if supported by your setup.
The AI agent will handle API errors gracefully by logging the failure and retrying a limited number of times. If the error persists, the row is marked as failed and continues to the next item to avoid blocking the entire run. You will have an audit trail to investigate failing domains and credentials. Consider setting up alerting on API outages for your team.
The AI agent uses your own watchlist data and API credentials. Data handling stays within your configured services, and all actions are logged for traceability. Ensure you have consent for processing domain and company data and that your PredictLeads usage complies with their terms. Review internal policies to confirm appropriate data handling and access controls.
Yes. The Gmail alert payload can be customized to include different fields (company, domain, detected tech, date, and additional contextual data). You can also adjust the tone and formatting of the message. If you need more dynamic content, extend the code to pull extra fields from the watchlist or API responses.
The architecture is designed to be extensible. You can add additional data sources or tools (e.g., alternative detection APIs or multiple sheets) by extending the integration layer and updating the matching logic. Ensure you maintain consistent data schemas across sources. Proper testing is recommended to validate end-to-end flow after changes.
Monitor a Google Sheets watchlist, detect competitor tech adoption via PredictLeads, and notify AEs with contextual alerts.
