Case Studies

Real results from Effi Flo deployments

Named clients. Measurable outcomes. The architecture behind every build. Read how recruiting agencies and marketplaces use Effi Flo to hit 85%+ match accuracy, replace manual outreach teams, and reactivate stale databases.

What you will find here

Real clients, named in full. Stacked SP, MarketerHire, and more. No placeholder names, no anonymous anecdotes.
Quantified outcomes. Match accuracy deltas, shortlist turnaround, CRM volume reactivated, revenue impact. Numbers you can cite.
Full architecture breakdowns. Clay, Supabase, n8n, custom matching logic. The exact stack decisions behind every build.
Honest limitations. What each system does not solve and the prerequisites you need before it will work for you.

Who these case studies are for

These case studies are written for operators. If you run a recruiting agency, a two-sided marketplace, or an in-house talent acquisition team and you are evaluating whether AI automation can meaningfully move your numbers, these pages are for you. Every case study includes the problem, the solution, the architecture, the results, and the failure modes. If you want to see what we build, start with the services page. If you want to see how we write about the work, the blog goes deeper on the patterns.

How is each case study written?

Every case study on this page is built from a recorded client interview. Direct quotes come from the transcript, not from testimonial snippets. Metrics come from live production data, not from pitch decks. And every case study is reviewed by the client before it goes live. If a client prefers anonymity, we skip the case study entirely rather than use placeholder names. That accountability is the whole point.

Marketplace

MarketerHire

500K+

How MarketerHire Turned a Stale 500K+ Contact CRM Into a Signal-Based Outbound Engine

We replaced MarketerHire's manual outreach team with a signal-based outbound system, reactivated their stale 500,000+ contact CRM, and built the Marketing Intelligence Report — a personalized lead magnet template that became a core part of their pipeline.

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Recruiting Agency

Stacked SP

85%+

How Stacked SP Hit 85%+ Candidate Match Accuracy with AI-Driven Sourcing

We built Stacked SP a concurrent AI matching engine that delivers 85%+ accurate candidate shortlists in hours instead of weeks, replacing manual LinkedIn sourcing with an automated pipeline that hits 2x the industry match rate.

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Frequently Asked Questions

What does a typical Effi Flo case study cover?

Each case study walks through the client's problem, the system we built, quantified results (match accuracy, time saved, revenue impact), the exact tech stack (Clay, Supabase, n8n, custom matching logic), and the honest limitations of the approach. Every case study uses named clients and named metrics — no generic anonymous stats.

How long does an Effi Flo engagement typically last?

A typical engagement runs multi-week to multi-month. The first two weeks scope the system and ship an MVP. From there we iterate on scoring logic, edge cases, and scale based on real production data. Most clients stay on retainer after the initial build to extend the system into adjacent workflows.

Which industries has Effi Flo deployed these systems in?

Recruiting agencies (tech, consulting, engineering), marketplaces matching clients to freelancers or talent networks, and in-house talent acquisition teams. The architecture is similar across all three — signal monitoring, candidate matching, enrichment, outreach — but the scoring logic and data providers vary by vertical.

Do Effi Flo case studies include direct client quotes?

Yes. Every case study pulls direct quotes from recorded client interviews. No paraphrasing, no hypothetical endorsements. If a client prefers anonymity, we skip the case study entirely rather than use placeholder names.

Can you build something similar for my business?

Probably, with two caveats. First, we only take engagements where we can name the client and publish the result after launch — that keeps us accountable. Second, the architecture works best when you already have a clear definition of what a great candidate or match looks like. If that definition does not exist yet, we start there before building anything.

Want to be the next case study?

We only take engagements where we can name the client and publish the result after launch. That keeps us accountable.

Talk to Effi Flo