How to Migrate From Trigify to SignalRaven: A Step-by-Step Playbook

TL;DR

Export and inventory your Trigify setup, then start a SignalRaven workspace while Trigify still runs.
Map each search and monitored profile to a SignalRaven source.
Count sources and destinations to size your plan.
Translate agent prompts and If conditions into SignalRaven's ICP and built-in qualification.
Reconnect destinations and remap webhook fields.
Run both systems side by side, then cut over ahead of your end date.

Don't migrate the workflow. Migrate what the workflow was trying to do.

Trigify and SignalRaven don't model LinkedIn signals in exactly the same way. A Trigify workflow might combine a search, an Agent prompt, If conditions, severity settings and several downstream actions. In SignalRaven, some of that logic is handled by sources, some by ICP qualification and built-in filtering, some by prioritization and some by destinations. Preserve the original Trigify configuration for reference, but rebuild the intent rather than trying to reproduce every node.

For most customers, Trigify access ends at 11:59pm BST on October 22, 2026. Trigify's announcement says its team is joining HubSpot and the standalone platform is retiring. Integrations and API access stop then too. Your account notice sets any other end date and spells out your data and billing terms. SignalRaven is independent of Trigify and HubSpot.

Step 1: Export and inventory Trigify

Work from the inventory in our guide to export your Trigify data: every saved search with its Boolean terms, every monitored profile, every agent prompt and If condition, and every system Trigify feeds. Preserve the originals before you translate anything.

Step 2: Start SignalRaven while Trigify still runs

Start your SignalRaven workspace while Trigify is still active so you have time to compare the two systems before cutover. The strategic point is overlap.

SignalRaven's setup process gives you a starting configuration based on your company and market: it reads your company's LinkedIn page and website, then drafts an ICP, competitor company pages with a mention source for each, keyword searches and your own page. For migration, treat that as a draft. Your proven Trigify sources should take priority over automatically suggested sources while you're validating the move, so pause suggested sources as needed to make room for the Trigify sources you're actively comparing, and leave your own page active.

Step 3: Map your monitoring sources

Trigify item	SignalRaven source	How to add it
Monitored person (their own posts)	Posts written by specific people	Paste linkedin.com/in/ URLs, up to 20 per paste.
Monitored company page	A company's LinkedIn page	One company URL per source. Its staff are filtered out.
Brand or competitor name search	Posts that mention a company	Paste company page URLs, up to 10 per paste. Catches posts that tag each company. Add the name as a keyword phrase to catch untagged mentions.
Keyword or Boolean saved search	Posts matching keywords	One short quoted phrase per source.
Your own company page	Your own company's posts	AI setup adds it.

Copy each monitor's URL from the profile_url on its Trigify search record. Your Output CSVs hold the LinkedIn URLs of the people Trigify surfaced, so save those for the side-by-side dedupe.

If the person themselves matters, and not just the audience engaging with their posts, SignalRaven can also follow that person's public LinkedIn activity with the "Follow specific people's activity" source. That has no Trigify equivalent for a monitored profile, and it's often the better fit for known people at target accounts.

SignalRaven also supports source types that may not map directly to an existing Trigify workflow, including hashtags, industries, topic-filtered company pages and Conference Intelligence. Rebuild first, then expand: get your proven Trigify coverage running, and once the parallel test is stable, consider whether those add useful coverage rather than simply recreating the old setup.

Size your plan

Every profile, page and phrase is its own source. Only active sources count toward your cap, so pausing one frees its slot and keeps its settings. The plan caps on the pricing page are 20 sources on Solo, 80 on Pro and unlimited on Scale. For destinations, Solo connects 1, Pro connects 2 and Scale is unlimited.

A fictional count: 15 monitored people, 8 company pages, 3 competitor mention sources, 5 Boolean searches rewritten as 12 phrases, and your own page make 39 sources. That's over Solo's 20 and inside Pro's 80. Prioritize the people and sources that consistently produced useful signals, because each one takes a slot.

