Gojiberry AI

Watches public buying signals, scores the people behind them, and sends the first LinkedIn message

YES · focused build
price $99/mosubscription / year $1,188estimated build time one sittingreplaced by 0 people

Every stage of this loop is already a bought part. Signals come from ready-made Apify actors, or from a Hermes agent running the watch on a schedule. Finding and enriching the people behind them is a single call to MoltSets or Prospeo. The LinkedIn send is Unipile, which will hold several connected accounts at once, so the DIY version is not capped at the two senders Pro gives you. What you actually write is the glue, the ICP scoring, and the send queue, and that is one sitting. The asterisk: you are renting four services instead of writing them, and the outreach still runs on real LinkedIn accounts with real limits.

Build verification: not recorded. How we judge buildability

What you give up

  • one enrichment provider instead of a 15+ provider waterfall, so coverage on the hard contacts is thinner
  • cross-customer benchmarking and the weekly self-tuning
  • the ten-minute setup: your version does not exist until you build it
  • someone else absorbing the breakage when an actor or a LinkedIn endpoint changes
  • a support line when a sending account gets restricted

Why people still pay

They pay to skip the assembly and the maintenance. Gojiberry turns a website URL into a running agent in ten minutes, keeps the scrapers working when a page layout changes, and puts the signal source, the enrichment waterfall, and both channels on one bill. Rent the parts yourself and the monthly cost drops, but you own every break, you are reconciling four dashboards, and the signals only stay useful if you keep feeding the watchlist new pages and creators.

Your build guide

The stack, security requirements, and agent rules for a focused replacement.

Before you start

  • A supported Node release, PostgreSQL, HTTPS for a shared deployment, and a backup destination. Begin with one workspace and explicit owner/member permissions.
  • Implementation components: Next.js App Router and TypeScript for server-rendered pages and validated mutations. PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.
  • Scope boundary: Platform-approved acquisition, enrichment rights and lawful outreach need independent confirmation; no account-fleet automation.
01
Next.js App Router and TypeScript for server-rendered pages and validated mutations.
02
PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.
03
Domain model: user-authorized prospect imports, observable signals, ICP rules, source evidence, draft messages and suppression states
engineering roadmap

Implementation plan

1

Phase 1

Scope and fixtures. Implement this bounded workflow: Import a permitted prospect dataset, score it against transparent ICP rules and draft a connection note referencing the recorded signal. Require human review and manual outreach or an explicitly authorized provider integration. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Platform-approved acquisition, enrichment rights and lawful outreach need independent confirmation; no account-fleet automation.

2

Phase 2

Durable model. Model user-authorized prospect imports, observable signals, ICP rules, source evidence, draft messages and suppression states Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Do not scrape protected accounts, rotate identities or automate unsolicited messages; absence of data is not an inferred personal trait.

3

Phase 3

Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.

4

Phase 4

Permissions and integration failure. Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.

5

Phase 5

Portable handoff. Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.

6

Phase 6

Acceptance scenarios. A prospect lacking the required signal receives no fabricated personalization; a suppressed contact cannot enter an approved outreach export. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

the pro prompt
download AGENTS.md
WORKING SLICE
Import a permitted prospect dataset, score it against transparent ICP rules and draft a connection note referencing the recorded signal. Require human review and manual outreach or an explicitly authorized provider integration.

Build this scoped Gojiberry AI-inspired workflow with a documented data model and visible failure states.

Architecture
- Next.js App Router and TypeScript for server-rendered pages and validated mutations.
- PostgreSQL with Drizzle migrations; Better Auth sessions for a small private workspace.

Prerequisites and limits
A supported Node release, PostgreSQL, HTTPS for a shared deployment, and a backup destination. Begin with one workspace and explicit owner/member permissions.
Outside this release: Platform-approved acquisition, enrichment rights and lawful outreach need independent confirmation; no account-fleet automation.

Data model and correctness
user-authorized prospect imports, observable signals, ICP rules, source evidence, draft messages and suppression states
Invariant: Do not scrape protected accounts, rotate identities or automate unsolicited messages; absence of data is not an inferred personal trait.
Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.

Security and privacy
Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML.

Recovery and export
Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching.

Implementation order
1. Phase 1 — Scope and fixtures. Implement this bounded workflow: Import a permitted prospect dataset, score it against transparent ICP rules and draft a connection note referencing the recorded signal. Require human review and manual outreach or an explicitly authorized provider integration. Record prerequisites, select representative user-owned fixtures and document the unsupported features: Platform-approved acquisition, enrichment rights and lawful outreach need independent confirmation; no account-fleet automation.
2. Phase 2 — Durable model. Model user-authorized prospect imports, observable signals, ICP rules, source evidence, draft messages and suppression states Add migrations or a versioned document format, explicit validation, stable IDs and a visible import-error report. Preserve this rule: Do not scrape protected accounts, rotate identities or automate unsolicited messages; absence of data is not an inferred personal trait.
3. Phase 3 — Complete the first useful path. Implement the workflow's input, review and output interface, with clear controls and explicit empty/error states. Commit related database changes in one transaction. Use version checks for competing edits and an outbox for external notifications; retry delivery independently of saving the record.
4. Phase 4 — Permissions and integration failure. Authorize every record read and mutation on the server using its workspace membership; validate payloads, protect mutations against CSRF, and escape user-authored HTML. Request integration credentials and permissions only for the enabled feature; show a disconnected state instead of mock results.
5. Phase 5 — Portable handoff. Export versioned JSON plus attachments and a readable CSV summary. Restore into a separate database and compare record IDs and attachment checksums before switching. Include setup, operating limits, fixture walkthrough and shutdown/restart instructions in the README.
6. Phase 6 — Acceptance scenarios. A prospect lacking the required signal receives no fabricated personalization; a suppressed contact cannot enter an approved outreach export. Repeat the workflow after restart and with a denied permission or unavailable dependency; show recoverable failure rather than a success placeholder.

