Refine AIAI agents that automate lead enrichment with templates and predictable pricing.

4.7 (6)

შეფასებული Daniel Nikulshyn·განახლდა ივლისი, 2026

Lead Enrichment Automation Go-To-Market AI Agents Sales RevOps Templates

მიმოხილვა

Refine AI is a lead enrichment platform that uses AI agents to gather, clean, and structure prospect data at scale. It positions itself as a streamlined alternative to tools like Clay, focusing on ready-made templates and a pricing model that avoids per-credit surprises. Users can spin up agents to handle tasks such as finding contact details, qualifying companies, scraping public sources, and appending custom data points to existing lists. Templates cover common go-to-market workflows so teams can get started without building enrichment pipelines from scratch. The tool is aimed at sales, growth, and RevOps teams that want predictable costs and faster setup when building outbound lists or enriching CRM records.

ძირითადი ფუნქციები

AI agents for automated enrichment
Library of workflow templates
Contact and company data lookup
Custom data point appending
Bulk list processing
Flat, predictable pricing plans

ფასები

მოდელი: Free
კატეგორია: Lead Generation
შეფასება: 4.7 / 5 (6)

გამოყენების შემთხვევები

Build enriched outbound prospect lists

Sales teams can run AI agents on bulk lists to append contact details, company info, and custom data points for outbound campaigns.

Qualify inbound companies automatically

RevOps teams use agents to research and qualify inbound leads against ICP criteria, scraping public sources to score fit before routing to reps.

Launch GTM workflows from templates

Growth teams pick pre-built workflow templates to start enrichment pipelines quickly without configuring multi-step agents from scratch.

Append custom data to existing CRM lists

Teams upload existing prospect lists and use agents to fill missing fields or add custom attributes with predictable, flat-rate pricing.

დადებითი და უარყოფითი

დადებითი

Predictable pricing without complex credit math
Pre-built templates speed up setup
AI agents handle multi-step enrichment
Positioned as a simpler Clay alternative

უარყოფითი

Newer platform with smaller ecosystem
Fewer integrations than established competitors
Template library still expanding

შეფასებები

4.7

საშუალო 6 შეფასებიდან.

შედი ანგარიშზე შეფასების დასატოვებლად.

Yuki Mori

May 23, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is contact and company data lookup — handled better than most — and positioned as a simpler Clay alternative. Template library still expanding is my one real gripe. Worth the time if this is your use case.

Margaret Whitfield

Feb 22, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is custom data point appending — handled better than most — and positioned as a simpler Clay alternative. Worth the time if this is your use case.

Beatriz Costa

Dec 25, 2025

Does the job

Pretty happy overall. Bulk list processing just works and aI agents handle multi-step enrichment. but no dealbreakers — I'd recommend it to a friend without hesitating.

Fatima Zahra

Dec 15, 2025

Does the job

Pretty happy overall. Contact and company data lookup just works and pre-built templates speed up setup. Template library still expanding can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Marcus Bell

Jul 2, 2025

Solid for our team

We rolled this out across the team last quarter and pre-built templates speed up setup. Contact and company data lookup fits neatly into how we already work, and flat, predictable pricing plans removed a step we used to do by hand. Newer platform with smaller ecosystem, which is the main caveat, but it has held up under daily use.

Olga Ivanova

Jun 25, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on contact and company data lookup, and positioned as a simpler Clay alternative caught me off guard. Template library still expanding is why this isn't a perfect score, still, I'd recommend giving it a real trial.