Count one destination for each system Trigify fed. If you need to route signals to more destinations than your plan supports directly, a workflow platform such as n8n can distribute a webhook to additional systems.

Step 4: Translate Boolean searches into focused monitoring

Trigify's keyword searches combine AND, OR and NOT fields, and its help page suggests NOT terms like hiring or job posting to cut noise.

Don't automatically recreate every AND, OR and NOT clause. Look at which parts of the search actually produced useful conversations. Those often translate well into focused phrases based on the language people use in real posts. Broad searches consume more processing and create more noise downstream: in SignalRaven data, about 78% of commenters on keyword posts are screened out as off-topic or off-profile. A focused phrase reduces unnecessary processing and gives the qualification system a cleaner starting point, which also makes better use of your source and usage limits.

A fictional expense-management vendor searched "expense reports" (AND) and "chasing receipts," "approval delays" and "recommendations" (OR). Its NOT field held "hiring" and "job posting." Two OR terms carry practitioner language, and "recommendations" pulls in every tool roundup. The search could become two keyword sources, "chasing receipts" and "expense approval delays," with the NOT list left to qualification. How to write keyword searches that find the right signals goes deeper on query craft.

Step 5: Translate qualification logic

Trigify's help center shows ICP filtering written as an agent prompt that checks company type, headcount and seniority, then answers yes or no for an If condition. In SignalRaven that logic lives in the ICP editor and the built-in checks.

Trigify prompt concept	SignalRaven ICP field	Fictional example
Role or title	Target titles, Target personas	Controller, AP Manager; Finance operations leader
Market or problem fit	Market affinity (scored from your product description)	Companies dealing with expense approval delays
Industry	Target industries	Logistics, Construction
Headcount	Employee range (company size)	100 to 1,000
Geography	Target locations	United States, Canada
Excluded personas	Exclude titles	Intern, Recruiter
Known good or bad examples	Classification examples	A 300-person freight broker marked as a target account

Not every Trigify condition belongs in the ICP. Move exclusions to the built-in checks first. The staff filter sets aside anyone who works at your company or at a company whose page you watch. Commercial classification identifies sellers at competing vendors and influencers separately, so they aren't treated as ICP signals. If your prompt screened out consultants or agencies, switch on Exclude influencers: despite the label, this setting also excludes consultants, agencies and systems integrators, and it works on every plan.

SignalRaven scores five ICP dimensions: title and role, market affinity, industry, company size and location. AI setup gives each one its own threshold, and a person must pass every switched-on dimension that has data for them. Admins and managers can edit ICP fields on any plan. Only Scale lets you change the thresholds. Start broad, then narrow one field at a time against the side-by-side log. The ICP qualification guide explains what each dimension scores, and the 14-stage qualification process covers the rest of the checks.

Trigify severity rules from the Configure Signal node can be translated into SignalRaven's urgency model, a score from 1 to 10. Urgency incorporates more context than a label: it rises with earlier signals from the same person or company, a top-relevance post and an employer classified as a target company rather than a seller or influencer. Urgency is a prioritization score, not a claim that someone is ready to buy. It helps order signals using the available context, including prior activity from the person or company.

Step 6: Reconnect destinations

Slack, Google Sheets, Notion, Clay, n8n and webhooks connect directly, and the destinations guide covers setup. Most CRMs and sequencers take the signed webhook through an automation tool such as n8n. Give each destination a routing rule: every signal, a minimum urgency or manual sends only. Signals identify people by LinkedIn profile URL and carry no email address, so keep your email-finding step ahead of the sequencer.

