Case study
Fee-only financial planning for specialist physiciansThe prospect enrichment engine behind Prolific New York
Prolific New York does fee-only financial planning for specialist physicians. We built the AI engine that finds its best prospects, enriches them from across the web and lands them, ready to work, in Lusha, Apollo and the team's CRM.
- Client
- Prolific New York
- Industry
- Financial planning for physicians
- Scope
- AI prospect enrichment engine
- Integrations
- Lusha, Apollo, ContactOut
- Delivery
- Idea to production in days
The challenge
Finding the right physicians is slow, manual work
Prolific New York's clients are specialist physicians: surgeons, cardiologists, pulmonologists and their peers at institutions such as Mount Sinai, Northwell Health and Maimonides. It is a narrow, high-value audience. Reaching it means knowing who these people are, where they practise and how to contact them before the first conversation ever happens.
That research lived in browser tabs and spreadsheets. Details were scattered across hospital directories, professional profiles and prospecting tools, and the sources rarely agreed with each other. Each prospect took real time to assemble and verify, records ended up duplicated across tools, and the team, like most operators, was rightly cautious about handing any of it to AI without knowing where the practical line was.
What we did
An enrichment engine, from idea to production in days
We scoped the work with Prolific around one question: what does a fully researched prospect look like, and how much of that can a machine assemble reliably? The answer became the engine. It takes Prolific's targeting criteria, finds physicians who match, and enriches each one from multiple public web sources into a single, consistent profile.
AI handles the messy middle: normalising names, titles and institutions, resolving duplicates, and scoring how well each prospect fits. The finished records flow straight into Lusha and Apollo for contact data and outreach, and into the CRM the team already works from. Nobody re-keys anything. We went from idea to a running engine in days and stayed with the project until it was in the team's hands.
Deliverables
What we built
Six pieces that turn a targeting brief into CRM-ready prospects.
- 01
Prospect discovery
Targeting rules for specialty, institution and location, so the engine looks for the physicians Prolific actually wants to talk to.
- 02
Multi-source web enrichment
Automated collection from hospital directories, professional profiles and other public web sources, merged into one profile per person.
- 03
AI normalisation and de-duplication
Names, titles and institutions cleaned into a consistent form, with the same physician recognised across sources instead of appearing three times.
- 04
Fit scoring
Each prospect ranked against Prolific's ideal-client profile, so the team works the best matches first.
- 05
Lusha and Apollo integration
Enriched prospects pushed into the prospecting tools the team uses for contact data and outreach, without manual uploads.
- 06
CRM sync
Clean, complete records land in the CRM the team already works from, so there is one list rather than several.
The engine
What makes the engine useful, not just clever
Scraping is easy. Turning scattered web data into a prospect list a team trusts is the hard part.
Looks for the right people
Starts from Prolific's definition of an ideal client, not from whatever a data tool happens to return.
Many sources, one profile
Reconciles what different websites say about the same physician into a single record with the gaps filled in.
Ranked by fit
Scores every prospect so the team's time goes to the strongest matches first.
Clean before it lands
Validated and de-duplicated before anything reaches Lusha, Apollo or the CRM, so the tools downstream stay tidy.
Results
What changed for the team
Prolific hasn't published figures from this work, and we don't invent them. What we can say is how the team's prospecting works now compared with before.
What changed
- Prospect research that was done by hand now runs automatically from multiple web sources.
- Every prospect arrives in Lusha, Apollo and the CRM enriched, de-duplicated and ready to contact.
- The team spends its time talking to physicians, not assembling spreadsheets.
- From idea to a working engine in days, with Agenthos staying on through delivery.
- A version 2 is planned on the same foundation.
“Like most operators, my team moved into AI very cautiously. We needed an expert who could show us the line between what is possible and what is practical. Their team did that and more: from idea to execution in a few days, and they stayed with the project to the end. We are looking forward to version 2.”
Reach us
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What to expect
Confirmation email
01You'll receive an immediate confirmation with next steps.
Response within 24 hours
02A team member will reach out within one business day.
Discovery call
0330-minute conversation about your goals and challenges.
No obligation
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