AI assistants and search decision
Influence
A single competent developer can build a useful subset (lead ingestion, LLM-driven outreach, scheduling, basic analytics) in a multi-week effort using existing open-source tools, but reproducing Dreamstate's full product polish, multi-surface AI-visibility connectors, and curated growth playbooks would be costly to match.
Visit website↗$99/mo
$1,188/yr
Read off the official pricing page.
$100one-off160 h to build
$150/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Discover buyer signals → enrich and score leads → generate personalized outreach/content via LLM → queue for approval → publish/send (LinkedIn/X) → track replies and AI-search visibility → iterate.
What it still won’t have
- Proprietary, prebuilt AI-playbooks and tuned voice-profiles
- Polished UX, built-in Chrome extension, and polished approval workflows
- Integrated AI-visibility tracking across multiple AI search surfaces (prebuilt connectors)
- Customer support, SLAs, and curated deliverability/LinkedIn account management
- Scale-tested orchestration and out-of-the-box analytics rollups
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted AI growth workspace using Node.js + Express backend, React admin UI, Postgres for storage, Redis + BullMQ for job orchestration, and OpenAI-compatible LLM API for generation. Core features in scope: 1) a lead ingestion service that pulls public signals (X/Reddit/Hacker News) into Postgres and provides basic ICP scoring; 2) an LLM-driven templating engine plus per-brand voice profile to generate outreach and social drafts; 3) OAuth-based LinkedIn and X publish/schedule integration and a queue with approval UI; 4) a simple analytics dashboard (sends, replies, engagement) stored in Postgres and surfaced in the UI; 5) error handling, retry logic for job failures, and unit/integration tests for ingestion, generation, publishing, and orchestration. Out of scope: AI-search visibility connectors to ChatGPT/Claude/Perplexity/Gemini, Chrome extension, multi-tenant billing, and advanced deliverability/Account Management. Deliverables must include Docker Compose for local dev, documented env vars, automated tests, and basic CI.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productDreamstate — official product
- official pricingDreamstate Pricing
- official docsDreamstate Demo
Integrity checks
What held up, and what did not.