Trigify concept	SignalRaven field
Record ID for dedupe	signalId
Severity or prioritization score	strength (urgency, 1 to 10)
LinkedIn profile URL	personLinkedinUrl
Name, title, company	personName, personTitle, personCompany
ICP fit	icpPersona, icpCompositeScore
Post	sourcePostUrl, postTextPreview
AI reasoning	whyItMatters, postSummary
Suggested context or opener	suggestedOpener (stronger signals only)
Comment text	engagementDetail

If you're migrating custom webhook consumers, update the field mapping and verify SignalRaven's signature before cutover. Technical note: copy the destination's secret when the portal shows it at creation, check the X-SignalRaven-Signature header (sha256= plus an HMAC-SHA256 of the raw request body under that key) and drop repeat deliveries on the Idempotency-Key header, which carries the signal ID. The webhook documentation has the details.

Step 7: Run a side-by-side test

Point both tools at the same proven sources.
Have Trigify's Google Sheets action and a SignalRaven Google Sheets destination write to separate tabs of one spreadsheet.
Log each signal with its source system, person, company, ICP fit and engagement context. Dedupe on the LinkedIn profile URL so each person appears once.
Score each row on the same columns: Signal, Source, Person or account match, Relevant context, Trigify passed, SignalRaven passed, Useful, Why.

Then evaluate the run as a whole:

Did both systems find the relevant activity?
Did either produce obvious noise?
Did SignalRaven correctly classify the person?
Was the context useful?
Were repeat person and account patterns retained?
Would the team want this routed downstream?

Also compare account-level patterns. SignalRaven may surface several qualified people from the same company or retain prior activity that changes the importance of a new signal, so don't judge the systems only row by row. Use the parallel run to identify an initial routing threshold for your CRM destination, then adjust it based on signal quality and volume rather than a single example. Keep the Sheets destination on every signal to see what falls below the threshold, and move CRM rules keyed to Trigify severity or another prioritization score to the matching urgency range.

Step 8: Cut over

Pick a cutover date ahead of your Trigify end date.
Repoint webhook consumers and sequencer inputs to SignalRaven.
Confirm each destination received a signal end to end.
Pause the sequences and automations Trigify feeds.
Archive the Trigify configuration, then retire the workflows.

Don't rebuild what you don't need

A migration is a good time to retire sources and logic that no longer produce useful signals. Before recreating a Trigify monitor, ask when it last produced something useful, whether someone still owns the downstream workflow and whether another SignalRaven source covers the same need better. Archive the old configuration, but rebuild only what still serves a purpose.

Go beyond your old Trigify setup

Once you've rebuilt the basics and the parallel test is stable, expand your coverage with sources and views Trigify didn't have:

Follow specific people's activity. Track public activity from known people rather than only the audiences around their posts.
Conference Intelligence. Monitor event-related LinkedIn activity before, during and after conferences.
Topic-filtered company pages. Watch a broad company page but focus monitoring on specific themes.
Industry monitoring. Discover relevant conversations across an industry.
Hashtags. Monitor communities or recurring conversations organized around specific tags.
Signal history and account patterns. Earlier activity from the same person or company raises urgency on every plan, the Companies view groups signals by account, and Pro and Scale add the signal stack view.

Rebuild what worked. Validate it side by side. Then expand beyond the old setup.

Trigify migration FAQ

The Trigify alternative FAQ answers scope questions about other channels, contact data and job changes.

Will my Trigify workflows map directly into SignalRaven?

Not node for node. The goal is to preserve what each workflow was trying to accomplish. Searches and monitors become sources; qualification logic moves into the ICP and built-in checks; severity logic maps into prioritization; and downstream actions become destinations or automation workflows.

Can I run Trigify and SignalRaven at the same time?

Yes, until your Trigify end date. Both can watch the same LinkedIn sources while one spreadsheet logs who each tool surfaced.

Can I create sources through the API or MCP server?

Not today. You add sources in the portal. The REST API and MCP server read your sources and can create or update destinations.

How do I move a long list of Trigify profile monitors into SignalRaven?

There's no file import today, so paste them in batches of up to 20 linkedin.com/in/ URLs into "Posts written by specific people." Each profile becomes its own source. Before importing the full list, use your Trigify history to remove people whose posts never produced useful signals, so legacy noise doesn't fill your source allocation.

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