CRM and sales decision
Poach VC
A technical user can reproduce the core filtered exports and daily emails, but the vendor's proprietary identity-resolution data and scale are durable advantages that are hard to match; building a minimal replacement is realistic, but matching product parity is not.
Visit website↗Built by Jared Rhizor, who ships 4 products in this index
$1/mo
$12/yr
Read off the official pricing page.
$100one-off56 h to build
$150/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 159 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
- Monitor VC follows on Twitter, resolve identities to LinkedIn, label people with AI, store and expose records, and generate daily CSV/email exports.
What it still won’t have
- Proprietary identity-resolution accuracy and tuned matching heuristics
- Historical dataset scale and proven early-warning signals amassed by the vendor
- Curated daily newsletters and editorial selection
- Operational polish (uptime, onboarding, and ready-to-use filtering presets)
What remains hard
- Proprietary data
We match Twitter accounts to LinkedIn accounts using our proprietary identity resolution and enrich our records with work history and education.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 159 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 self-hosted Poach-like lead enrichment service using Node.js (Express) + Postgres + Redis. Core features: 1) Ingest Twitter follows for a configurable list of VCs (use Twitter API or mocked ingest) and store follow events; 2) Identity-resolution worker that queries LinkedIn (or accepts manual LinkedIn URLs) and enriches profiles with work history and education; 3) AI labeling microservice (call an LLM) that classifies role labels and generates short bios; 4) REST API to query/filter profiles and a CSV export endpoint; 5) Daily scheduler that composes an email with top leads and attaches a CSV (use a transactional email provider). Out of scope: training proprietary ML models, replicating the vendor's historical dataset, and premium UI. Include error handling, retries for external API calls, background job visibility, basic tests for API and workers, and deployment instructions using Docker and a small cloud VM (e.g., $20/mo).
How we checked
How the score was reached
- Partly verdict base52
- Price verified on pricing page+3
- Hard moats found in the evidence-3
- Evidence score52
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 · 1
Every page the run actually retrieved.
- official pricingPoach - Early stage founder leads for VCs
Integrity checks
What held up, and what did not.