Acceptance
A prospect lacking the required signal receives no fabricated personalization; a suppressed contact cannot enter an approved outreach export.
Use real source data or clearly labeled fixtures. Explain unsupported input and provider failures; do not fabricate analytics, delivery receipts, accuracy claims or security guarantees.

Optional agent guidance
Optional external skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — Improve React and Next.js data fetching, rendering, bundle size and server performance. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [supabase-postgres-best-practices](https://github.com/supabase/agent-skills/blob/main/skills/supabase-postgres-best-practices/SKILL.md) — Review PostgreSQL schemas, queries, indexes, pooling, concurrency and row-level security. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Optional external skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — Review web interfaces for accessibility, keyboard focus, forms, navigation and interaction quality. Review its instructions and compatibility before use; it does not grant deployment, data-access or publication permission.
Project rule — data model: user-authorized prospect imports, observable signals, ICP rules, source evidence, draft messages and suppression states
Project rule — preserve this invariant: Do not scrape protected accounts, rotate identities or automate unsolicited messages; absence of data is not an inferred personal trait.
Project rule — acceptance evidence: A prospect lacking the required signal receives no fabricated personalization; a suppressed contact cannot enter an approved outreach export.

$ open in your agent (prompt prefilled, you press enter), copy the prompt or copy or download AGENTS.md

prior art · use these instead of building, if you'd ratherHermes Agentself-hosted agent that can run the signal watch on a schedule instead of a cron service you write↗n8nself-hostable workflow automation, the usual no-code way to wire signals to outreach↗Mauticopen-source marketing automation with contacts, campaigns, sequences, and suppression↗
share on X ↗

Gojiberry AI pricing

planmonthlyannual (per mo)what you get
pro$99/workspace—Email + socials; live in 5 minutes; numeric lead/message caps not published.First AI sales rep positioning; free trial/cancel anytime copy shown.

free tierno free tier; free trial mentioned but no numeric caps published.

billingmonthly only; no annual plan verified

hidden costsNo numeric usage caps, seat limits or overages were published on the crawled page.

pricing sources checked 2026-08-10 · pricing source ↗

Questions about Gojiberry AI

Can you build your own Gojiberry AI with AI?

The verdict is yes for the scoped workflow. Every stage of this loop is already a bought part. Signals come from ready-made Apify actors, or from a Hermes agent running the watch on a schedule. Finding and enriching the people behind them is a single call to MoltSets or Prospeo. The LinkedIn send is Unipile, which will hold several connected accounts at once, so the DIY version is not capped at the two senders Pro gives you. What you actually write is the glue, the ICP scoring, and the send queue, and that is one sitting. The asterisk: you are renting four services instead of writing them, and the outreach still runs on real LinkedIn accounts with real limits.

What does the Gojiberry AI build prompt cover?

The prompt starts with this scope: Import a permitted prospect dataset, score it against transparent ICP rules and draft a connection note referencing the recorded signal. Require human review and manual outreach or an explicitly authorized provider integration. Full-product capabilities excluded from the comparison include: one enrichment provider instead of a 15+ provider waterfall, so coverage on the hard contacts is thinner; cross-customer benchmarking and the weekly self-tuning; the ten-minute setup: your version does not exist until you build it. Follow the implementation plan and its prerequisites before expanding the build.

How do I use the prompt, AGENTS.md and agent skills?

Start with the Gojiberry AI prerequisites and stack, then copy the prompt into your coding agent. Save the project rules as AGENTS.md in the project root. Linked skills are optional packages or source instructions for specific tasks; review their current contents and install only those matching the chosen stack. A skill does not supply API credentials or verify the finished app.

How long will this Gojiberry AI project take?

The catalogue estimate is one sitting for the limited scope. Setup, integration approvals, debugging, deployment and ongoing maintenance can add time. This is an estimate, not a delivery guarantee.

What would I give up by replacing Gojiberry AI?

one enrichment provider instead of a 15+ provider waterfall, so coverage on the hard contacts is thinner; cross-customer benchmarking and the weekly self-tuning; the ten-minute setup: your version does not exist until you build it; someone else absorbing the breakage when an actor or a LinkedIn endpoint changes; a support line when a sending account gets restricted. They pay to skip the assembly and the maintenance. Gojiberry turns a website URL into a running agent in ten minutes, keeps the scrapers working when a page layout changes, and puts the signal source, the enrichment waterfall, and both channels on one bill. Rent the parts yourself and the monthly cost drops, but you own every break, you are reconciling four dashboards, and the signals only stay useful if you keep feeding the watchlist new pages and creators.

What price is this guide comparing against?

The recorded Pro plan is $99/mo (monthly), checked 2026-08-03. Check the linked pricing source before buying. Building your own also has hosting, API and maintenance costs; the recorded amount is not a guaranteed saving.

What can I use instead of building Gojiberry AI?

The prior-art section lists Hermes Agent, n8n, Mautic as starting points. Review their current scope, license and maintenance before adopting one.

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